[1]Sylvain Fontaine 1]GEMASS, CNRS-Sorbonne Université, 59-61 rue Pouchet,
75017 Paris, France 2]CREST, CNRS-IPP, 5 avenue Henry Le Chatelier, 91120
Palaiseau, France

# Epistemic integration and social segregation of AI in neuroscience

<EMAIL_ADDRESS>Floriana Gargiulo  Michel Dubois  Paola Tubaro  [  [

###### Abstract

In recent years, Artificial Intelligence (AI) shows a spectacular ability of
insertion inside a variety of disciplines which use it for scientific
advancements and which sometimes improve it for their conceptual and
methodological needs. According to the transverse science framework originally
conceived by Shinn and Joerges, AI can be seen as an instrument which is
progressively acquiring an universal character through its diffusion across
science. In this paper we address empirically one aspect of this diffusion,
namely the penetration of AI into a specific field of research. Taking
neuroscience as a case study, we conduct a scientometric analysis of the
development of AI in this field We especially study the temporal egocentric
citation network around the articles included in this literature, their
represented journals and their authors linked together by a temporal
collaboration network. We find that AI is driving the constitution of a
particular disciplinary ecosystem in neuroscience which is distinct from other
subfields when regarding the references, and which is gathering atypical
scientific profiles who are coming from neuroscience or outside it. Moreover
we observe that this AI community in neuroscience is socially confined in a
specific zone of the neuroscience collaboration network, which is also keeping
to publish in a small set of dedicated journals that are mostly active in AI
research. According to these results, the diffusion of AI in a discipline such
as neuroscience didn’t really challenge its disciplinary orientations but
rather induced the constitution of a dedicated socio-cognitive workforce
inside this field.

###### keywords:

Science of science, Artificial Intelligence, neuroscience, research-technology

##  1 Introduction

In recent years, Artificial Intelligence (AI) is continuously spreading across
science. The associated worldwide scientific production and the amount of
dedicated funding programs for technological developments supported both by
academia and industry, are spectacularly growing [11, 3, 20], and the output
of such research are touching a variety of disciplines that are more and more
citing it [8, 2]. Developed and confined within STEM disciplines between the
late 1980’s and 2010, mainly represented by mathematics, statistics and
computer science, AI research is now opening to fields that are cognitively
and methodologically distant from these originating ones [13].

AI knowledge and tools are thus amenable to applications in such welcoming
disciplines which use them for scientific advancements, and which sometimes
adapt and improve them in turn for their conceptual and methodological needs.
Bianchini et al. (2022) studied especially such mutual improvement dynamics at
a macroscopic scale of science by focusing on the diffusion of neural
networks’ concepts and tools in numerous disciplines. Qualified as a general
method of invention by these authors because of its non-disciplinary status,
AI is thus expanding the adjacent possible of the purposes of the concerned
disciplines, and is therefore the source of innovation in the latter [18, 23].

In this paper we address the process of penetration of AI inside a given field
of research, which is not part of originating STEM disciplines while largely
applying AI. We focus on neuroscience, which are part of such fields according
to Gargiulo et al. (2022). We choose especially this field as it has a
particular history which is closely intertwined with the AI one. Indeed
neuroscience and AI are continuously providing feed-backs to each other
towards the comprehension of the human brain and the artificial reproduction
of some cognitive processes [16, 26]. This lead us to also assume that
neuroscience, under the assumption of an AI-receiving field of research,
contribute also to reshape AI knowledge, tools and practices, thus expanding
the adjacent possible of AI.

Along these lines, we focus on two dynamical mechanisms underlying the
appropriation of AI by neuroscience and then the possible knowledge transfer
between the two fields:

  * •

The cognitive embedding of AI into the multidisciplinary context of
neuroscience:

    1. 1.

what is the disciplinary landscape around neuroscience research which is using
AI?

    2. 2.

how such research fits in the disciplinary objectives of neuroscience?

  * •

The diffusion throughout the neuroscience community:

    1. 3.

who are the actors leading AI research in this field?

    2. 4.

do AI-related knowledge widespread throughout the whole scientific community?

In the following we answer these questions by analyzing a large corpus of
neuroscience publications published between 1970 and 2019, extracted with a
journal-oriented query from the Microsoft Academic Knowledge Graph (MAG) [9].
We distinguish AI-related publications from others with a dedicated keyword
filter applied to their titles and abstracts, as in [13]. After discussing in
Section 2 the intertwined history of AI and neuroscience, and also the main
concepts mobilized in this paper, we detail in Section 3 the building of some
relational structures and some metrics given by the corpus that will be
analyzed afterwards, namely its temporal egocentric citation network (composed
of bibliographical references and citations of each paper), its peer-reviewed
journals, and its temporal collaboration network, featured with the
disciplinary profiles of the scientists.

In Sections 4.1 and 4.2, we identify within the citation network the
disciplines that are shaping the AI research in neuroscience, and to what
extent they differs from those that are characterizing other neuroscience’s
subfields that are not concerned with AI. While the influences of the AI
research in neuroscience are progressively shifting since 1970 from biomedical
science to the STEM disciplines, and is building its own knowledge basis
inside neuroscience, it keeps to fit in the disciplinary objectives of the
latter. We also confirm this partial cognitive integration in Section 4.3,
which reveals the spreading of AI research in almost all the journals of the
database, but with different patterns of promotion of such research. In
particular, we show that the journals that are publishing a lot of AI-related
works are invested by researchers who contributed to AI research in general
(inside or outside neuroscience). By regarding the temporal co-authorship
network of the field, we show in the following Section 4.4 that the AI
community is polarized between two main academic profiles, a biomedical and
clinical one integrated inside neuroscience while being a few involved in AI,
and another much more involved in AI research but remaining close to STEM
disciplines. Putting all these scientists together, we then show that they are
more and more isolated in the collaboration network since 1970.

Finally, by considering AI as a research-technology spreading across science,
as originally conceived by Shinn and Joerges (2002) and formalized later by
Hentschel (2015), we finally discuss in Section 5 the partial diffusion
observed empirically in our results. With this framework we also discuss the
similarity and differences between the local development dynamics of AI within
this particular field of research and the global one within the whole science
system that is depicted in [13].

##  2 Literature review

###  2.1 AI and neuroscience: toward the mutual expansion of their adjacent
possible

Artificial intelligence (AI) commonly refers to both a research program and,
more generally, a set of complex computer-based programs which aim to mimic
human mind processes with high reckoning power. Although its foundations are
mainly associated with STEM disciplines, mainly mathematics, statistics and
computer science [13], the AI is constantly evolving alongside the
neuroscience by maintaining a virtual circle of mutual improvement [16].

Indeed neuroscience bring in a first place empirical confirmation of some
theoretical models that reproduce parts of mental processes, and that were
first imagined, analytically derived and computationally simulated by
(neuro)psychologists into the field of cognitive science [6, 19]. Most of
these models are at the roots of AI-related algorithms, notably the case of
the bio-inspired AIs such as artificial neural networks and their numerous
versions, which became biologically plausible with neuroscience. In
particular, the 1980’s and early 1990’s marked also the launching of the first
body-scanner machines applying positron emission tomography (PET) and
functional magnetic resonance imaging (fMRI), that both enabled important
discoveries on functional biological mechanisms in human brain that are
induced by complex cognitive tasks [6]. This contributed to the rise the
connectionist paradigm that is today dominant in brain sciences, even if some
debates are persisting into cognitive science about the representations of
knowledge and the logical operations to process them in interaction with the
real world [22, 1, 24]

In turn, in the recent big data era, a panel of AI tools enables today the
efficient process of various biomedical data that was acquired from important
clinical trials and cohorts for studying brain damages [14]. They are
especially part of the improvement of the diagnosis of various neuro-
degenerative diseases and of the attribution of potential dedicated
treatments, if they exist.

This virtual feedback loop, well documented in the neuroscience literature, is
thus inducing a reinforcement dynamics of both AI and neuroscience which are
receiving it. From this assertion we assume that AI and neuroscience are
expanding the adjacent possible of each other [18, 23], ie. one is extending
the field of possibilities that have yet to be explored of the other, by
blending with the pre-existing knowledge and practices of the latter. One is
thus reshaping the knowledge space of the other.

###  2.2 Conceiving AI as a research-technology in science

In a socio-historical perspective of science, Shinn and Joerges (2002) propose
the notion of research-technology, called also transverse science, to describe
the dynamics of science since the end of the $19^{th}$ century, now largely
based on instrumentation for experimental or empirical investigations,
especially in physical and life sciences. Within such a research regime, the
production of knowledge is conditioned by an instrument that is designed in a
specific research environment before being disseminated outside the latter
[29, 21]. Such an instrument requires the involvement of a dedicated socio-
cognitive workforce composed of a variety of actors (scientists, technicians,
promoters, administrators,…), that is developing a dedicated technological
culture of it, ie. a set of associated knowledge and practices shared by
everyone in the community, without necessarily claiming together a common
professional identity related to it. Regarding these criteria, they are
shaping a social group that could be also defined as an epistemic community
[15, 25], although the research-technology framework relaxes the notion of
socio-cognitive boundaries that are specific to such communities. According to
Shinn and Joerges (2002), the actors mentioned above are indeed able to move
between different research environments and are often coming across the
established disciplinary boundaries in order to provide their expertise for
the resolution of diverse scientific problems.

The research-technology associated with the instrument is thus a dynamical
entity with changing social and institutional organizations at different
moment of its development. Based on Shinn and Joerges (2002), Hentschel (2015)
proposes four stages to describe the life of a given research-technology in an
historical perspective, ie. throughout long time period: prehistory,
exploration, optimization and diffusion.

The first two stages are often associated together in the designing and
testing phases of a given instrument. According to these authors, the
underlying process needs temporarily a closure of the community working on it,
thus fostering the creation of a dedicated scientific field, often
interdisciplinary, in which the instrument is also a research object.

Shinn and Joerges (2002) also highlight the criterion of genericity of an
instrument: at the later stages of its development, it could be adapted for
disciplinary research contexts other than those in which it has been
originally designed for, or for a variety of applications outside scientific
research. The two last development steps of Hentschel are thus representing
such dynamics toward this final step. In particular, the diffusion phase
requires a relaxation of the disciplinary boundaries within which it has been
designed during the prehistoric and exploratory phase, hence an openness of
the actors in the community, which promote it in different fields of research
within academia or industry. This the case of technologies related to laser
beams or X-ray emission, now use widely from experimental physics to medical
research and standard practices, even in daily objects such as smartphones
[17].

According to the analysis of the AI research ecosystem provided by Gargiulo et
al. (2022), AI seems to embrace some characteristics of an instrument embedded
in a global research-technology program within science. Indeed its modern
forms was designed in a bounded disciplinary environment from the 1980’s until
the 2010’s, and it is now spreading in other disciplines. Through the analysis
of the development of AI in neuroscience, although within a bigger period
(1970-2019), we discuss in Section 5 to what extent this framework applies in
the case of the development and the diffusion of AI in a single field of
research.

##  3 Data and methods

###  3.1 Extraction and preprocessing of the data

MAG database is a big network of metadata, such as title, abstract,
publication date or publisher, that are associated to a huge set of various
scientific contributions published as journal articles, books or papers in
conference proceedings [9]. These metadata are embedded as strings or
numerical identifiers in a set of dump files linked together by link files,
all of them being available on the data deposit platform Zenodo [10].

To build an exhaustive database of the neuroscience literature from MAG, we
draw upon a list of journals that we extracted first from the databases Web of
Science (WOS) and SCImago Journal Rank (SJR), after removing duplicates
occurring in both of them. We use the SJR in addition of WOS because it
provides another measure of impact that is improving the visibility of some
journals that could have a lower impact factor in WOS. Moreover, because of
the different disciplinary classifications provided by the databases, SJR is
covering more neuroscience journals than WOS. For instance, in 2021, WOS
referenced only 281 journals labeled as “neuroscience” while SJR referenced
608 journals labeled as such. We consider here the neuroscience journals
referenced by WOS and other neuroscience journals referenced as such by SJR
but that are labeled differently by WOS – for instance, the well-recognized
journal Neurocomputing, while labeled with both “neuroscience” and “computer
science” in SJR, is labeled only as “computer science” by WOS. We thus get 421
journals. For sake of consistency, we lie only on the disciplinary
classification of WOS.

Here we focus only on peer-reviewed journals because of their easy
availability in WOS database, although we are conscious that AI research could
be largely published in other publication media such as preprint archives and
conference proceedings [30].

Then we extract the publications from MAG with an ISSN identifier matching
procedure based on this list of journals. These publication are brought
together in a set denoted by $\mathcal{P}$ in the following. In order to
distinguish AI-related works in our neuroscience dataset, we apply a selection
criterion such that they must include at least one AI-related keyword in their
title or abstract (see the SI of Gargiulo et al. (2022) for the complete list
of keywords). This subset is denoted $\mathcal{P}\cap AI$. All neuroscientific
publications outside this set is called $\mathcal{P}\cap\overline{AI}$.

In the following we keep only the papers that have been published in the
period running from 1970 until 2019, and that have at least 10 references and
at least 10 generated citations. Finally the sets
$\mathcal{P}\cap\overline{AI}$ and $\mathcal{P}\cap AI$ includes respectively
829,317 and 26,374 papers, spread over the period mentioned above according to
Fig.1. Therefore, among all these high-impact research in neuroscience, only
3% of them contain AI-related keywords. The inset of this figure exhibits
especially a slow growth of the share of AI-related publications in
neuroscience at the very end of the 1980’s, which is stabilizing around 1995
and followed by a very rapid growth from 2007 until 2019. This plateau around
1995 suggests a second, prolonged period AI winter in neuroscience,
characterized here by stable interest in AI research but not waning, unlike in
other disciplines or research fields [5, 27]. The following period of
important growth of this share, started in 2007, suggests also the main
influence of the rise of deep learning techniques, a well-known trend shared
by almost all the sciences [5, 13]. The share of AI publications in
neuroscience reaches only 10% of the number of publications at its highest
stage situated at the end of the studied period, which means that the AI-
related research remains rather marginal in neuroscience even today.

Figure 1: Left: Classification of the papers in the extracted corpus. The
cross-section between the blue and red zones is corresponding to the set
$\mathcal{P}\cap AI$ in the main text, while the blue zone excluded this
cross-section is representing the set $\mathcal{P}\cap\overline{AI}$. The
green set $\overline{\mathcal{P}}$ includes all papers added after the
building of the ego-centered citation network that are not published in
neuroscience journal. Right: Cumulative number $N_{c}$ of papers in the two
main subsets considered in this paper. The inset shows the instantaneous part
of AI-related publications in the whole neuroscience corpus.

###  3.2 Capturing the disciplinary landscape

####  3.2.1 Building of a citation ecosystem

After gathering the dataset under study, we build an exhaustive citation
ecosystem around neuroscience publications, as illustrated in Fig.2. We use
the general reference link file of MAG – which is actually a giant citation
network – for retrieving the direct references from and toward our identified
papers. We thus add papers that are published either in neuroscience
publishers, therefore in the set $\mathcal{P}$, or in others such that they
join the set denoted by $\overline{\mathcal{P}}$, ie. the green border zone in
Fig.1. With the journal classification of the WOS, we then enrich these sets
of references and citations by assigning them a set of Journal Subject
Categories (JSC) associated to their journals if they are referenced in this
database, which is a synonym of a large well-recognized discipline in science.
The papers with no associated JSC thus are automatically discarded.

Then, for each corpus $\mathcal{P}\cap AI$ and $\mathcal{P}\cap\overline{AI}$,
we count the annual number of citations obtained by each JSCs represented in
the global set of bibliographic references used by their papers. Doing this
year by year over the period 1970-2019, we therefore build a temporal ranking
of fields of study that are most cited by each corpus. For example, in Fig. 2,
the papers cited by one corpus at year $y_{0}$ (grey points) are mainly
published in journals labeled by the JSC $d_{1}$ with 8 citations, followed by
the JSCs $d_{2}$ with 5 citations and $d_{3}$ with only one (red points and
corresponding red-contoured ranking). Hence the disciplinary ranking
$r(y_{0})=\\{d_{1},d_{2},d_{3}\\}$ with each JSC sorted according to their
respective ranks mentioned above. However this ranking could be different
before this year $y_{0}$ and also in the following ones, depending on the
citations received by each JSC at these years.

We do the same for the JSCs that are most citing our papers of interest up to
one year after their official publication year. We choose this temporal period
to avoid the high time dispersion of the generated citations in the studied
temporal period, the citation impact by the papers published in 1970 being
eventually higher than those published in 2019.

In this way we assess from which JSCs are issued at first the generated
citations before eventually spreading into a broader disciplinary landscape.
Finally, by looking at both reference and impact side, we are representing two
disciplinary landscapes associated respectively with the corpora
$\mathcal{P}\cap\overline{AI}$ and $\mathcal{P}\cap AI$.

Figure 2: Citation ecosystem centered here around three papers (grey)
published at year $y_{0}$ and that are included either in $\mathcal{P}\cap AI$
or $\mathcal{P}\cap\overline{AI}$. One dashed arrow represents the citation of
a target paper by a source one. Hence red points are the papers that are cited
by the paper of our corpus (reference) while the blue ones are citing them
(impact). The rankings are shown in decreasing order with the associated
number of citations of each JSC $d_{i}$.

####  3.2.2 Representation of the disciplinary landscape

Following the procedure described in Section 3.2.1, and according to all the
couples of references’ and citations’ numbers associated with all the
represented JSCs in $\mathcal{P}\cap\overline{AI}$ and $\mathcal{P}\cap AI$,
we associate with each JSC $d$ two couples of ranks that is indicating its
weight either in references or in citations of the two corpora at year $t$,
denoted respectively as
$r^{d}_{R}(t)=\left(r^{d}_{R,\mathcal{P}\cap\overline{AI}}(t),r^{d}_{R,\mathcal{P}\cap
AI}(t)\right)$ and
$r^{d}_{I}(t)=\left(r^{d}_{I,\mathcal{P}\cap\overline{AI}}(t),r^{d}_{I,\mathcal{P}\cap
AI}(t)\right)$. In case of the a missing JSC in one of the two corpora at a
given year $t$, we fill the missing rank by a maximum value set at 100.

With one of such couple of ranks (either for references or citations), we can
locate one discipline $d$ in a 2D space of rankings, as shown in Fig. 3. In
this coordinate system, lower the value of one axis, better the associated
rank, and then higher the number of collected citations along this axis. The
angle $\theta$ indicates the deviation of $d$ from the diagonal in this space,
where the ranks are exactly the same in each corpus.

From this map we define the common interest area between $\mathcal{P}\cap AI$
and $\mathcal{P}\cap\overline{AI}$ (in grey) as the region close to the
diagonal where the two rankings of the fields of study inside it are almost
the same and not significantly varying over time. This zone is comprised
between the two lines of respective equations $r_{\mathcal{P}\cap
AI}=r_{\mathcal{P}\cap\overline{AI}}+\tau$ (above the diagonal) and
$r_{\mathcal{P}\cap AI}=r_{\mathcal{P}\cap\overline{AI}}-\tau$ (below the
diagonal), with $\tau$ a parameter set as 10 in this paper.

The blue zone over the common interest area is corresponding to the space
where the disciplines inside it have a better rank in
$\mathcal{P}\cap\overline{AI}$ than in $\mathcal{P}\cap AI$. In the case of
references (citations), such disciplines are thus more cited by (citing) the
former than the latter. The purple zone over the common interest area is thus
the opposite case.

Figure 3: Coordinate system to characterize the distribution of disciplines in
the references and citations of the corpora $\mathcal{P}\cap\overline{AI}$ and
$\mathcal{P}\cap AI$.

In Section. 4.2, we apply this per decade periods instead of single years, ie.
we build decennial ranking space from the total number of citations given or
received by the represented JSCs in $\mathcal{P}\cap\overline{AI}$ and
$\mathcal{P}\cap AI$ within the period under study.

###  3.3 Capture the use of AI within the neuroscientists

####  3.3.1 Classification of the authors according to their AI involvement

The dataset $\mathcal{P}$ alone would not be sufficient for a study of social
dynamics of scientists inside neuroscience. Indeed the latter would have had
published into disciplines or fields of research other than neuroscience or in
journals that are not labeled as such in the WOS database. We thus add another
level of granularity by extracting all the missing publications that have been
published by the authors who belong to $\mathcal{P}$. Notice that these
publications can have been published in the neuroscience journals extracted in
our list of 421 journals (see Section 3.1), since we focus only on the more
impacting authors who have published papers with at least 10 generated
citations (and having at least 10 references). From this extended set of
papers we deduce the duration of the scientific life of each author, and we
select first those who began after 1940. In addition, because of the poor
disambiguation of some authors in the MAG dump-files, whose are showing at
first glance a career spanning over one or more centuries and far too many
publications, we select only the authors having a reasonable duration academic
life, here up to 50 years.

With these added data, we also capture the real activity (before 2019) of each
author in AI research, not only in neuroscience but also in other fields of
research by attributing them an AI score. We define it as the share of AI-
related papers published before 2019, $f_{AI}(a)=n^{\mathcal{P}\cap
AI}_{a}/n^{tot}_{a}$. To prevent the unexpected effect of accumulation of
scientists having an AI score equal to 1 due to a single publication in their
very short career, we filter a second time the authors dataset by considering
only those who have published at least 3 papers. We thus consider in the
following only 886,074 scientists, in which 188,325 (only 16% of the
scientists) have published at least one AI-related paper in neuroscience.
According to the $f_{AI}$ distribution shown in Fig.4, we divide this set of
authors in four parts, namely $\overline{Q}$ ($f_{AI}=0$; $N_{a}=697749$),
$Q_{0}$ ($f_{AI}\in(0,0.5)$; $N_{a}=182925$), $Q_{1}$ ($f_{AI}\in[0.5,1)$;
$N_{a}=4977$) and $Q_{2}$ ($f_{AI}=1$; $N_{a}=423$).

Figure 4: Distribution of the AI score $f_{AI}$ of the authors.

####  3.3.2 Collaboration network

We then build a temporal co-signature network TCN from the whole set of papers
containing both $\mathcal{P}$ and $\overline{\mathcal{P}}$. Two authors $i$
and $j$ publishing together at year $t$ are linked by an edge weighted by the
number of common publications at this year $w_{ij}(t)$. All co-signatures in
all papers published in year $t$ shape a weighted snapshot called TCN($t$). In
order to unveil the structure of the collaboration at macro-scale in the whole
period 1970-2019, we build the weighted time-aggregated co-signature network
ACN, which includes the set $V$ of all authors appearing in the dataset, and a
set of weighted edges obtained by summing all the weights of the edges
appearing in each snapshot of TCN, $E=\\{\tilde{w}_{ij}\,\forall(i,j)\in
V^{2}|\tilde{w}_{ij}=\sum_{t}w_{ij}(t)\\}$. ACN is finally composed of 871,282
nodes and 7,420,423 weighted edges, and is divided into 17,599 independent
connected components, with a giant one including 93% of the nodes. The
smallest components exhibit also a non-negligible representation of authors
belonging to the quartiles $Q_{1}$ and $Q_{2}$, respectively 25,5% and 26,5%
of the population in these quartiles. Since the authors belonging to them are
supposed to drive the AI research in neuroscience, we choose to keep all the
communities for our analysis, even smaller than the giant component.

Here the collaboration network is built only among the most impacting
scientists of our dataset. Indeed we draw on one hand the collaboration
network directly from the set of neuroscience papers that are the most
impacting science in terms of citations, and on the other hand we have focused
on the authors who published at least 3 articles up to 2019. Therefore we
don’t take into account the short-terms collaborations among very early-career
scientists, small-career scientists or extra-academic ones that are involved
in one or two publications with too small impact but who can potentially drive
forward innovation.

####  3.3.3 Intensity of collaborations between $f_{AI}$ quartiles

In this section we detail the general computation of z-scores which will be
used in Section 4.4.2 to indicate the propensity of collaboration in the time-
aggregated collaboration network ACN.

We first consider a static collaboration network $G={S,E}$ with $E$ the set of
scientists and $E$ the set of edges between them. Each scientist in $S$ is
identified by their respective AI score $f_{AI}$. According to Section 3.3.1,
the distribution is divided in four parts
$\\{\overline{Q},Q_{0},Q_{1},Q_{2}\\}$, from the less AI-expert to the most
one. Therefore one author is belonging to one given group $Q$ given its own AI
score. By aggregating the scientists into such $Q$ groups, we compute the
$4\times 4$ matrix $\tau_{obs}$ of effective edges between these groups, where
one element is written as $\tau_{obs}(Q_{i},Q_{j})=|\\{(u,v)|u\in Q_{i},v\in
Q_{j}\\}|$.

Then we create a set of $N$ alternative collaboration networks
$\\{\tilde{G}_{k}\\}=\\{(S,\tilde{E}_{k}\\}$, with $k=\\{1,...,N\\}$, which
are all based on the same set of scientists but have different set of edges
obtained by perturbing the network with a uniform shuffling which conserves
the degree distribution of the scientists. By applying the same clustering of
scientists in $Q$ groups for each network as for the real network $G$, we draw
a set of matrices $\\{\tau_{k}\\}$ corresponding to the share of edges between
the $Q$ groups within the randomized networks $\\{\tilde{G}_{k}\\}$. From this
set we extract on one hand the average matrix $\tau_{sim}$ where one element
is defined as
$\tau_{sim}(Q_{i},Q_{j})=\frac{1}{N}\sum_{k}\tau_{k}(Q_{i},Q_{j})$, and on the
other hand the standard deviation matrix $\sigma$ where one element is defined
as
$\sigma(Q_{i},Q_{j})=\frac{1}{N-1}\sum_{k}(\tau_{k}(Q_{i},Q_{j})-\tau_{sim}(Q_{i},Q_{j}))^{2}$.

We finally compare the empirical matrix $\tau_{obs}$ of the real network with
the simulated one $\tau_{sim}$ through a $z$-score matrix where one element is
defined as follow:

$z(Q_{i},Q_{j})=\frac{\tau_{obs}(Q_{i},Q_{j})-\tau_{sim}(Q_{i},Q_{j})}{\sigma(Q_{i},Q_{j})}\,.$
(1)

This standardization is used here to test the over-representation or under-
representation of a given number of edges between two groups with respect to
the corresponding average simulated value which is representing an ideal
situation through randomization. Applying the whole procedure before on the
network ACN built in Section 3.3.2, with $N=100$, gives the Fig. 13B. Fig. 13A
is also drawn by applying the same method with same $N$ on each temporal
snapshot of the temporal collaboration network TCN (see also Section 3.3.2),
and by aggregating the groups $Q_{0}$, $Q_{1}$ and $Q_{2}$ into a single one
called $Q_{i}$ (see Fig. 13A) that includes all the scientists with at least
one AI-related publication. Since TCN is undirected, the $4\times 4$ matrices
$\tau_{obs}$, $\tau_{sim}$, $\sigma$ and $z$ are therefore reduced to scalars
defined with only the two groups $\overline{Q}$ and $Q_{i}$.

##  4 Results

###  4.1 Alignment of the neuroscience AI specialty with general neuroscience

First of all we investigate the embedding of AI technologies and knowledge
within neuroscience through the dynamical interaction between the disciplinary
environment of the two fields respectively.

After having divided the neuroscientific papers in two sub-corpora, one
including the AI-related papers ($\mathcal{P}\cap AI$) and another including
non-AI ones ($\mathcal{P}\cap\overline{AI}$), we build for each of them two
disciplinary rankings which could vary over time, one for the disciplines
appearing in their respective references ($r^{R}(t)$) and another for those
appearing in their respective citations ($r^{I}(t)$). The reference ranking
associated with one given corpus summarizes its main influential fields of
study, thus its disciplinary structure on which it draws upon over time, while
the citation ranking indicates the fields that are impacted at first by the
corpus. Section 3.2.1 details the building of such temporal disciplinary
rankings more precisely.

We then compare macroscopically at each year the reference rankings of the two
corpora ($r^{R}_{\mathcal{P}\cap AI}(t)$ and
$r^{R}_{\mathcal{P}\cap\overline{AI}}(t)$) and also their citation rankings
($r^{I}_{\mathcal{P}\cap AI}(t)$ and
$r^{I}_{\mathcal{P}\cap\overline{AI}}(t)$) by using a common similarity
metrics $J$ provided by Gargiulo et al. (2016, see their supplementary
materials). This last measure, comprised between 0 and 1, evaluates how much
similar are two given rankings $r_{A}$ and $r_{B}$: if $J(r_{A},r_{B})=1$, the
two rankings are exactly the same, ie. containing the same elements with the
same respective ranks; conversely, if $J(r_{A},r_{B})=0$, the elements
included in $r_{A}$ are not in $r_{B}$, whatever their respective ranks.

Fig. 5 shows the evolution of two such similarity measures, a first one for
comparing the rankings of disciplines cited by the corpora $\mathcal{P}\cap
AI$ and $\mathcal{P}\cap\overline{AI}$ (blue curve) and a second one for
comparing the rankings of the disciplines that are citing them over time (red
curve).

Figure 5: Instantaneous similarity between the references’ or impact’s
rankings of the two corpora $\mathcal{P}\cap AI$ and
$\mathcal{P}\cap\overline{AI}$ at year $t$.

Fig.5 shows that the AI-related literature in neuroscience is impacting
directly almost the same disciplines at the same level of interest shown by
the non-AI corpus for all the studied time period. AI research in neuroscience
is thus always being beneficial for all the same disciplines that are
structuring the last field of research since the 1970’s. However the
references remain quite different between both sets, but the almost linear
growing of the similarity of reference is a global sign of bibliographic
homogenisation as well between the two corpora. AI is thus progressively
penetrating the cognitive structure of neuroscience according to this
evolution.

###  4.2 Neuroscience and AI: two different disciplinary landscapes

In the following we compare how the individual disciplines appearing in the
references and/or in the citations contribute to shape the respective
disciplinary landscape of the corpora $\mathcal{P}\cap AI$ and
$\mathcal{P}\cap\overline{AI}$.

By applying the framework described in Section. 3.2, we associate with each
represented discipline $d$ in the references or citations of the corpora
$\mathcal{P}\cap\overline{AI}$ and $\mathcal{P}\cap AI$, their time-aggregated
ranks over decades,
$\left(\tilde{r}^{d}_{\mathcal{P}\cap\overline{AI}}(T),\tilde{r}^{d}_{\mathcal{P}\cap
AI}(T)\right)$, with $T=\left[t_{0},t_{0}+10\right)$ a given time period where
$t_{0}\in\\{1970,1980,1990,2000,2010\\}$. These ranks are built with the total
numbers of citations received by $d$ (references) or given by $d$ (citations,
impact) during the time period $T$ in the two corpora.

We represent them in the 2D space of rankings associated with each corpus
respectively, as shown in Fig. 6. In one of the maps drawn on this figure, one
colored disc represents a specific discipline $d$ that is located with its
respective ranks in each corpus during the considered time period, in
references (Fig. 6A) or citations (Fig. 6B). Lower the value of one axis,
better the associated rank, and then higher the number of collected citations
along this axis. From these maps we define the common interest area between
$\mathcal{P}\cap AI$ and $\mathcal{P}\cap\overline{AI}$ as the region close to
the diagonal where the two rankings of the fields of study are almost the same
and not significantly varying over time. These fields are colored in grey in
these maps. The fields that are more dispersed around the diagonal are
represented with two different colors, the blue ones having a better rank in
$\mathcal{P}\cap\overline{AI}$ than in $\mathcal{P}\cap AI$ and the purple
ones having a better rank in $\mathcal{P}\cap AI$ than in
$\mathcal{P}\cap\overline{AI}$. All of them are abbreviated for sake of
clarity. A correspondence table with their complete name in WOS are given in
App. C.

Figure 6: Time-aggregated ranking maps of the fields of study involved in
publications cited by both studied corpora (A) and in publications that are
citing them (B). The dashed lines show the diagonal where the rankings are
exactly the same in the corpora $\mathcal{P}\cap AI$ and
$\mathcal{P}\cap\overline{AI}$. Only are shown the most significant
disciplines in both corpora, with the condition that they appear in both
corpora. The sizes of the discs, based on their empirical number of citations
in the two different corpus, are normalized to make comparable their
respective citation weights within each corpus. Grey points are corresponding
to disciplines remaining confined in the area between the two lines of
respective equations $r_{\mathcal{P}\cap
AI}=r_{\mathcal{P}\cap\overline{AI}}+\tau$ (above the diagonal) and
$r_{\mathcal{P}\cap AI}=r_{\mathcal{P}\cap\overline{AI}}-\tau$ (below the
diagonal), with $\tau=10$, and with no important variations of positions from
one period to one another. The most persistent ones over decades, with a rank
lower than 20, are mentioned in the upper left box of each figure A and B.
Finally, blue points are the most preferred disciplines of the
$\mathcal{P}\cap\overline{AI}$ corpus while purple ones are those for the
corpus $\mathcal{P}\cap AI$. Abbreviations of the apparent disciplines are
given in App. C.

To characterize each of these three area around the diagonal in the references
(citations) of each corpus, we use the annual couples of ranks $r^{d}_{R}(t)$
($r^{d}_{I}(t)$) of each represented discipline $d$ in
$\mathcal{P}\cap\overline{AI}$ and $\mathcal{P}\cap AI$ and compute the
temporal angle $\theta(t)$ that indicates its temporal deviation from the
diagonal, here corresponding to $\theta=0$. Then we compute inside each zone
and at each year the average angle $\langle\theta\rangle$ and the standard
deviation from the latter, both represented in Fig. 7. The impact plot shows
only the existence of the grey zone until the 1980’s, which means the two
corpora was cited by common disciplines in this period, without characteristic
disciplines that are shaping further the blue and purple curves.

Figure 7: Temporal average angles produced by the disciplines in each area of
reference’s (left) and impact’s (right) diagrams of Fig. 6, with respect to
the diagonals represented here by a dashed line at $\langle\theta\rangle=0$.
The colored area covering the curves are representing their respective
standard deviation from the mean angle. The angles are expressed in radians.

The disciplinary composition of the common interest area, concentrated around
the diagonals of all the maps, includes rather the same fields at each decade
both for reference and impact side. According to the most persistent ones over
decades inside the references and generated citations of both corpora (see
upper left boxes in Figs. 6A and 6B), the core of the observed citation
dynamics lies in neuroscience and is composed by disciplines that are mainly
associated with medicine and biomedical research, such that Biochemistry &
Molecular Biology, Behavioral Sciences, Clinical Neurology, Physiology, Cell
Biology, Psychology, Psychiatry and Ophthalmology. This zone is thus
coinciding with the definition of neuroscience given by neuroscientists
themselves, namely it “include[s] all fields that are involved with the study
of the brain, the behaviors that it generates, and the mechanisms by which it
does so, including cognitive neuroscience, systems neuroscience and
psychology” ([16], p. 245). This zone is also accompanied by disciplines whose
ranks are more variable and that are associated to more technological aspects
of neuroscience, such as Computer Science, Engineering, Radiology,
Neuroimaging and Audiology & Speech-Language pathology. A description of the
evolution of the disciplinary landscape inside this zone is detailed in App.
B.

The special disciplinary ecosystem of the AI-corpus, in blue in the rankings
maps of Fig.6, is also centered around biomedical fields of study that tend to
be close to the disciplines that characterize the whole neuroscience as
mentioned above. In particular, as shown in shown in Fig.7, the mean angles
and the standard deviations of the disciplines in the associated zone are
asserting a global concentration of them towards the common interest area,
which includes on one hand the most influential fields of research (reference)
and on the other hand the core of those that show the same interest for the
two studied corpora (impact). We also notice in the reference’s angle plot of
Fig. 7 a deviation from the diagonal of the disciplines in this zone of
references since 2010, that is also shared by the disciplines in the common
interest area of references. This suggests a recent shift of references shared
by the two corpora toward fields of study preferred by the non-AI corpus
$\mathcal{P}\cap\overline{AI}$.

Conversely, the special disciplinary ecosystem of $\mathcal{P}\cap AI$ evolves
differently by representing the mathematical, computational and technological
part of neuroscience since 1970. The regular references to Computer Science,
Physics, Statistics & Probability, Mathematical & Computational Biology and
Engineering show a large influence of technological-oriented research in this
particular AI research in neuroscience. Progressively concentrated towards the
common interest area between 1970 and the late 1980 (see Fig. 7), these
references preferred by $\mathcal{P}\cap AI$ become more further away from the
common interest area and more dispersed after this period, thus indicating a
cognitive differentiation of references on which the AI-related corpus
$\mathcal{P}\cap AI$ is drawing upon from the non-AI corpus
$\mathcal{P}\cap\overline{AI}$. In addition, while neuroscience and associated
medical fields – as Clinical Neurology and Neuroimaging – remain the primary
stakeholders in the AI research conducted within it, the latter appears to be
of varied interest to a subset of disciplines since 1980’ that do not place as
much emphasis on works in the non-AI corpus, such as Mathematics, Physics,
Computer Science and also Automation & Control Systems (see Fig.6B). According
to Fig.7, these citing disciplines are also closer to the common interest area
than in references, thus confirming the involvement of AI research into the
general scientific purposes of neuroscience.

By regarding the disciplines with the most significant evolution within the AI
ecosystem, Fig.8 shows the rise of Neuroimaging and Radiology both in
references and impact sides, as already observed for the common interest area,
and also the progressive domination of Computer Science. This figure shows
also the decrease of the influence of fields of research linked to the
disciplinary lines of the neuroscience and of the corpus
$\mathcal{P}\cap\overline{AI}$. This is also a sign of a progressive
differentiation of the research supported by the AI research from the rest of
neuroscience. The case of physiology, which contributed to the foundations of
neuroscience [6], is particularly eloquent with its distancing in time from
the highest positions in the ranking since the 2000’s. This observed retreat
of master neuroscience disciplines, that had a strong influence in the
building of AI’s in this field since the 1970’s, suggests that a social
transformation occurred in this special research during the studied temporal
period. Perconti and Plebe (2020) mention such a transformation, in which AI
in neuroscience was a matter for biomedical specialists before becoming an
object of study and technological developments for engineers. This will be
shown empirically in the following sections.

Figure 8: Time evolution of the ranking $r_{\mathcal{P}\cap AI}$ of the
disciplines that are mostly represented by the corpus $\mathcal{P}\cap AI$ in
the references (top) and impact sides (bottom), ie. situated in the lower zone
of the maps shown in Fig.6. Only the most significant are shown with colors.

###  4.3 An AI literature confined in a small set of journals

Another factor of differentiation of the AI research community from the core
of neuroscience is the set of journals in which the first are mainly
published. Starting on the set of 421 journals included in the whole
neuroscience dataset, we compute the temporal share $a_{AI}(t)$ of AI-related
publications in each of them (called also temporal AI activity), and its
global share $a_{AI}$ of such publications since its launch year (called also
justAI activity). Here we consider the whole publication history of each
journal for computing these scores, since we omit some papers published in
them with the filter based on a minimum number of references and impact (see
Section. 3.1). These scores are represented in Fig. 9. We also extract the
journals with $a_{AI}$ higher than 10%, whose names and launch years are given
in the table in the bottom of this figure.

According to the evolution of the AI activities of neuroscience journals, the
AI-related research in this field is concentrated around a small subset of
journals providing development in computational techniques mainly linked to
neural networks and cognition. These journals are quite representative of the
connectionist wave of AI which seems to be active in neuroscience since the
late 1980’s [5]. They are representing 32,7% of the scientific production of
AI in neuroscience during the whole period 1970-2019, according to our
dataset, the rest being distributed among the 398 other journals of the
dataset with a much smaller AI-activity (less than 10%).

The spectrum of $a_{AI}$ (top yellow curve on Fig. 9) shows that the launches
of the most AI active journals in the neuroscience field are concentrated
around three periods. The first one spans the period 1987-1994 with especially
7 journals (no. 3 to 9 in the table of Fig. 9) whose $a_{AI}$ is higher than
28% and whose scopes are oriented toward computational neuroscience and the
use of neural network formalism for complex calculations. These journals,
except no. 7 and 9 (respectively Network: Computation in Neural Systems and
Neural Network World) are showing well-sustained activity on AI research until
2019. This period falls commonly into the second “AI winter”, when AI-research
funding and the production of scientific results and associated technological
solutions were at their lowest for a second time [5, 27]. Paradoxically,
neuroscience are especially active in such research in this period, as shown
by the strong, long-lasting AI-activity of these journals created in this
period.

The second period includes the journals 10, 11 and 12 that have been launched
between 1999 and 2001. The journal IEEE Transactions on Neural Systems and
Rehabilitation Engineering is especially oriented toward the development of
computational methods and technological tools to capture the neural activity
of the brain. This period is followed by another until 2007 which did not lead
to the launch of AI-active journals.

Finally the third period following the latter, spanning the period 2007-2009,
is composed of 7 journals (no. 14 to 20) that are fostering research at the
crossroads of neuroscience and cognitive science. It includes especially the
most AI active journal of our dataset, Evolutionary Intelligence, whose scope
is oriented toward evolutionary computation, which is a subset of the field of
optimization. Its temporal activity is also strongly sustained until 2019.

|  Journal |  Launch year in the database | $a_{AI}$
---|---|---|---
1 |  Cognitive Science |  1976 | 0.116029
2 |  Journal of Sensory Studies |  1986 | 0.104065
3 |  Neural Networks |  1987 | 0.530183
4 |  Neural Processing Letters |  1988 | 0.694971
5 |  Neurocomputing |  1989 | 0.559622
6 |  Neural Computation |  1989 | 0.370334
7 |  Network: Computation in Neural Systems |  1990 | 0.281250
8 |  Adaptive Behavior |  1992 | 0.313070
9 |  Neural Network World |  1994 | 0.392226
10 |  Cognitive Systems Research |  1999 | 0.303797
11 |  IEEE Transactions on Neural Systems and Rehabilitation Engineering |  2001 | 0.224457
12 |  Neuroinformatics |  2001 | 0.184906
13 |  Plos Computational Biology |  2005 | 0.122665
14 |  Frontiers in Neuroinformatics |  2007 | 0.185841
15 |  Computational Intelligence and Neuroscience |  2007 | 0.547596
16 |  Cognitive Neurodynamics |  2007 | 0.242938
17 |  Frontiers in Computational Neuroscience |  2007 | 0.226304
18 |  Evolutionary Intelligence |  2008 | 0.695946
19 |  Cognitive Computation |  2009 | 0.513138
20 |  Topics in Cognitive Science |  2009 | 0.100173
21 |  Journal of Mathematical Neuroscience |  2011 | 0.126984
22 |  NeuroImage: Clinical |  2012 | 0.111653
23 |  Biological Psychiatry: Cognitive Neuroscience and Neuroimaging |  2016 | 0.112500

Figure 9: Top diagrams: A: Temporal AI activity of the journals. One square at
the position $(j,t)$ of the diagram is the share of AI publications in the
journal $j$ at time $t$. One column of the plot is the AI activity of one
journal. B: Distribution of the global AI activity of the journals over all
their publications up to 2019. The horizontal axis is corresponding to the
journals axis of the bottom plot A, as shown by the red dashed zone. C: Zoomed
activities of the 23 most active journals in AI research, with a global AI-
activity higher than 10%. They are indicated in the bottom table.

Aggregating the date over time, Fig.10 shows a linear correlation between the
mean AI-activity in the journals and the average of the AI scores $f_{AI}$ of
the authors that have published at least one paper in these journals. The tail
at highest $a_{AI}$ (higher than 0,1) is corresponding to the top 15 of the
most AI-active journals given in Fig. 9. This particular result thus unveils
an attraction of the authors publishing the most AI-related works of our
dataset in the journals with a high AI-activity in time. This suggests that AI
research in neuroscience is done by a specialized scientific community inside
this field with its own journals for communicating results.

Figure 10: Correlation plot between the global AI activity $a_{AI}$ of the
journals appearing in $\mathcal{P}$ and the average AI scores
$\overline{f_{AI}}$ of the authors that have published in it between 1970 and
2019. Each point represents one journal in the horizontal $a_{AI}$ space,
colored by the standard deviation of the $f_{AI}$ scores of the authors who
have published in it. The maximum average value of $\overline{f_{AI}}$ is
situated around 0,4.

###  4.4 Who are the scientists making AI in neuroscience?

####  4.4.1 Disciplinary profiles in each quartile of the $f_{AI}$
distribution

In this part we focus on the profiles of the scientists doing AI in
neuroscience (in Fig.4, included in one of the quartiles $Q_{0}$, $Q_{1}$ or
$Q_{2}$) and how they are inserted into the global authorship landscape of
neuroscience. In particular, we compare these lasts with the scientists in
neuroscience who did never publish AI-related papers (included in the quartile
$\overline{Q}$) under two aspects, namely (1) their disciplinary background
and (2) their disciplinary trajectory in academia.

We define the “disciplinary background” of one author the combination of
disciplines corresponding to the papers he/she has published in his/her first
year of academic life. Then we compute for each quartile the temporal
cumulative number of new scientists formed in each represented disciplinary
background in this quartile, as shown in Fig.11. In this way we assess for
each quartile the main native specialties in which our authors have first
published.

Fig.11 shows that the profiles included in $Q_{0}$ and $\overline{Q}$ overall
the period 1940-2019 are very similar. They are mainly confined in biomedical
research around neuroscience, as well as in Multidisciplinary Sciences which
is represented by 91 international journals with a broad topical diversity. We
recognize also the main fields of research that are shaping the common
interest area of the disciplinary ecosystems of the two separated AI and non-
AI corpora, that are shown in the grey rows of the tables in Fig.6. These two
profiles are thus representing at most the disciplinary spectrum of
neuroscience itself. We notice in addition the spectacular increase of the
Neuroscience curve in the two plots until the 1970’s (after having emerged in
1957 for $\overline{Q}$ and in 1962 for $Q_{0}$), that is followed by a quasi-
linear progression until 2015. This boom of neuroscience profiles in these
quartiles let us suppose that the modern neuroscience is progressively
inserting institutionally as a well-structured discipline in science.

The profiles of $Q_{1}$ and $Q_{2}$ shown in Fig.11 are at the opposite of the
previous ones, coming at most from fields of research related to Computer
Science. The specialty Computer Science, Neuroscience, which emerged in 1988
for $Q_{1}$ and in 1991 for $Q_{2}$ indicates the rising of a group of
scientists that are specialized into computation in neuroscience. Notice that
the behavior of the Neurosciences curve within $Q_{1}$ Fig.11 appears later
than computational and engineering profiles, suggesting also that AI-related
knowledge and technological tools penetrate progressively the global
neuroscience field.

Figure 11: Cumulative number of new authors per disciplinary background for
each quartile. For one plot, the point of the curve of the specialty $d$ at
year $t$ is the number of authors who have published their first articles
inside $d$ during their very first year of academic career, since the year of
apparition of $d$. Only the top 10 native specialties in 2019 are shown in
$\overline{Q}$, $Q_{0}$ and $Q_{1}$, and the top 6 for $Q_{2}$ because of the
insignificance of the following ones.

From the subset of authors belonging to the most frequent disciplinary
background in each quartile, we consider their respective disciplinary profile
as the disciplines corresponding to the journals in which they publish
throughout their scientific life – that would not be ended for the youngest
still publishing in 2019. We therefore draw from these two parameters –
disciplinary background and career-related disciplines – the typical
disciplinary trajectories in each quartile, which are shown in Fig.12. Since
the backgrounds are built from the publications at a given year, the authors
who began their career in 2019 would have a disciplinary profile corresponding
to it. For avoiding an over-representation of some confined disciplinary
trajectories due to these newcomers, we therefore consider only the authors
who began at most in 2018. For the sake of clarity we select only the most
significant trajectories.

Figure 12: Significant disciplinary trajectories of the authors confined in
each quartile. On each plot lie on the left the most significant disciplinary
backgrounds, and on the right lie the career-related JSCs up to 2019.

As a confirmation of the disciplinary background shown in Fig.11, Fig.12 shows
that the studied authors in $\overline{Q}$ and $Q_{0}$ are involved in a
similar disciplinary landscape centered around Ophthalmology, Clinical
Neurology and general Neurosciences, while $Q_{1}$ and $Q_{2}$ are more
confined into Computer Science and Engineering. In particular, by considering
again neuroscience as the subset of disciplines including Neurosciences,
Clinical Neurology and Neuroimaging, we observe that $\overline{Q}$ and
$Q_{0}$ are more involved in that field of research, with respectively 77% of
the authors in the first and 78% of those in the second who are showing a
disciplinary profile that includes one or more of the fields of research
associated with neuroscience. On the contrary, 45% of the authors in $Q_{1}$
and 42% of the authors in $Q_{2}$ have a disciplinary profile that includes
such JSCs. These two quartiles are therefore mainly detached of the
neuroscience goals given the disciplinary backgrounds and profiles of their
main respective authors. These results show at first glance that neuroscience
is a field that bring together heterogeneous profiles who seem to serve
different epistemic objectives inside and outside neuroscience [28].

Nonetheless we notice two special things about these last quartiles. First the
authors who began into the specialties including one or more neuroscience-
related disciplines tends to continue in the same field of research, which is
sometimes interdisciplinary such that Computer Science, Neurosciences and
Neuroimaging, Neurosciences, Radiology. This means that these scientists are
formed into a disciplinary context centered around neuroscience. Second, some
authors who began into Computer Science, Engineering or mathematics-related
disciplines – who seem to be not concerned about neuroscience in their
disciplinary backgrounds – continue into neuroscience, as shown by the
combination of their originating JSCs with neuroscience ones in their
disciplinary profiles in Fig.12, for instance Computer Science, Engineering
leading to Computer Science, Engineering, Neurosciences. The late emergence of
interdisciplinary profiles in computer science and neuroscience, who are more
involved in neuroscience in general such as $Q_{2}$, also testify that AI
community is taking root in the global neuroscience landscape, the more recent
profiles in neuroscience becoming insiders in this new technological
specialty. All these scientists thus represents a special labor force for
neuroscience whose main expertise lies in AI, and more generally in
mathematical, computational and technological tools [24].

####  4.4.2 Structure of the collaboration network between quartiles

Now we investigate how the AI practitioners are distributed among the
neuroscience community, and how are shaped the collaborations between them,
especially between the different kind of scientific profiles described before.

We first consider the temporal collaboration network TCN built in Section
3.3.2, and we evaluate the temporal standardized share of edges between
scientists belonging respectively to $\overline{Q}$ and to all other quartiles
$Q_{i}$’s (see Section 3.3.3 for the calculation of such score). This score is
showed in Fig.13A, from which we deduce two facts.

First, all its values are lower than 0 over time, meaning that the links
between the scientists in the two studied ensembles of authors in the period
1970-2019 are under-represented compared to situations obtained by randomized
reshuffles of the edges between them. Second this situation becomes worse over
time, with a continuous reduction under 0 until the 1990’s followed by a quick
one until 2015. Although this tendency is reversed since 2015, these results
indicates that the neuroscientists making AI, ie. in $Q_{i}$’s, are shaping an
almost independent community inside neuroscience by widening progressively a
gap with other neuroscientists belonging to the ensemble $\overline{Q}$.
According to the profiles shown in Fig.11, since the scientists who published
AI-related research until the 1990’s are mostly coming from $Q_{0}$, and since
they are representing the whole neuroscience field, the result advanced above
let us suppose that these scientists in $Q_{0}$ are outsiders in the history
of AI in neuroscience, by analogy with the notion first advanced by Crane
(1969) in the sociology of science.

This social divide is confirmed with Fig.13B, which represents the panorama of
the links shared between the different quartiles in ACN (see also Section
3.3.3). This diagram represents a stabilized situation observed since the end
of the 1990’s (see Fig.16 in App.D), where the $Q_{i}$’s cosign more together
than with $\overline{Q}$ while the scientists in the second ensemble prefer to
collaborate together as well. However, in regards with the temporal similarity
index of impacted JSCs shown in Fig.5, as well as the temporal evolution of
the disciplinary landscapes shown in Fig.6, the $Q_{i}$’s export their
knowledge mainly in the whole neuroscience, even those cited by articles
written by scientists belonging to $\overline{Q}$. We thus assist to a social
separation in neuroscience around AI research, that does not produce a strict
knowledge divide in neuroscience though.

We also observe in Fig.13B a polarization within the subset of AI
practitioners in neuroscience. Indeed the authors belonging to $Q_{1}$ and
$Q_{2}$ are more strongly connected together than with those in $Q_{0}$. This
can be explained by the disciplinary proximity of the authors in $Q_{1}$ and
$Q_{2}$ observed at first glance in Fig.11, especially the prominence between
the late 1980’s and the early 2000’s of full computer scientists and hybrid
profile publishing in journals labeled as Computer Science, Neurosciences.
Furthermore, the links between $Q_{1}$ and $Q_{0}$ are much more important
than those between $Q_{2}$ and $Q_{0}$. The scientists in $Q_{1}$ thus appears
to be the most interdisciplinary by assuring the bridge between these
differentiated profiles inside AI community. These particular outsiders
described before are driving the diffusion of AI in neuroscience from
computation to medical and clinical applications, given their disciplinary
trajectories shown in Fig.12.

---
Figure 13: A: Temporal $z$-score applied to the edges in TCN between the
scientists in $\overline{Q}$ and those in the other $Q_{i}$’s, here aggregated
together under the notation $Q_{i}$. B: $Z$-score matrix of the edges between
each quartile in the time-aggregated collaboration network ACN. The values are
normalized with the absolute maximum one in the matrix.

##  5 Discussion

In this article we have explored the penetration of AI in neuroscience through
the co-development of both fields and especially the construction of a
technological specialty centered around AI inside neuroscience. To do this we
conducted a scientometric analysis of an exhaustive bibliometric database that
aims to represent at best the neuroscience research between 1970 and 2019.
This analysis lies on many indicators.

First, with a comparative analysis of the egocentric citation networks
associated with respectively AI research and non-AI one, we have shown a
progressive cognitive differentiation of the first from the second, which is
based on the employed bibliographic references in the two fields respectively.
We observed indeed a specialization of AI research toward computer science,
mathematics and engineering, while the core of neuroscience research draws
upon biomedical and clinical research fields related to it. This
differentiation is also shown by the neuroscience journals landscape, in which
a small set of 23 journals, the most active in AI research in neuroscience,
are representing those STEM fields in references and are gathering the
scientists who are the most active in AI in the neuroscience field as well.
However, we showed that the demarcation of AI research that is observed in
bibliographical references remains partial because its citational impact is
more broadly distributed across the entirety of the field of neuroscience. The
impact of AI-related research provided by neuroscience is thus not confined
into a subset of research which could be the same as the one visible in its
employed references – even if AI research in neuroscience exhibits a
particular accent for engineering and medical imaging technologies in its
references and target fields of research since around the 1990’s, just after a
period marked by a decline in AI research activities called “second AI
winter”.

In a second step we explained this partial specialization by conducting a
relational approach based on the scientific profiles of the authors of the
studied database. By distinguishing the AI practitioners in neuroscience from
the neuroscientists, we have shown that the first tend to not maintain links
with the second in the temporal co-authorship network which includes the main
collaborations since 1970. With the previous results we concluded that AI
researchers are “outsiders” in the field of neuroscience. We especially have
distinguished two classes in this group of scientists, namely a first wave of
outsiders including authors who are trained in the main disciplines that are
shaping the neuroscience field since the 1940’s and who have a low AI
activity, and a smallest second wave which emerged around the 1980’s and that
is including authors who are trained in other disciplines that are not
represented in the former group, such as computer science and engineering, and
who are exhibiting the highest activities in AI research in general (inside
and outside neuroscience). Furthermore this second group is not the most
involved in the field of neuroscience, its members keeping to publish within
their original disciplines that are mainly STEM ones.

This social polarization inside the AI practitioners in neuroscience suggests
that AI becomes over the years a set of technologies that need to be shaped
not only by neuroscientists themselves, but also with the help of scientists
who come from outside neuroscience, or from within but with an
interdisciplinary background, and who present specific expertise about AI
itself. Since we considered AI as a global research-technology in science [29,
21, 17], these results thus are a consequence of the diffusion of AI outside
its originating STEM disciplines and throughout the science system [13]. The
second wave of AI researchers described before is indeed representing the
mobility of experts towards other fields of research in order to propose and
integrate the associated knowledge and technologies to achieve some of their
disciplinary objectives, or, less ambitiously, solve some technical problems
that could not be solved within the intrinsic epistemic framework of the
welcoming discipline.

These results thus illustrate quite well the generic character of AI when
applied in neuroscience, which is producing a social and cognitive
differentiation inside the latter [29]. However we could ask the relevance of
this criterion in this case study, where only 3% of the neuroscience
publications in our dataset is concerned about AI according to our keyword
filter introduced in Section 3.1. This leads especially to ask how spread is
AI across the topics covered by neuroscience over time, ie. if AI is present
in all subfields of neuroscience or concentrated around a few ones. With such
a topic space, and by reusing the data about the authors and the co-signature
network, we could evaluate more precisely how much universal is AI inside
neuroscience through the distribution of authors in this topic space, and then
deduce the propensity of AI to fit with the knowledge and methods associated
with some topics rather than others.

Nonetheless, the dynamical process of integration of AI in neuroscience is
exhibiting some differences regarding the global history of AI in science
depicted in [13]. Although the development of AI in science is statistically
characterized by a disciplinary closure around STEM disciplines between 1980
and 2010, AI was keeping to be invested and developed by neuroscience for its
own epistemic purposes in this period, even marginally, as shown by a large
number of journals created in this period and that are very active in AI
research (see Section 4.3), and also its citational impact in general (see
Sections 4.2 and 4.1). However, a social closure was simultaneously occurring
and accentuated inside the field, where not everyone is finally using AI at
all, even in recent days. According to Shinn and Joerges (2002), this is
typical of a differentiation process observed in the conception phase of an
instrument.

The penetration of AI inside a single discipline thus could also be described
as another underlying dynamical process of development of the associated
knowledge and instruments following the four steps of Hentschel (2015), which
is included inside the diffusion phase of the original instrument. For
instance, the developed AI inside neuroscience, which could be slightly
different from the originally produced one in STEM disciplines, would be also
adapted to furnish other capabilities in neuroscience first, but also in other
disciplines of fields of research afterwards if they judge it useful for their
own goals. Under such an hypothesis, we could explore more precisely one one
hand the expansion of adjacent possible of neuroscience caused directly by AI,
and on the other hand the expansion of the adjacent possible of other fields
that have been influenced later by an adapted AI that was designed in
neuroscience [18, 23, 4].

In addition of the limitations mentioned throughout this paper, especially for
the building of our database described in Section 3, these results are facing
another important one, which is lying into the type of publications that could
differ from one discipline to another, and the manner it impacts different
disciplinary community as well. Indeed we have shown that most of AI research
published in peer-reviewed journals in neuroscience is impacting mainly the
latter itself, but we could ask this pattern if we add other media for
communicating research results, such as conference proceedings or preprints
sharing platforms such as arXiv, which are most distributed among
mathematicians, physicists and also computer scientists [30], who are here the
most active scientists in AI research. These media thus are susceptible to be
chosen by scientists who are also the most active in AI research in
neuroscience, as we shown in all this paper. Because of the remaining
involvement of these scientists into their disciplines of training, which are
mainly not neuroscience (see Section 4.4), this new consideration could lead
to a further study about the impact of the AI research done in neuroscience,
especially if it is exported outside this field with other communication
media. This study would also brings issues about interdisciplinarity itself
through the communication media, as well as the potential conflict of
publishing traditions in neuroscience that could lead to an impact of these AI
research toward specific disciplines outside the neuroscience purposes.

Finally, in order to study broadly global construction of AI in all the
science, this paper intends to be a road map for further studies of the
diffusion of AI in other disciplines or fields of research that are receptive
to it, but probably with different patterns. A comparative work would then be
required.

##  6 Declarations

  * •

Ethics approval and consent to participate: Not applicable.

  * •

Consent for publication: Not applicable.

  * •

Availability of data, materials and codes: The datasets used during the
current study, and the codes that permitted to analyze them, are available
from the corresponding author on reasonable request.

  * •

Competing interests: The authors declare that they have no competing
interests.

  * •

Funding: S.F. is funded through a CNRS-MITI PhD grant within the project
“Epistemic Impact of Artificial Intelligence in Science” (EpiAI). This
research has been partially supported by the ANR grant ScientIA
(ANR-21-CE38-0020).

  * •

Authors’ contributions: S.F. collected and analyzed the data, discussed the
results, wrote the manuscript. F.G. conceived the research, collected the
data, discussed the results, wrote the manuscript. M.D. discussed the results,
wrote the manuscript. P.T. discussed the results, wrote the manuscript. All
authors have read and approved the manuscript.

  * •

Acknowledgements: Not applicable.

##  Appendix A Self-similarities of references and citations, and disciplinary
concentration in the AI and non-AI corpora

This section focuses on the cognitive development of the research field
associated with the AI corpus ($\mathcal{P}\cap AI$) and the non-AI one
($\mathcal{P}\cap\overline{AI}$) corpora, based on the same similarity measure
introduced in Section 4.1. We compute for each corpus two such measures that
could vary over time:

  1. 1.

the temporal self-similarity of one given ranking $r$, namely the similarity
between this ranking at time $t$ and the the same at the previous time $t-1$,
denoted as $J(r_{t-1},r_{t})$; we apply it for references’ and citations’
rankings in each corpus,

  2. 2.

the temporal disciplinary concentration of one corpus, namely the similarity
between its references ranking $r_{R}$ and its generated citations one $r_{I}$
at a given time $t$, denoted as $J(r_{R}(t),r_{I}(t))$,

The evolution of these two different indices are represented in Fig. 14A and B
respectively.

According to Fig. 14A, the references on which the AI corpus and the non-AI
one draw upon respectively (solid lines) are both consolidating with time
toward their highest respective values in 2017, but not at the same speed.
Indeed the non-AI corpus lies on almost the same set of disciplines from year
to year, while the AI-related one grow from a lower similarity in 1970 around
50% to a stable set with a similarity around 90% between 2015 and 2017. The
observed decrease after 2017 is mainly due to the lack of data grabbed by MAG.
The self-similarities of citations in each corpus (dashed lines) follow the
same trend, but with more variations until a stable phase since 2009 and with
a lower similarity level than for references. This means that the short-term
impacted fields in the two corpora are also consolidating toward rather the
same ones in recent days. More precisely, Fig. 14B indicates the growing
concentration of references’ and impact’s rankings inside each corpus since
around 1990, ie. the impacted disciplines and those appearing in references
become more and more similar, hence a research inspired by itself toward
itself. This shows also the progressive disciplinary homogenisation of the two
researches independently, and therefore a consolidation of the entire research
field of neuroscience.

We notice also in Fig. 14 that the self-similarity of citations of the two
corpora under study are almost identical, thus confirming the high constant
similarity of citations’ rankings between the corpora shown in Fig. 5 in
Section 4.1.

Figure 14: A: Temporal self-similarity of disciplinary rankings associated
with either the references ($R$) or generated citations ($I$) of one specific
corpus, either the AI-related one ($\mathcal{P}\cap AI$) or the non-AI one
($\mathcal{P}\cap\overline{AI}$). More precisely, one point at time $t$ is the
similarity between the ranking at time $t$ and the former one at $t-1$. B:
Instantaneous similarity between the references used by one corpus and its
produced citational impact.

##  Appendix B Disciplinary composition of the common interest area of the AI-
related and non-AI corpora

This section focuses more precisely on the disciplines inside the common
interest area of the AI and non-AI corpora defined in Section 4.2). According
to Fig. 6, each discipline $d$ appearing in the references and/or in the
citations of these corpora is located in a 2D space by the coordinates
($r^{d}_{\mathcal{P}\cap AI},r^{d}_{\mathcal{P}\cap\overline{AI}}$) associated
with its respective ranks in the AI and non-AI corpora. From these coordinates
we compute the distance of the disciplines from the origin of the map (point
with ranks (0,0)), denoted as $\rho$ in the following. Fig.15 shows the
evolution of this distance for some disciplines, here those that are
exhibiting the most significant variations (increasing or decreasing) over the
years since the 1970’s.

This figure shows especially a spectacular rise since around 1995 of the
references’ rankings associated with Radiology and Neuroimaging in both AI and
non-AI corpora, as well as a growing impact of neuroscience articles published
in this period on these disciplines. The 1980’s and early 1990’s marked also
the launching of the first body-scanner machines applying positron emission
tomography (PET) and functional magnetic resonance imaging (fMRI), that both
enabled important discoveries on functional biological mechanisms in human
brain that are induced by complex cognitive tasks [6]. This trend thus
testifies to the diffusion of these technologies in scientific and medical
practices associated with neuroscience.

Figure 15: Time evolution of the distance $\rho$ of the disciplines included
in the common interest area defined and shown in Fig. 6. Only the most
significant are shown with colors, here with the criterion that they distance
must vary at least with an increase or decrease equal to 15 between the
beginning and the end of their respective trajectory.

##  Appendix C Web of Science categories’ abbreviations table

WOS Categories | Abbreviations
---|---
Acoustics | Acoustics
Anatomy & Morphology | A&M
Anesthesiology | Anesth
Anthropology | Anthropo
Automation & Control Systems | A&CS
Biophysics | BioPhys
Biotechnology & Applied Microbiology | Biotech
Cardiac & Cardiovascular System | Cardio
Computer Science | CS
Critical Care Medicine | CCM
Dentistry | Dentistry
Ecology | Ecology
Education | Educ
Endocrinology & Metabolism | E&M
Engineering | Engineering
Entomology | Entomo
Ergonomics | Ergo
Gastroenterology & Hepatology | G&H
Genetics & Heredity | Genetics
Geriatrics & Gerontology | G&G
Hematology | Hemato
Imaging Science & Photographic Technology | IS&PT
Immunology | Immuno
Instruments & Instrumentation | Instrum
Language & Linguistics | L&L
Mathematical & Computational Biology | M&CB
Mathematics | Maths
Mechanics | Mech
Medical Informatics | MI
Medical Laboratory Technology | MLT
Microbiology | Microbio
Microscopy | Microscopy
Neuroimaging | NI
Nutrition & Dietetics | N&D
Operations Research & Management Science | OR&MS
Optics | Optics
Otorhinolaryngology | Otorhino
Pathology | Patho
Pediatrics | Ped
Peripheral Vascular Diseases | PVD
Philosophy | Philo
Physics | Phys
Plant Sciences | Plant
Public, Environmental & Occupational Health | Public Health
Radiology | Radio
Rehabilitation | Rehab
Social Sciences | SocSci
Sport Sciences | Sport
Statistics & Probability | S&P
Substance Abuse | SA
Telecommunications | Telecom
Toxicology | Toxico
Virology | Viro
Zoology | Zoology

##  Appendix D Temporal evolution of the inter-quartile collaboration network

Figure 16: Evolution of the $z$-score matrix of the share of edges between
$f_{AI}$ quartiles. The first line corresponding to the period 1970-1987
refers to the situation where $Q_{2}$ has not yet emerged in our dataset. The
separation of $\overline{Q}$ and the other $Q_{i}$’s occurs qualitatively in
the period 1988-1997 when $Q_{2}$ was emerging and was beginning to publish in
neuroscience. The ending period spanning from 1998-2010 is very similar to the
configuration shown in Fig.13 by considering all the period 1970-2019, as
expected by the high concentration of the edges of the temporal collaboration
network TCN in this period (91% of the 13,786,616 unweighted single edges in
TCN).

## References

  *   * Andler [1990] andler_connexionnisme_1990Andler, D.  1990\.  Connexionnisme et cognition: À la recherche des bonnes questions Connexionnisme et cognition: À la recherche des bonnes questions.  Revue de Synthèse1111-295–127,
  * Arencibia-Jorge . [2022] arencibia-jorge_evolution_2022Arencibia-Jorge, R., Vega-Almeida, R.L., Jiménez Andrade, J.L. Carrillo-Calvet, H.  2022\.  Evolution and Multidisciplinarity of Artificial Intelligence Research Evolution and Multidisciplinarity of Artificial Intelligence Research.  Scientometrics12721–20,
  * Baruffaldi . [2020] baruffaldi_identifying_2020Baruffaldi, S., van Beuzekom, B., Dernis, H., Harhoff, D., Rao, N., Rosenfeld, D. Squicciarini, M.  2020\.  Identifying and measuring developments in artificial intelligence: Making the impossible possible. Identifying and measuring developments in artificial intelligence: Making the impossible possible.  OECD Science, Technology and Industry Working Papers
  * Bianchini . [2022] bianchini_artificial_2022Bianchini, S., Müller, M. Pelletier, P.  2022\.  Artificial intelligence in science: An emerging general method of invention Artificial intelligence in science: An emerging general method of invention.  Research Policy5110,
  * Cardon . [2018] cardon_neurons_2018Cardon, D., Cointet, JP. Mazières, A.  2018\.  Neurons spike back. The invention of inductive machines and the artificial intelligence controversy Neurons spike back. The invention of inductive machines and the artificial intelligence controversy (L. Carey-Libbrecht, ).  Réseaux2115173–220,
  * Cooper Shallice [2010] cooper_cognitive_2010Cooper, R.P. Shallice, T.  2010\.  Cognitive Neuroscience: The Troubled Marriage of Cognitive Science and Neuroscience Cognitive Neuroscience: The Troubled Marriage of Cognitive Science and Neuroscience.  Topics in Cognitive Science23398–406,
  * Crane [1969] crane_social_1969Crane, D.  1969\.  Social Structure in a Group of Scientists: A Test of the ”Invisible College” Hypothesis Social Structure in a Group of Scientists: A Test of the ”Invisible College” Hypothesis.  American Sociological Review343335–352,
  * Frank . [2019] frank_evolution_2019Frank, M.R., Wang, D., Cebrian, M. Rahwan, I.  2019\.  The evolution of citation graphs in artificial intelligence research The evolution of citation graphs in artificial intelligence research.  Nature Machine Intelligence1279–85,
  * Färber [2019] ghidini_microsoft_2019Färber, M.  2019\.  The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data.  C. Ghidini . (), The Semantic Web – ISWC 2019 The Semantic Web – ISWC 2019 ( 11779, 113–129).  Springer International Publishing.
  * Färber [2020] mag_2020Färber, M.  2020\.  Microsoft Academic Graph records. Microsoft Academic Graph records.  Available at https://zenodo.org/record/3936556
  * Gao Wang [2023] gao_quantifying_2023Gao, J. Wang, D.  2023\.  Quantifying the Benefit of Artificial Intelligence for Scientific Research. Quantifying the benefit of artificial intelligence for scientific research.  Preprint at https://arxiv.org/abs/2304.10578
  * Gargiulo . [2016] gargiulo_classical_2016Gargiulo, F., Caen, A., Lambiotte, R. Carletti, T.  2016\.  The classical origin of modern mathematics The classical origin of modern mathematics.  EPJ Data Science5126,
  * Gargiulo . [2022] gargiulo_meso-scale_2022Gargiulo, F., Fontaine, S., Dubois, M. Tubaro, P.  2022\.  A meso-scale cartography of the AI ecosystem. A meso-scale cartography of the AI ecosystem.  Preprint at http://arxiv.org/abs/2212.12263
  * Gopinath [2023] gopinath_artificial_2023Gopinath, N.  2023\.  Artificial intelligence and neuroscience: An update on fascinating relationships Artificial intelligence and neuroscience: An update on fascinating relationships.  Process Biochemistry125113–120,
  * Haas [1992] haas_introduction_1992Haas, P.M.  1992\.  Introduction: Epistemic Communities and International Policy Coordination Introduction: Epistemic Communities and International Policy Coordination.  International Organization4611–35,
  * Hassabis . [2017] hassabis_neuroscience-inspired_2017Hassabis, D., Kumaran, D., Summerfield, C. Botvinick, M.  2017\.  Neuroscience-Inspired Artificial Intelligence Neuroscience-Inspired Artificial Intelligence.  Neuron952245–258,
  * Hentschel [2015] hentschel_periodization_2015Hentschel, K.  2015\.  A periodization of research technologies and of the emergency of genericity A periodization of research technologies and of the emergency of genericity.  Studies in History and Philosophy of Modern Physics52223–233,
  * Kauffman [2000] kauffman_investigations_2000Kauffman, S.A.  2000\.  Investigations Investigations.  Oxford University Press.
  * Lake . [2017] lake_building_2017Lake, B.M., Ullman, T.D., Tenenbaum, J.B. Gershman, S.J.  2017\.  Building Machines That Learn and Think Like People Building Machines That Learn and Think Like People.  Behavioral and Brain Sciences40e2531–72,
  * Liu . [2021] liu_tracking_2021Liu, N., Shapira, P. Yue, X.  2021\.  Tracking developments in artificial intelligence research: constructing and applying a new search strategy Tracking developments in artificial intelligence research: constructing and applying a new search strategy.  Scientometrics12643153–3192,
  * Marcovich Shinn [2020] marcovich_science_2020Marcovich, A. Shinn, T.  2020\.  Science research regimes as architectures of knowledge in context: A ‘longue durée’ comparative historical sociology of structures and dynamics in science Science research regimes as architectures of knowledge in context: A ‘longue durée’ comparative historical sociology of structures and dynamics in science.  Social Science Information592310–328,
  * McCarthy [1981] mccarthy_epistemological_1981McCarthy, J.  1981\.  Epistemological Problems of Artificial Intelligence Epistemological Problems of Artificial Intelligence.  Readings in Artificial Intelligence Readings in Artificial Intelligence ( 459–465).  Elsevier.
  * Monechi . [2017] monechi_waves_2017Monechi, B., Ruiz-Serrano, Ã., Tria, F. Loreto, V.  2017\.  Waves of novelties in the expansion into the adjacent possible Waves of novelties in the expansion into the adjacent possible.  Plos One126,
  * Perconti Plebe [2020] perconti_deep_2020Perconti, P. Plebe, A.  2020\.  Deep learning and cognitive science Deep learning and cognitive science.  Cognition20312,
  * Roth [2008] roth_reseaux_2008Roth, C.  2008\.  Réseaux épistémiques : formaliser la cognition distribuée Réseaux épistémiques : formaliser la cognition distribuée.  Sociologie du Travail503353–371,
  * Savage [2019] savage_how_2019Savage, N.  2019\.  How AI and neuroscience drive each other forwards How AI and neuroscience drive each other forwards.  Nature571776615–17,
  * Schuchmann [2019] schuchmann_history_2019Schuchmann, S.  2019\.  History of the Second AI Winter. History of the Second AI Winter.  Medium. https://towardsdatascience.com/history-of-the-second-ai-winter-406f18789d45 (visited on 2023-04-13)
  * Sedooka . [2015] sedooka_paradoxe_2015Sedooka, A., Steffen, G., Paulsen, T. Darbellay, F.  2015\.  Paradoxe identitaire et interdisciplinarité : un regard sur les identités disciplinaires des chercheurs Paradoxe identitaire et interdisciplinarité : un regard sur les identités disciplinaires des chercheurs.  Natures Sciences Sociétés234367–377,
  * Shinn Joerges [2002] shinn_transverse_2002Shinn, T. Joerges, B.  2002\.  The Transverse Science and Technology Culture: Dynamics and Roles of Research-Technology The Transverse Science and Technology Culture: Dynamics and Roles of Research-Technology.  Social Science Information412207–251,
  * Wainer . [2013] wainer_how_2013Wainer, J., Eckmann, M., Goldenstein, S. Rocha, A.  2013\.  How productivity and impact differ across computer science subareas How productivity and impact differ across computer science subareas.  Communications of the ACM56867–73,