# Building artificial neural circuits for domain-general cognition:
a primer on brain-inspired systems-level architecture ††thanks: This
manuscript is part of the AAAI 2023 Spring Symposium on the Evaluation and
Design of Generalist Systems (EDGeS)

Jascha Achterberg
University of Cambridge & Intel Labs
<EMAIL_ADDRESS>
Danyal Akarca
University of Cambridge
<EMAIL_ADDRESS>
Moataz Assem
University of Cambridge
<EMAIL_ADDRESS>
Moritz Heimbach
Julius-Maximilians-Universität Würzburg
<EMAIL_ADDRESS>
Duncan E. Astle
University of Cambridge
<EMAIL_ADDRESS>
John Duncan
University of Cambridge
<EMAIL_ADDRESS>

###### Abstract

There is a concerted effort to build domain-general artificial intelligence in
the form of universal neural network models with sufficient computational
flexibility to solve a wide variety of cognitive tasks but without requiring
fine-tuning on individual problem spaces and domains. To do this, models need
appropriate priors and inductive biases, such that trained models can
generalise to out-of-distribution examples and new problem sets. Here we
provide an overview of the hallmarks endowing biological neural networks with
the functionality needed for flexible cognition, in order to establish which
features might also be important to achieve similar functionality in
artificial systems. We specifically discuss the role of system-level
distribution of network communication and recurrence, in addition to the role
of short-term topological changes for efficient local computation. As machine
learning models become more complex, these principles may provide valuable
directions in an otherwise vast space of possible architectures. In addition,
testing these inductive biases within artificial systems may help us to
understand the biological principles underlying domain-general cognition.

_K_ eywords domain-general $\cdot$ multimodal $\cdot$ cognition $\cdot$ neural
networks $\cdot$ brain

##  1 Introduction

An aspiration of machine learning research is not just to create architectures
capable of achieving increasingly high levels of task-specific performance,
but the genesis of models able to achieve good performance across different
domains simultaneously. Recent striking advances in network models have
enabled them to solve many problems within a domain with just one architecture
[1, 2, 3]. Additionally, networks are increasingly acquiring multimodal
capabilities [4, 5] and learn in open-ended task environments [6, 7]. These
advances provide necessary building blocks for models capable of domain
general cognition, as observed in intelligent human behaviour. Crucially,
these new models may be able to go beyond simple generalisation to unseen data
[8]; they may be able to learn new abilities and directly abstract them,
allowing for generalisation across entire input modalities and the reuse of
skills learned in one domain to support learning in entirely new domains.
Indeed, this parallels how children learn over the course of their own
development [9]. However, the extent to which current models can achieve this
remains limited.

For decades, neuroscientists have been focused on identifying core features of
the brain’s structural and functional architecture. This allows us to connect
our knowledge of human neural architectures that enable flexible domain-
general cognition [10], with ideas on how we hope to achieve similar
capabilities in artificial systems. Here we provide an overview of mechanisms
underlying domain-general cognition in biological neural networks to derive
which features of the systems-level architecture may be important to build
flexible multimodal problem-solving capabilities into artificial systems.
Previously published reviews have already outlined which cognitive ideas and
modules might be essential [11, 12, 13, 14, 15]. We aim to expand these
cognitive perspectives by providing a brief introduction to the system-level
network structure underlying domain-general cognition in the brain,
highlighting what structural optimisation processes we think could be used in
machine learning models. In this, our goal is not to hard-code brain-like
anatomy into a network model’s architecture. Instead, we aim to identify
computationally beneficial structural motifs which can be soft-coded into the
network’s learning process to serve as helpful inductive biases or priors. As
we see increasingly complex machine learning models being built as a
combination of functional submodules [16, 5], we believe that the system-level
priors we outline may provide helpful guidance to coordinate information flow
in the most complex artificial neural networks [17].

##  2 A core domain-general network in the brain

The human brain, as with many complex physical systems, is economically
organised to balance numerous competing objectives – including metabolic,
computational, and geometric [18, 19]. These objectives have a strong
influence on the topology of the brain’s network; not only is it energetically
expensive to fully build and sustain neural connections [20, 21] but it is
highly costly to constantly communicate signals between neurons and assemblies
of neurons, particularly over longer distances [22]. Owing to its size,
complexity, and these economic considerations, it is infeasible for each
neural region to communicate directly with every other region equivalently
[23]. To avoid this problem, evolutionary pressures have guided the brain
towards a modularised network, with modules of very strong local connectivity
and high-connection hub nodes connecting across these modules [24, 25].
Networks with this structure are described as having “small-world”
characteristics, defined as having concurrently a highly clustered topology
and short path lengths, meeting a balance between totally random versus
regular networks [26, 27]. Small-world structures are commonly found in
distributed systems under resource constraints, showing patterns of locally
specialised computation alongside good propagation of signals within and
between hubs. In brains, this locality of computations results in
concentration of specific cognitive function within specific anatomical
regions. Specialised regions act as foci for cognitive functions like sensory
processing, semantic knowledge, and language abilities [28, 29, 30]. These are
likely semi-specialised, meaning that they mostly focus on unique local
computation but also partially integrate meaningful information across areas
and domains [31, 32].

This picture of a functionally modular system becomes more nuanced when we
consider human domain-general cognition. As the tasks to be solved become more
complicated, the brain increasingly abandons solely relying on its specialised
modular structure. Instead, neural architectures must increasingly integrate
signals across modules [33] and rely on its Multiple Demand system (MD) [10,
34]. This is a core network in the brain (depicted in Figure 1A) which is
highly active when a complex task of any nature is solved. It is thought that
the MD system serves as a central processing unit, receiving information from
more specialised input notes, to compress it into meaningful abstract
representations on which it can run problem solving algorithms (schematic
shown in Figure 1B). It also plays a central role in controlling information
in other brain regions, using knowledge and complex analysis of the situation
to control thought processes in specialised brain regions through top-down
control processes [10, 35, 36, 37, 38]. Ultimately it is this central
processing circuit that likely gives the primate brain the ability to have
abstract thoughts used to solve complex problems to reach long-term goals.

Figure 1: A - The cortical areas forming the core Multiple Demand system in
the human brain, from [34]. B – Schematic depiction of a systems-level view of
the brain. The Multiple Demand system lies at the core of information
processing in the brain, exchanging inputs with more specialised regions such
as language, memory, sensory and social processing. Due to its central
position, the MD core can influence computations in multiple specialised areas
by broadcasting information it constructed from integrating across domains
back to specialised regions, e.g., influencing perception by abstract
understanding of the environment / situation at large. Refer to [34] for
detailed anatomical perspective of the MD system’s core and penumbra regions
not discussed here.

What are the key principles underpinning this system? In the following we will
discuss three computational / structural motifs which vary across the
hierarchy from specialised regions to the integrative MD system, which allow
the network to show domain-general cognitive skills. These are: Recurrence,
communicability, and short-term topological changes. We will review each of
these in terms of their relevance in biological networks before then
discussing possible directions for artificial implementations, in the context
of related existing implementations.

##  3 Computational motifs supporting domain-general cognition

###  3.1 Global recurrence

Computations in functionally more specialised regions depend strongly on a
feed-forward structure that extracts increasingly abstract features from
sensory inputs [39, 40, 41]. Much work shows how this process can be modelled
using an artificial feed-forward network [42, 43]. While there is also
recurrent processing in these specialised systems [39, 40, 44], the recurrent
loops in these systems are likely very local and cover relatively short
distances and timescales. This means that a signal sent from a node will only
travel a short path before arriving back at its starting point. As we move
towards more integrative and domain-general cognition, recurrent connections
become a hallmark feature of the brain’s systems-level design. The frontal
cortex, where a large part of the MD system lies, is often thought of as
implementing recurrent loops for abstract information processing [41, 45].
Importantly, these loops not only process information locally but also
broadcast information widely across the brain, influencing and controlling
computations in specialised regions. It does so by not only having local
recurrent connections within the circuit but also many loops spanning large
distances in the brain, reaching out to nodes which lie far outside the core
[37, 46, 41, 36]. With nodes widely distributed over the cortex, coupled to
strong communication between these nodes, the MD system is well positioned for
widespread integration and communication. A large set of recursive processing
loops with varied scales in terms of time and spatial distance likely
facilitate the MD system’s abstract domain-general processing and deliver the
ability to coordinate computation in a large distributed system [10].

The use of recurrent loops in artificial neural networks has a long history
[47]. They proved to be useful tools for processing and predicting time series
data but also suffered from problems of vanishing gradient and computational
complexity when capturing long-range dependencies in the input [48, 49, 50].
To avoid these issues, feed-forward based architectures can be used as
substitutes [50] and various attention-based architectures have recently been
very effective in capturing dependencies in language time courses and multiple
other modalities [49, 51]. This works by inputting an entire time series in a
single time step so that the attention mechanism learns the relationship
between timesteps without needing to hold past time points in memory. While
these architectures likely can be good substitutes for the local recurrent
loops, we believe that ultimately, researchers are going to have to find a way
to also introduce global recurrent loops to arrive at domain-general cognition
in artificial systems. Approaches like weight-sharing in deep models paired
with skip-connections may allow us to mimic a recurrent process in a regular
forward pass but it seems likely that alternative ways will be needed to allow
abstract multimodal knowledge to be broadcasted through the network to inform
distributed computations. This seems even more timely now that models generate
impressive responses to inputs such as images or language [1, 52], but
struggle to be constrained by meaningful world models (e.g., intuitive
physics, [15]). Instead, researchers rely on human feedback signals in the
training pipeline [53]. As such, machine learning models may need to be
adapted to allow for the introduction of a global recurrent architecture
similar to the MD system.

###  3.2 Communicability in large scale networks

For any complex network which is concerned with processing information, it is
of central importance to optimise how signals are communicated between the
nodes within the network [54]. This becomes an increasingly challenging
problem as a network grows, leaving nodes to only be able to communicate with
a smaller proportion of the network. This limited communication capacity
naturally leads to variation in terms of how much information is exchanged
between different pairs of nodes across the network. This results in a very
real challenge for any large-scale network system to optimise its structure to
integrate information most effectively and efficiently across its functional
hubs. This is constrained, ultimately, by the topological arrangement of the
network. The idea of how much information is exchanged between nodes is
captured by the concept of communicability [54, 55, 56] and is a highly
effective framework to understand how the structure of the brain guides
function [57, 58, 59, 60, 61, 62, 63]. Specifically, across the brain’s
complex network, regions vary in terms of how well they can communicate to
other regions, and the macro-scale dynamics and capabilities of the brain will
be determined by this interareal communication. This heterogeneous
communicability becomes especially interesting when one considers how system-
level communication link to domain-general cognition. In the previous section,
we described how more specialised regions tend to have a mostly feed-forward
structure with some local recurrence. As such, information tends to be
communicated locally between adjacent and functionally related regions. This
changes as information approaches the domain-general MD system with its wider
communicative influence. In its central position, the MD system not only
receives information from all over the brain but utilises its widespread
connectivity as global recurrent loops to broadcast processed information to a
distributed set of brain regions [41, 10, 36]. On the systems-level
perspective of the brain, a given region’s communicative structure heavily
depends on its functional role and hence its degree of specialisation.

The concept of heterogeneous communicability between regions and modules of
the brain has not been particularly relevant in artificial neural network
architectures which were state-of-the-art until very recently. Take
convolutional neural networks (CNNs) as an example. In CNNs, which dominated
processing of visual information for several years [47], information is mostly
passed along from layer to layer in a relatively even fashion. This means
regions do not stick out has having a particular communicative ability (though
see work like [64] for interesting communicative extensions of CNNs). However,
this is changing with new architectures which have been growing in scale [65].
Especially for network models which utilise multiple modalities, architectures
have increasingly been created by combining existing pre-trained models into
more complex modular architectures [52, 5]. Once we build complex system like
these, it becomes increasingly important to not only think about which models
to combine, but also how to combine them. This means that the communicability
between parts of the network can be optimised to achieve better information
flow between components and hence improve performance. A first step in this
direction was made by a multimodal transformer model which outperformed prior
networks by introducing a set of special bridge layers to connect two modality
specific models. These bridge layers allow the model to learn a communicative
structure in which abstract semantic knowledge is gradually merged across
modalities. This increased performance in several relevant benchmark tasks
[66]. In addition, other implementations have shown that bringing ideas from
highly communicative small world graph structures into a Transformer’s
attention mechanism can help with processing longer sequences [67]. In simple
recurrent neural networks, we also have seen that system-level communicability
can easily be used as a regularisation term to optimise the communicative
structure of a sparsely connected network to arrive at a network with many
brain-like structural and functional properties [68]. As network models grow
in complexity and increasingly make use of composite structures which combine
sub models into larger networks, it will be important to fine tune the
communicative structure of a network. Having good priors and inductive biases
for these linkages can help circumvent problems arising from adding the
extensive set of connections it would require to fully connect multiple models
which already have a complex structure themselves. Following this line of
thinking, we believe that making use of work on communicability and how it can
be optimised in complex networks will be of central importance to inform model
building on a systems-level.

###  3.3 Short-term topological changes

The discussion so far has focused on how the systems-level network structure
of the brain and the unique communicative structure of its MD system play a
key role in the domain-general cognition we see in humans. An important
element of its flexible and multimodal information processing capabilities is
how the MD system’s network structure is not fully fixed but often rapidly
changing. This means that while the MD system is running multimodal
computations internally, the connections between its neurons are in continuous
flux. As such, the general problem-solving ability of this network is assumed
to be due to its inherent flexibility. In it, local computations are organised
by rapid changes to the network structure [69, 70, 71], often called short-
term plasticity. This allows the network to continuously reassign its neurons
and modules new computational roles while solving complex sequential problems
[72, 37, 73, 74, 75]. Rapid topological changes are likely induced by local
learning rules which supplement the more long-term optimisation of the global
network structure. These mechanisms likely underly a multitude of complex
abilities of the human brain [34, 76] and some of them strongly overlap with
timely discussions in machine learning. As one example, research points to the
fact that the MD system uses its short-term dynamics for attentional control,
to focus on information which is relevant for the current operation [77, 78,
79] and break complex pieces of information down into simple computable bits
[10] – a function which has played a central role in machine learning
discussion recently [64, 80]. Another example is the MD system’s ability to
construct abstract representations of problems [81] to then tie observed
stimuli rapidly to their roles in this abstract problem representation [10,
75], a phenomenon going by the name of variable binding [82] or meta-learning
[83]. These are very related to few-shot learning [1] and in-context learning
abilities [84] observed in large Transformer models.

As we already see foundations of these skills emerging in currently existing
architectures it is reasonable to believe that they will continue to improve
purely by scaling existing architectures [65]. In this case models would use
their unit activations to implement rapid in-context learning and it has been
shown that this can work well without any short-term synaptic changes [85]. In
fact, even in the brain many complex computations are likely facilitated due
to dynamics of the network activations which do not necessarily have to rely
on changes in the network structure [86]. But once computations reach the
scale of using network-wide attention processes to controlling the flow of
information across the entire brain network and flexibly combining task
modules to solve the task at hand [10, 87, 88], rapid topological network
changes might be necessary for domain-general computations [69, 10]. Reaching
this level of flexible and multimodal cognition might not be possible in
current static architectures and hence might require us to allow models to
modify some of their connections in the moment through local learning rules.
Some work in smaller network models is highlighting how local learning
mechanisms can complement network-wide optimisation processes [89, 90] with
relevant comparisons to Transformer implementations [91]. Other examples point
to how local learning rules and single neuron-based optimisation principles by
themselves can be sufficient to solve meaningful cognitive tasks [92, 93]. In
addition, we have seen how standard network optimisers can be updated with
certainty judgements to support rapid relational learning [94]. If we could
scale these rapid learning dynamics to large Transformer models, this might
allow models to flexibly combine abstract task structures with capabilities
learned in the past, to flexibly apply skills across modalities in a truly
domain-general way. One research direction which might support rapid learning
processes is work on using local loss functions and learning mechanisms to
substitute costly global optimisation processes [95, 96, 97]. Combining these
local optimisation processes with more wide-spread recurrent loops and an
optimised communicative structure in large networks might bring us closer to
observing flexible domain-general cognition in artificial neural networks.

##  4 Conclusion

We believe that in the pursuit of building artificial intelligence which is
able to engage in domain-general problem solving, a systems-level view of the
human brain will provide useful guidance [98, 99]. We believe this will become
increasingly relevant as AI systems become more and more complex. The topics
of recurrence, communication and rapid structural changes are particularly
relevant at the current point due to their central role in theories of domain-
general cognition in the brain and their links to existing works in neural
network models. As such, they might be key drivers behind efficient and
flexible information processing in large multimodal networks. But we do not
believe that any of these features should be fully hard-coded – instead we
should think of them as useful priors and inductive biases which can guide
complex learning processes. Ultimately, bringing these features into machine
learning models opens up the perspective of not only improving the performance
of artificial neural networks but also for us to understand which core
principles underly domain-general and multimodal computations in neural
networks - may these be biological or artificial.

##  5 Acknowledgments

J.A., Da.A., M.A., Du.A., and J.D. are supported by UKRI MRC funding and as a
result the authors have applied a Creative Commons Attribution (CC BY) license
to this manuscript for the purpose of open access. J.A. receives a Gates
Cambridge Scholarship. Da.A. receives a Cambridge Trust Vice Chancellor’s
Scholarship. Da.A. and Du.A. are both supported by the James S. McDonnell
Foundation Opportunity Award. J.A. was a research intern at Intel Labs at the
time of writing this manuscript.

## References

  * [1] Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei.  Language Models are Few-Shot Learners, July 2020.  arXiv:2005.14165 [cs].
  * [2] Taylor Webb, Keith J. Holyoak, and Hongjing Lu.  Emergent Analogical Reasoning in Large Language Models, December 2022.  arXiv:2212.09196 [cs].
  * [3] Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, et al.  Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models, June 2022.  arXiv:2206.04615 [cs, stat].
  * [4] Peng Xu, Xiatian Zhu, and David A. Clifton.  Multimodal Learning with Transformers: A Survey, June 2022.  arXiv:2206.06488 [cs].
  * [5] Cem Akkus, Luyang Chu, Vladana Djakovic, Steffen Jauch-Walser, Philipp Koch, Giacomo Loss, Christopher Marquardt, Marco Moldovan, Nadja Sauter, Maximilian Schneider, Rickmer Schulte, Karol Urbanczyk, Jann Goschenhofer, Christian Heumann, Rasmus Hvingelby, Daniel Schalk, and Matthias Aßenmacher.  Multimodal Deep Learning, January 2023.  arXiv:2301.04856 [cs, stat].
  * [6] Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, and Anima Anandkumar.  MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge, November 2022.  arXiv:2206.08853 [cs].
  * [7] Adaptive Agent Team, Jakob Bauer, Kate Baumli, Satinder Baveja, Feryal Behbahani, Avishkar Bhoopchand, Nathalie Bradley-Schmieg, Michael Chang, Natalie Clay, Adrian Collister, Vibhavari Dasagi, Lucy Gonzalez, Karol Gregor, Edward Hughes, Sheleem Kashem, Maria Loks-Thompson, Hannah Openshaw, Jack Parker-Holder, Shreya Pathak, Nicolas Perez-Nieves, Nemanja Rakicevic, Tim Rocktäschel, Yannick Schroecker, Jakub Sygnowski, Karl Tuyls, Sarah York, Alexander Zacherl, and Lei Zhang.  Human-Timescale Adaptation in an Open-Ended Task Space, January 2023.  arXiv:2301.07608 [cs].
  * [8] Moritz Hardt and Benjamin Recht.  Patterns, predictions, and actions: a story about machine learning.  Princeton University Press, Princeton, 2022.
  * [9] Rogier A Kievit.  Sensitive periods in cognitive development: a mutualistic perspective.  Current Opinion in Behavioral Sciences, 36:144–149, December 2020\.
  * [10] John Duncan, Moataz Assem, and Sneha Shashidhara.  Integrated Intelligence from Distributed Brain Activity.  Trends in Cognitive Sciences, 24(10):838–852, October 2020.
  * [11] Jacob Russin, Randall C O’Reilly, and Yoshua Bengio.  DEEP LEARNING NEEDS A PREFRONTAL CORTEX.  “Bridging AI and Cognitive Science” (ICLR 2020), 2020.
  * [12] Anirudh Goyal and Yoshua Bengio.  Inductive biases for deep learning of higher-level cognition.  Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 478(2266):20210068, October 2022.  Publisher: Royal Society.
  * [13] Rufin VanRullen and Ryota Kanai.  Deep learning and the Global Workspace Theory.  Trends in Neurosciences, 44(9):692–704, September 2021.
  * [14] Yann LeCun.  A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27, June 2022.
  * [15] Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman.  Building machines that learn and think like people.  Behavioral and Brain Sciences, 40:e253, 2017.  Publisher: Cambridge University Press.
  * [16] Jonas Pfeiffer, Sebastian Ruder, Ivan Vulić, and Edoardo Maria Ponti.  Modular Deep Learning, February 2023.  arXiv:2302.11529 [cs].
  * [17] Anirudh Goyal, Aniket Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Mozer, and Yoshua Bengio.  Coordination Among Neural Modules Through a Shared Global Workspace, March 2022.  arXiv:2103.01197 [cs, stat].
  * [18] Santiago Ramon y Cajal, Neely Swanson, Larry W. Swanson, Santiago Ramon y Cajal, Neely Swanson, and Larry W. Swanson.  Cajal’s Histology of the Nervous System of Man and Vertebrates.  History of Neuroscience. Oxford University Press, Oxford, New York, April 1995.
  * [19] Ed Bullmore and Olaf Sporns.  The economy of brain network organization.  Nature Reviews Neuroscience, 13(5):336–349, May 2012.  Number: 5 Publisher: Nature Publishing Group.
  * [20] Marcus E. Raichle and Debra A. Gusnard.  Appraising the brain’s energy budget.  Proceedings of the National Academy of Sciences, 99(16):10237–10239, August 2002.  Publisher: Proceedings of the National Academy of Sciences.
  * [21] Dardo Tomasi, Gene-Jack Wang, and Nora D. Volkow.  Energetic cost of brain functional connectivity.  Proceedings of the National Academy of Sciences, 110(33):13642–13647, August 2013.  Publisher: Proceedings of the National Academy of Sciences.
  * [22] William B Levy and Victoria G. Calvert.  Communication consumes 35 times more energy than computation in the human cortex, but both costs are needed to predict synapse number.  Proceedings of the National Academy of Sciences, 118(18):e2008173118, May 2021.  Publisher: Proceedings of the National Academy of Sciences.
  * [23] Szabolcs Horvát, Răzvan Gămănut, Mária Ercsey-Ravasz, Loïc Magrou, Bianca Gămănut, David C. Van Essen, Andreas Burkhalter, Kenneth Knoblauch, Zoltán Toroczkai, and Henry Kennedy.  Spatial Embedding and Wiring Cost Constrain the Functional Layout of the Cortical Network of Rodents and Primates.  PLOS Biology, 14(7):e1002512, July 2016.  Publisher: Public Library of Science.
  * [24] Laura E Suarez, Yossi Yovel, Martijn P van den Heuvel, Olaf Sporns, Yaniv Assaf, Guillaume Lajoie, and Bratislav Misic.  A connectomics-based taxonomy of mammals.  eLife, 11:e78635, November 2022.  Publisher: eLife Sciences Publications, Ltd.
  * [25] Andrea I. Luppi, Pedro A. M. Mediano, Fernando E. Rosas, Negin Holland, Tim D. Fryer, John T. O’Brien, James B. Rowe, David K. Menon, Daniel Bor, and Emmanuel A. Stamatakis.  A synergistic core for human brain evolution and cognition.  Nature Neuroscience, 25(6):771–782, June 2022.  Number: 6 Publisher: Nature Publishing Group.
  * [26] Danielle Smith Bassett and Ed Bullmore.  Small-World Brain Networks.  The Neuroscientist, 12(6):512–523, December 2006.  Publisher: SAGE Publications Inc STM.
  * [27] Danielle S. Bassett and Edward T. Bullmore.  Small-World Brain Networks Revisited.  The Neuroscientist, 23(5):499–516, October 2017.  Publisher: SAGE Publications Inc STM.
  * [28] Jon H Kaas and Christine E Collins.  The organization of sensory cortex.  Current Opinion in Neurobiology, 11(4):498–504, August 2001.
  * [29] Matthew A. Lambon Ralph, Elizabeth Jefferies, Karalyn Patterson, and Timothy T. Rogers.  The neural and computational bases of semantic cognition.  Nature Reviews Neuroscience, 18(1):42–55, January 2017.  Number: 1 Publisher: Nature Publishing Group.
  * [30] Michael A. Skeide and Angela D. Friederici.  The ontogeny of the cortical language network.  Nature Reviews Neuroscience, 17(5):323–332, May 2016.  Number: 5 Publisher: Nature Publishing Group.
  * [31] Huriye Atilgan, Stephen M. Town, Katherine C. Wood, Gareth P. Jones, Ross K. Maddox, Adrian K. C. Lee, and Jennifer K. Bizley.  Integration of Visual Information in Auditory Cortex Promotes Auditory Scene Analysis through Multisensory Binding.  Neuron, 97(3):640–655.e4, February 2018.
  * [32] Nicholas A. Steinmetz, Peter Zatka-Haas, Matteo Carandini, and Kenneth D. Harris.  Distributed coding of choice, action and engagement across the mouse brain.  Nature, 576(7786):266–273, December 2019.  Number: 7786 Publisher: Nature Publishing Group.
  * [33] Jonathan D. Power, Bradley L. Schlaggar, Christina N. Lessov-Schlaggar, and Steven E. Petersen.  Evidence for Hubs in Human Functional Brain Networks.  Neuron, 79(4):798–813, August 2013.
  * [34] Moataz Assem, Matthew F Glasser, David C Van Essen, and John Duncan.  A Domain-General Cognitive Core Defined in Multimodally Parcellated Human Cortex.  Cerebral Cortex, 30(8):4361–4380, June 2020.
  * [35] Gustavo Deco, Diego Vidaurre, and Morten L. Kringelbach.  Revisiting the global workspace orchestrating the hierarchical organization of the human brain.  Nature Human Behaviour, 5(4):497–511, April 2021.  Number: 4 Publisher: Nature Publishing Group.
  * [36] Stanislas Dehaene, Michel Kerszberg, and Jean-Pierre Changeux.  A neuronal model of a global workspace in effortful cognitive tasks.  Proceedings of the National Academy of Sciences, 95(24):14529–14534, November 1998.  Publisher: Proceedings of the National Academy of Sciences.
  * [37] Earl K. Miller and Jonathan D. Cohen.  An Integrative Theory of Prefrontal Cortex Function.  Annual Review of Neuroscience, 24(1):167–202, 2001.  _eprint: https://doi.org/10.1146/annurev.neuro.24.1.167.
  * [38] Donald A. Norman and Tim Shallice.  Attention to Action.  In Richard J. Davidson, Gary E. Schwartz, and David Shapiro, editors, Consciousness and Self-Regulation: Advances in Research and Theory Volume 4, pages 1–18. Springer US, Boston, MA, 1986.
  * [39] Kalanit Grill-Spector and Rafael Malach.  The Human Visual Cortex.  Annual Review of Neuroscience, 27(1):649–677, 2004.  _eprint: https://doi.org/10.1146/annurev.neuro.27.070203.144220.
  * [40] Troy A. Hackett.  Information flow in the auditory cortical network.  Hearing Research, 271(1):133–146, January 2011.
  * [41] George A. Mashour, Pieter Roelfsema, Jean-Pierre Changeux, and Stanislas Dehaene.  Conscious Processing and the Global Neuronal Workspace Hypothesis.  Neuron, 105(5):776–798, March 2020.
  * [42] Martin Schrimpf, Jonas Kubilius, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Daniel L. K. Yamins, and James J. DiCarlo.  Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?, September 2018.  bioRxiv: 407007 Section: New Results.
  * [43] Grace W. Lindsay.  Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future.  Journal of Cognitive Neuroscience, 33(10):2017–2031, September 2021\.
  * [44] Tim C. Kietzmann, Courtney J. Spoerer, Lynn K. A. Sörensen, Radoslaw M. Cichy, Olaf Hauk, and Nikolaus Kriegeskorte.  Recurrence is required to capture the representational dynamics of the human visual system.  Proceedings of the National Academy of Sciences, 116(43):21854–21863, October 2019.  Publisher: Proceedings of the National Academy of Sciences.
  * [45] Earl K. Miller and Timothy J. Buschman.  Rules through Recursion: How Interactions between the Frontal Cortex and Basal Ganglia May Build Abstract, Complex Rules from Concrete, Simple Ones.  In Silvia A. Bunge and Jonathan D. Wallis, editors, Neuroscience of Rule-Guided Behavior, page 0. Oxford University Press, November 2007\.
  * [46] Yuko Munakata, Seth A. Herd, Christopher H. Chatham, Brendan E. Depue, Marie T. Banich, and Randall C. O’Reilly.  A unified framework for inhibitory control.  Trends in Cognitive Sciences, 15(10):453–459, October 2011.
  * [47] Juergen Schmidhuber.  Annotated History of Modern AI and Deep Learning, December 2022\.  arXiv:2212.11279 [cs].
  * [48] Sepp Hochreiter and Jürgen Schmidhuber.  Long Short-Term Memory.  Neural Computation, 9(8):1735–1780, November 1997.  Conference Name: Neural Computation.
  * [49] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin.  Attention is All you Need.  In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017.
  * [50] Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller.  Deep learning for time series classification: a review.  Data Mining and Knowledge Discovery, 33(4):917–963, July 2019.  arXiv:1809.04356 [cs, stat].
  * [51] Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler.  Efficient Transformers: A Survey.  ACM Computing Surveys, 55(6):109:1–109:28, December 2022.
  * [52] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer.  High-Resolution Image Synthesis with Latent Diffusion Models, April 2022.  arXiv:2112.10752 [cs].
  * [53] Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe.  Training language models to follow instructions with human feedback, March 2022.  arXiv:2203.02155 [cs].
  * [54] Ernesto Estrada, Naomichi Hatano, and Michele Benzi.  The physics of communicability in complex networks.  Physics Reports, 514(3):89–119, May 2012.
  * [55] Jonathan J Crofts and Desmond J Higham.  A weighted communicability measure applied to complex brain networks.  Journal of The Royal Society Interface, 6(33):411–414, January 2009\.  Publisher: Royal Society.
  * [56] Pragya Srivastava, Erfan Nozari, Jason Z. Kim, Harang Ju, Dale Zhou, Cassiano Becker, Fabio Pasqualetti, George J. Pappas, and Danielle S. Bassett.  Models of communication and control for brain networks: distinctions, convergence, and future outlook.  Network Neuroscience, 4(4):1122–1159, November 2020.
  * [57] Joaquín Goñi, Martijn P. van den Heuvel, Andrea Avena-Koenigsberger, Nieves Velez de Mendizabal, Richard F. Betzel, Alessandra Griffa, Patric Hagmann, Bernat Corominas-Murtra, Jean-Philippe Thiran, and Olaf Sporns.  Resting-brain functional connectivity predicted by analytic measures of network communication.  Proceedings of the National Academy of Sciences, 111(2):833–838, January 2014.  Publisher: Proceedings of the National Academy of Sciences.
  * [58] Caio Seguin, Adeel Razi, and Andrew Zalesky.  Inferring neural signalling directionality from undirected structural connectomes.  Nature Communications, 10(1):4289, September 2019.  Number: 1 Publisher: Nature Publishing Group.
  * [59] Richard F. Betzel, Joshua Faskowitz, Bratislav Mišić, Olaf Sporns, and Caio Seguin.  Multi-policy models of interregional communication in the human connectome, May 2022.  bioRxiv: 2022.05.08.490752 Section: New Results.
  * [60] Alessandra Griffa, Mathieu Mach, Julien Dedelley, Daniel Gutierrez-Barragan, Alessandro Gozzi, Gilles Allali, Joanes Grandjean, Dimitri Van De Ville, and Enrico Amico.  The evolution of information transmission in mammalian brain networks, May 2022.  bioRxiv: 2022.05.09.491115 Section: New Results.
  * [61] Andrea Avena-Koenigsberger, Bratislav Misic, and Olaf Sporns.  Communication dynamics in complex brain networks.  Nature Reviews Neuroscience, 19(1):17–33, January 2018.  Number: 1 Publisher: Nature Publishing Group.
  * [62] Andrea Avena-Koenigsberger, Xiaoran Yan, Artemy Kolchinsky, Martijn P. van den Heuvel, Patric Hagmann, and Olaf Sporns.  A spectrum of routing strategies for brain networks.  PLOS Computational Biology, 15(3):e1006833, March 2019.  Publisher: Public Library of Science.
  * [63] Simon B. Laughlin and Terrence J. Sejnowski.  Communication in Neuronal Networks.  Science, 301(5641):1870–1874, September 2003.  Publisher: American Association for the Advancement of Science.
  * [64] Abhinav Shrivastava, Rahul Sukthankar, Jitendra Malik, and Abhinav Gupta.  Beyond Skip Connections: Top-Down Modulation for Object Detection, September 2017.  arXiv:1612.06851 [cs].
  * [65] Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei.  Scaling Laws for Neural Language Models, January 2020.  arXiv:2001.08361 [cs, stat].
  * [66] Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, and Nan Duan.  BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning, February 2023.  arXiv:2206.08657 [cs].
  * [67] Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, and Amr Ahmed.  Big Bird: Transformers for Longer Sequences, January 2021.  arXiv:2007.14062 [cs, stat].
  * [68] Jascha Achterberg, Danyal Akarca, D. J. Strouse, John Duncan, and Duncan E. Astle.  Spatially-embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings, November 2022.  bioRxiv: 2022.11.17.516914 Section: New Results.
  * [69] Mark G. Stokes.  ‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework.  Trends in Cognitive Sciences, 19(7):394–405, July 2015.
  * [70] Hua Tang, Mitchell R. Riley, Balbir Singh, Xue-Lian Qi, David T. Blake, and Christos Constantinidis.  Prefrontal cortical plasticity during learning of cognitive tasks.  Nature Communications, 13(1):90, January 2022.  Number: 1 Publisher: Nature Publishing Group.
  * [71] Miguel A. García-Cabezas, Mary Kate P. Joyce, Yohan J. John, Basilis Zikopoulos, and Helen Barbas.  Mirror trends of plasticity and stability indicators in primate prefrontal cortex.  European Journal of Neuroscience, 46(8):2392–2405, 2017.  _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ejn.13706.
  * [72] John Duncan.  An adaptive coding model of neural function in prefrontal cortex.  Nature Reviews Neuroscience, 2(11):820–829, November 2001.  Number: 11 Publisher: Nature Publishing Group.
  * [73] David A. Crowe, Bruno B. Averbeck, and Matthew V. Chafee.  Rapid Sequences of Population Activity Patterns Dynamically Encode Task-Critical Spatial Information in Parietal Cortex.  Journal of Neuroscience, 30(35):11640–11653, September 2010.  Publisher: Society for Neuroscience Section: Articles.
  * [74] Ethan M. Meyers, David J. Freedman, Gabriel Kreiman, Earl K. Miller, and Tomaso Poggio.  Dynamic Population Coding of Category Information in Inferior Temporal and Prefrontal Cortex.  Journal of Neurophysiology, 100(3):1407–1419, September 2008.  Publisher: American Physiological Society.
  * [75] Jascha Achterberg, Mikiko Kadohisa, Kei Watanabe, Makoto Kusunoki, Mark J. Buckley, and John Duncan.  A One-Shot Shift from Explore to Exploit in Monkey Prefrontal Cortex.  Journal of Neuroscience, 42(2):276–287, January 2022.  Publisher: Society for Neuroscience Section: Research Articles.
  * [76] John Duncan.  The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour.  Trends in Cognitive Sciences, 14(4):172–179, April 2010.
  * [77] Masamichi Sakagami and Hiroaki Niki.  Encoding of behavioral significance of visual stimuli by primate prefrontal neurons: relation to relevant task conditions.  Experimental Brain Research, 97(3):423–436, January 1994.
  * [78] Gregor Rainer, Wael F. Asaad, and Earl K. Miller.  Selective representation of relevant information by neurons in the primate prefrontal cortex.  Nature, 393(6685):577–579, June 1998.  Number: 6685 Publisher: Nature Publishing Group.
  * [79] Timothy J. Buschman, Eric L. Denovellis, Cinira Diogo, Daniel Bullock, and Earl K. Miller.  Synchronous Oscillatory Neural Ensembles for Rules in the Prefrontal Cortex.  Neuron, 76(4):838–846, November 2012.
  * [80] Grace W. Lindsay.  Attention in Psychology, Neuroscience, and Machine Learning.  Frontiers in Computational Neuroscience, 14, 2020.
  * [81] Jonathan D. Wallis, Kathleen C. Anderson, and Earl K. Miller.  Single neurons in prefrontal cortex encode abstract rules.  Nature, 411(6840):953–956, June 2001.  Number: 6840 Publisher: Nature Publishing Group.
  * [82] Paul Smolensky.  Tensor product variable binding and the representation of symbolic structures in connectionist systems.  Artificial Intelligence, 46(1):159–216, November 1990.
  * [83] Matthew Botvinick, Sam Ritter, Jane X. Wang, Zeb Kurth-Nelson, Charles Blundell, and Demis Hassabis.  Reinforcement Learning, Fast and Slow.  Trends in Cognitive Sciences, 23(5):408–422, May 2019.  Publisher: Elsevier.
  * [84] Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov.  Transformers learn in-context by gradient descent, December 2022.  arXiv:2212.07677 [cs].
  * [85] Jane X. Wang, Zeb Kurth-Nelson, Dharshan Kumaran, Dhruva Tirumala, Hubert Soyer, Joel Z. Leibo, Demis Hassabis, and Matthew Botvinick.  Prefrontal cortex as a meta-reinforcement learning system.  Nature Neuroscience, 21(6):860–868, June 2018.  Number: 6 Publisher: Nature Publishing Group.
  * [86] Saurabh Vyas, Matthew D. Golub, David Sussillo, and Krishna V. Shenoy.  Computation Through Neural Population Dynamics.  Annual Review of Neuroscience, 43(1):249–275, 2020.  _eprint: https://doi.org/10.1146/annurev-neuro-092619-094115.
  * [87] Timothy J. Buschman and Earl K. Miller.  Goal-direction and top-down control.  Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1655):20130471, November 2014.  Publisher: Royal Society.
  * [88] Camden J. MacDowell, Alexandra Libby, Caroline I. Jahn, Sina Tafazoli, and Timothy J. Buschman.  Multiplexed Subspaces Route Neural Activity Across Brain-wide Networks, February 2023.  bioRxiv: 2023.02.08.527772 Section: New Results.
  * [89] James C. R. Whittington, Timothy H. Muller, Shirley Mark, Guifen Chen, Caswell Barry, Neil Burgess, and Timothy E. J. Behrens.  The Tolman-Eichenbaum Machine: Unifying Space and Relational Memory through Generalization in the Hippocampal Formation.  Cell, 183(5):1249–1263.e23, November 2020.
  * [90] Ronald Boris Dekker, Fabian Otto, and Christopher Summerfield.  Determinants of human compositional generalization, March 2022.
  * [91] James C. R. Whittington, Joseph Warren, and Timothy E. J. Behrens.  Relating transformers to models and neural representations of the hippocampal formation, March 2022.  arXiv:2112.04035 [cs, q-bio].
  * [92] Nicolas Y. Masse, Guangyu R. Yang, H. Francis Song, Xiao-Jing Wang, and David J. Freedman.  Circuit mechanisms for the maintenance and manipulation of information in working memory.  Nature Neuroscience, 22(7):1159–1167, July 2019.  Number: 7 Publisher: Nature Publishing Group.
  * [93] J. Benjamin Falandays, Jeff Yoshimi, William Warren, and Michael Spivey.  A Potential Mechanism for Gibsonian Resonance: Behavioral Entrainment Emerges from Local Homeostasis in an Unsupervised Reservoir Network, February 2023.
  * [94] Stephanie Nelli, Lukas Braun, Tsvetomira Dumbalska, Andrew Saxe, and Christopher Summerfield.  Neural knowledge assembly in humans and neural networks.  Neuron, 0(0), March 2023.  Publisher: Elsevier.
  * [95] Sindy Löwe, Peter O’Connor, and Bastiaan S. Veeling.  Putting An End to End-to-End: Gradient-Isolated Learning of Representations, January 2020.  arXiv:1905.11786 [cs, stat].
  * [96] Mengye Ren, Simon Kornblith, Renjie Liao, and Geoffrey Hinton.  Scaling Forward Gradient With Local Losses, March 2023.  arXiv:2210.03310 [cs].
  * [97] Geoffrey Hinton.  The Forward-Forward Algorithm: Some Preliminary Investigations, December 2022.  arXiv:2212.13345 [cs].
  * [98] Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick.  Neuroscience-Inspired Artificial Intelligence.  Neuron, 95(2):245–258, July 2017.
  * [99] Anthony Zador, Sean Escola, Blake Richards, Bence Ölveczky, Yoshua Bengio, Kwabena Boahen, Matthew Botvinick, Dmitri Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad Koerding, Alexei Koulakov, Yann LeCun, Timothy Lillicrap, Adam Marblestone, Bruno Olshausen, Alexandre Pouget, Cristina Savin, Terrence Sejnowski, Eero Simoncelli, Sara Solla, David Sussillo, Andreas S. Tolias, and Doris Tsao.  Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution, February 2023.  arXiv:2210.08340 [cs, q-bio].