address this need, by measuring spiking activity of neural populations in
vlPFC with multi-electrode recording techniques.

##### Material and Methods

In this study, we investigate the neural correlate of visual awareness in
mesoscopic scale. Recording procedure is similar to awake experiments of Paper
6@ explained earlier (see “Material and Methods”of Chapter 10). The core
behavioral paradigm used in this study was a passive ambiguous stimulation,
and consist of two tasks, Binocular Rivalry (BR) and Physical Alternation
(PA). Both tasks consist of fixation period similar to fixation task explained
earlier in Chapter 10, and followed by presentation of 1 or 2 seconds upward
or downward moving gratings (presented only to one eye – half of the trials
for each eye). After the phase of stimulus presentation, in PA trials, the
first stimulus was removed and a moving grating in the contralateral eye was
presented in the opposite direction. BR trials had the identical structure of
the stimulus presentation, but with the difference that, the second stimulus
was presented without removing the first stimulus. In BR trials that two
opposite moving grating were presented simultaneously, the perception of the
monkey spontaneously switches between the stimulus (i. e. upward and downward
grating) across the the entire length of trial (8-10 seconds). Whereas, in PA
trials, there are no perceptual switches, but perception of the animal changes
by the alternation of the presented stimuli (upward and downward grating).
Parameters of the visual stimulus (moving gratings) are identical to the
experiment explained in Chapter 10. Furthermore, Optokinetic Nystagmus (OKN)
reflexes 888 OKN reflexes are characteristic patterns of eye movements in
response to moving stimuli, that consist of smooth pursuit and fast saccadic
eye movements. has been used to determine the perception of the animal.

In addition to the main experiment that consist of BR and PA tasks, we
additionally have a control experiment for controlling eye movement as a
confounding factor. Given that determining the animal perception is based on
eye movements (OKN reflexes), to rule out the eye movement as a confounding
factor, we perform a passive fixation experiment similar to the awake
experiment of Paper 6@ explained earlier (see “Material and Methods”of Chapter
10), but without eye movement. In this experiment, the eye movement during
presentation of moving grating were suppressed by instructing the animal to
maintain the fixation during the task (by overlaying a fixation point with
size of $1$-$2^{\circ}$ on top of the moving grating).

##### Results

Firstly, the perpetual dominance periods detected based on OKN reflexes follow
a gamma distribution which is compatible with previous studies [296]. This
indicates that using no-report paradigms of BR lead to compatible results with
human studies. Given the availability of neurons [recorded by Utah array] that
respond to direction of motion of moving grating stimuli in PFC (see Paper
6@), we can quantify the proportion of perceptual modulation of neurons in our
experiment that use upward and downward moving gratings as rivaling patterns.
Interestingly, compatible with previous studies that used different tasks and
visual stimuli [194], majority of sensory modulated units were also
perceptually modulated. Moreover, in the population level, the content of
conscious perception of the animals was decodable from spiking activity of
neural populations in vlPFC. Lastly, the decoding algorithm that we used for
decoding the content of the perception [297], could also reliably decode the
content of the presented visual stimulus (in the passive fixation experiment)
both in presence and absence of eye movement i. e. training the decoder with
responses in presence of eye movement, and test when the eye movement are
suppressed (fixation-on task) and vice versa. Therefore, our control analysis
suggest that eye movements are not a confounding factor for our perceptual
modulation.

##### Conclusion

In this study, we showed that activity of the majority of sensory modulated
neurons of vlPFC is correlated with conscious perception in a no-report
binocular rivalry task, and the content of conscious experience is decodable
from mesoscopic dynamics of PFC. Moreover, this study has an important
implication for the neural correlate of visual awareness. This study adds
another piece of evidence for the involvement of PFC in conscious perception
which has been an important debate in the field of consciousness research in
the last few years (also see Paper 5@).

###  Chapter 12 Paper 8@

#### Paper information

Title:

Prefrontal state fluctuations gate access to consciousness

Authors:

Abhilash Dwarakanath∗, Vishal Kapoor∗, Joachim Werner, Shervin Safavi Leonid
A. Fedorov, Nikos K. Logothetis, Theofanis I. Panagiotaropoulos (∗ indicate
equal contributions)

Status:

Preprint is available online, see dwarakanathBistabilityPrefrontalStates2023
[224]

Presentation at scientific meetings:

FFRM 2015 [288], SfN 2018 [289], AREADNE 2018 [298]

Author contributions:

Conceptualisation: A.D., V.K., T.I.P. (lead), N.K.L.; Data curation: A.D.
(lead), V.K. and J.W.; Formal analysis: A.D. (lead), V.K., J.W., L.A.F.;
Funding acquisition: N.K.L.; Investigation: A.D. (equal), V.K. (equal), T.I.P.
(supporting); Methodology: A.D. (equal), V.K. (equal), J.W. & S.S.
(supporting), T.I.P. (equal); Project administration: T.I.P.; Resources: J.W.,
N.K.L. (lead); Software: A.D. (lead), V.K., J.W., L.A.F. & S.S. (supporting);
Supervision: T.I.P.; Visualisation: A.D. (lead), T.I.P. (supporting); Writing
– original draft: A.D., T.I.P. (lead); Writing – review & editing: A.D., V.K.,
L.A.F., S.S., T.I.P. (lead), N.K.L.

#### Summary

##### Motivation

In Section 11 we elaborated on the motivations for studying the phenomenon of
binocular rivalry (BR) in a mesoscopic scale and in Paper 7@ we showed that
content of conscious experience is decodable from mesoscopic dynamics of PFC.
This was the first confirmation on the usefulness of the meso-scale
observation. This allows us to go one step further in studying the mesoscopic
dynamics of PFC. One of the most important markers of coordination in
mesoscopic dynamics of the brain, is neural oscillations [19, 299]. In this
study we investigate oscillatory dynamics in ventro-lateral PFC (vlPFC) and
its connection to conscious visual perception.

##### Material and Methods

Most of the experimental details for this study was explained in summaries of
the other papers (Paper 6@, and Paper 7@). Recording procedure is similar to
awake experiment of Paper 6@ explained earlier (see “Material and Methods”of
Chapter 10). The behavioral paradigm used in this study is also explained
earlier (see “Material and Methods”of Chapter 11). In this study, Continuous
Wavelet Transform (CWT) [300] has been used to extract spectral content of
LFPs and Chronux toolbox [301] for quantifying spike-LFP coupling by computing
Spike-Field-Coherence (SFC).

##### Results

This study reveals various characteristic oscillatory activities which are
happening in the vicinity of the perceptual switches detected based on
Optokinetic Nystagmus (OKN) reflexes. The frequency of these transient
oscillatory activities are covering low and intermediate ranges (namely 1-9 Hz
and 20-40 Hz). In addition to presence of these coordinated dynamics in the
mesoscopic activity of PFC neural populations and their relationship to
perceptual events, the statistics and spatio-temporal patterns of some of
these transitory events lend support to important frameworks of studying the
consciousness.

##### Conclusion

This study adds another piece of evidence for the involvement of PFC in
conscious perception, in addition to the one discussed earlier in Paper<EMAIL_ADDRESS>In
particular, it reveals signatures of neural coordination reflected in the
oscillatory dynamics (see Section 6.2) of neural populations involved in
conscious visual perception. Revealing these signatures could not be possible
without investigating the system in meso-scale (see more elaborating in
Section 11). Lastly similar to Paper 7@, this study has an important
implication for the neural correlate of visual awareness. This study
highlights the involvement of PFC in conscious perception which has been an
important debate in the field of consciousness research in the last few years
(also see Paper 5@).

This part is dedicated to a subjective perspective on how the research line of
this thesis can or should be extended. In this thesis, we sought for
_principled_ ways of approaching the brain. Although this thesis touched on
various such aspects, but I believe it misses an important aspect of the brain
which is its _adaptivity_. In the end, brain, presumably the most “complex
system”, needs to survive in the environment. Indeed, in the field of _complex
adaptive systems_ , the endeavor is understanding very similar questions in
the nature. Inspired by some ideas discussed in the field of complex adaptive
systems, we suggest a set of new research directions that intend to
incorporate the adaptivity aspect of the brain as one of the principles. Of
course, these research directions, remain close to the neuroscience side,
similar to the intention of the research presented in previous parts.

##  Part III Outlook

###  Chapter 13 Brain as a complex _&_ adaptive system

In Chapter 1, we argue that brain can be approached as a complex system.
Certainly, this is a valuable perspective toward the brain and was the pivotal
idea of this thesis. Nevertheless, an important aspect of the brain, as a
biological information processing system, is not taken into account in the
approach we followed and discussed in this thesis. This important aspect is
_adaptivity_ of humans/animals. They need to be _adaptive_ in order to
survive. That being said, perhaps we should consider humans/animals as
_adaptive agents_ and the brains as a complex _and_ adaptive system. Indeed,
Complex Adaptive Systems (CAS) have been an independent field of research (see
hollandStudyingComplexAdaptive2006 (302) for a brief review).

††margin:  Approaching the brain as a complex and adaptive system

Inspired by general properties and mechanisms introduced for CAS (that are
briefly discussed in Section 14), again, new questions can be asked in various
domains of neuroscience, and moreover, even old questions can be revisited
based on this perspective. In this chapter, we introduce a set of new research
directions that we believe are complementary to the ideas that motivated and
shaped this thesis.

Conceiving the brain as a CAS implies that certain computations are needed to
satisfy the adaptivity of the agent (see Section 15 for further elaboration).
Moreover, as we discussed earlier (see Chapter 1), conceiving the brain as a
complex system has implications on the dynamics of the brain. More generally,
on one hand, behavior is a rich source for seeking and understanding the
computational objectives (pertaining to adaptivity of humans and animals)
††margin:  Through behavior we can understand computation needed to be
adaptive and through multi-scale dynamics of the brain we can understand the
brain’s biophysical machinery On the other hand, multi-scale dynamics of the
brain, as briefly discussed in Chapter 2, is a rich source for understanding
the biophysical machinery of this adaptive agent implementing the computation.
For instance, concerning the adaptivity of the humans and animals, focusing on
behavior have led us to various developments in ecological psychology [303],
reinforcement learning [304], and even understanding the emotion [305] that
all inform us about the brain computations [306]. Concerning the multi-scale
dynamics, studying the brain across scales, has helped us to understand the
emergent properties of this biophysical machinery (for further elaboration,
see pesensonMultiscaleAnalysisNonlinear2013 [307, Chapter 1] and
siettosMultiscaleModelingBrain2016 [308]).

From a broader perspective, particularly in terms of Marr’s levels of
understating [309], it can be argued that, understanding the brain dynamics,
brings us closer to the implementation level and perhaps to some degree to the
algorithmic level; and understating the behavior brings us closer to
understanding the computation and more explicitly the algorithm. With no
doubt, both of these aspects are utterly important for understating the brain.
††margin:  An integrative understating of the brain need a bridge Therefore,
it is import to establish a connection between these two, in order gain an
_integrative_ understating of the brain (see churchlandComputationalBrain1992
[8, Chapter 2, Section 2] for a broad perspective on the importance of this
bridge and stephanTranslationalPerspectivesComputational2015 [310] and
forstmannIntroductionModelbasedCognitive2015 [311, Chapter 8] for showcases of
their importance in translational neuroscience). Motivated by the importance
of establishing this bridge, in Section 16 we outline various approaches we
can take for relating behavior to multi-scales brain dynamics.

####  14 Complex adaptive systems

Complex adaptive systems (CAS) can be broadly defined as a system composed of
multiple elements, called agents, _"that learn or adapt in response to other
agents"_ [4, Chapter 3]. CAS have been studied for decades (see
morowitzMindBrainComplex1995 [312] for historical note), and there have been
efforts to explain the behavior of various natural and artificial systems
based on the CAS formalism; They include adaptive behavior of the immune
system [313], finical market [4] and even language [314].

Different sets of properties and mechanisms which are considered to be common
between different CAS have been suggested [315]. We outline the 4 features
proposed by hollandStudyingComplexAdaptive2006 [302]. Although, some of the
core ideas are common among most of the other proposals and indeed those
commonalities are the foundations for ideas presented in the following, but
readers are also encouraged to refer to properties and mechanism proposed by
others as well (for example see gell-mannComplexAdaptiveSystems1994 [316] and
arthurEconomyEvolvingComplex1997 [317, Chapter 1]).

hollandStudyingComplexAdaptive2006 [302] introduces 4 major features or
characteristics that CAS have in common in spite of their substantial
differences:

  1. 1.

Parallelism: Complex systems (also briefly discussed in Chapter 1) are
constructed with many _intently interacting_ components. Due to the need for
tight coordination, simultaneous communications between components of the
system are inevitable.

  2. 2.

Conditional actions: In CAS, agents need to act conditionally as the required
action is defined by the agent’s internal state (condition) and actions of
external agents.

  3. 3.

Modules and hierarchies: CAS are often organized in a modular and hierarchical
fashion (for the latter see [4, Chapter 7] and [318, Chapter 8]).

  4. 4.

Adaptation and evolution: Agents in CAS need to change over time in order to
gain a better performance. Adaptation requires solutions to two important
problems, namely _credit assignment_ and _rule discovery_.

Features or characteristics mentioned in the number two and four of Holland’s
idea are particularly pertaining to _computations_ that CAS need to perform.
Interestingly, some of these computations are already a focus of research in
the field of neuroscience as well (but not necessarily based on a similar
foundation we motivate by CAS ideas). In section Section 15 we briefly discuss
some of these computational objectives that can be closely connected to the
brain.

####  15 Brain computational objectives

As briefly discussed earlier, humans/animals as information processing
systems, are adaptive agents, and need to interact with a complex environment.
We can conceive the brain as a CAS, and based on CAS notions introduced
earlier, we can argue that due to their adaptivity they need to perform
certain computations. Indeed, mitchellComplexityGuidedTour2011 [3, Chapter 12]
argue that,

> "At a very general level, one might say that computation is what a complex
> system does with information in order to succeed or adapt in its
> environment."

To emphasize conceiving the brain as a CAS and the computations it implies, we
highlight some of the computational objectives of the brain that are under
active investigation _and_ and are closely related to general properties of
CAS discussed in Section 14. The need for _conditional actions_ , solving the
_credit assignment_ problem and _discovering rules_ in the environment that
were mentioned in Section 14 as general properties of CAS, are closely related
to _representation_ , _decision making_ and _reinforcement learning_ which are
actively investigated in neuroscience.

One of these computational objectives is efficient representations. The
ability of an agent to act upon actions and states of external agents relies
on _efficient representation_ of information pertaining to external agents.
The other computational objective is credit assignment and rule discovery that
are both premises of reinforcement learning [319].

Certainly, this section, by no means, provides a comprehensive list of
computational objectives of the brain that have been already studied in
neuroscience. Rather, it highlights examples that are closely related to the
ones CAS should have in a general sense. In the next step, we need to find the
connections between these computational objectives and their biophysical
machinery by investigating the relationship between behavior and multi-scale
dynamics of the brain.

####  16 Relating behavior to multi-scale brain dynamics

As argued earlier, behavior is a rich source for understating such
computational objectives in human/animals and multi-scale dynamics is a rich
source for understating the biophysical machinery behind it. This is the
motivation for relating the behavior to multi-scale brain dynamics. In this
section, we introduce potential approaches that we think can relate these two
facets of the brain.

Certainly, establishing this connection is challenging. Therefore, we need to
decompose it into smaller but complementary steps that can be supported by the
existing models and/or empirical evidence. In the next sections (Section 16.1,
Section 16.2, and Section 16.3), we propose various approaches that are more
or less accessible and can potentially bring us a few steps closer to
establishing a bridge between behavior and multi-scale brain dynamics.

#####  16.1 Relating neural dynamics and neural computation

As discussed earlier, neural computation and dynamics are both important
aspects of the brain. There are various frameworks and models in neuroscience
which are either centered around neural computation [252, 253, 254, 143, 255,
181, 256, 182, 257, 258] or neural dynamics [166, 259, 260, 144, 261, 262,
263, 264, 149, 151] but also have some implications for the other one (also
see maassSearchingPrinciplesBrain2016 [262] for a brief review). These models
are not necessarily well connected to _behavior_ and _multi-scale_ dynamics of
the brain, but still can fill some space in this large gap between behavior
and multi-scale. Further investigation in such frameworks and models, that are
outlined in the next sections, can potentially help us to accomplish the
mentioned goal, which is relating behavior to multi-scale brain dynamics.

######  16.1.1 Normative models with implications for neural dynamics

There have been various efforts to relate neural computation to neural
dynamics by introducing normative models of neural computation (e. g. based on
sampling theories, Bayesian inference algorithms) which can explain some
aspects of observed dynamics of the brain such as irregular spiking and neural
oscillations [265, 255, 181, 256, 182, 320, 258, 321]. More generally there
have been efforts to relate the state of the machinery implementing a given
neural computation to a putative dynamical regime of the neural circuits. For
instance, echevesteCorticallikeDynamicsRecurrent2020 [258] and
lengyelMatchingStorageRecall2005 [252] have developed neuronal networks which
implement Bayesian inference that are attractor networks as well. Neural
coding, in particular, is one of the well established computations that brain
needs to accomplish [177] and there have been various efforts to connect
neural coding and neural dynamics [265, 181, 182, 258]. In most of such
normative models, we optimize or train a network of neurons based on a
specific computational objective (such as reconstruction error), and the
features of the neural dynamics appear in the resulting network activity
automatically.

All the features of neural dynamics that have been explained by the previous
normative models are among the important ones and some of them are even
considered computationally relevant (like oscillations [182, 322]).
Nevertheless, the brain dynamics has been shown to be more complex than the
reach of normative models so far [31, 164]. Not only in terms of complexity of
the observed dynamics, but also in terms of scale, particularly large scale
dynamics and multi-scale dynamics [244, 141]. Next steps should include
developing normative models with richer neural dynamics, in particular, the
large scale and multi-scale dynamics.

######  16.1.2 Models of neural dynamics with implications for neural
computation

One of the frameworks for explaining the neural dynamics with connection to
neural computation is the “criticality hypothesis of the brain” (for a review
see [48] – also briefly discussed in Section 7). Certainly, frameworks like
criticality are insightful for brain dynamics [48] in particular because they
provide explanations for observed multi-scale dynamics of the brain [141].

One approach to better connect the criticality hypothesis of the brain to
neural computation could be the one we used in Chapter 3, which is searching
for signatures of criticality in neuronal networks that can be optimized based
functionally relevant computational objectives (in Chapter 3, we used
efficient coding objectives). Of course, this is not necessarily informative
on a mechanistic level, rather is an indication of _potential_ connections.
Presence of signatures of criticality may or may not hint for more mechanistic
approaches. Nevertheless, some clues can guide us toward more formal
investigations. For instance, for the particular case discussed in Chapter 3,
Fisher information can be a candidate quantity that both frameworks –
efficient coding [323] and criticality [324, 325, 326, 327] – use to assess
the closeness to their optimal point.

Another potential approach is seeking for other kinds of functionally relevant
attributes for notions established in criticality hypothesis of the brain. For
instance, it has been suggested that neural avalanches are related to cell
assemblies [328] and indeed the notion of cell assemblies are closely
connected to computations implemented in the brain [329, 330, 331, 332, 333].

#####  16.2 Exploiting models of pivotal tasks

For the purpose expressed in Section 16, we can also exploit behavioral tasks
which have been comprehended from a wide range of perspectives. To the best of
my knowledge, not so many such tasks are identified and exhaustively explored.
Nevertheless, we believe this small number is sufficient to make further
exploration in this direction justified, given the potential insight that we
can get from them. For instance, cavanaghCircuitMechanismIrrationalities2019
[334] studied perceptual decision-making through interventional
experimentation, and multi-scale computational modeling. Indeed, such theory-
experiment hybrid approaches can be insightful, both for understanding the
multi-scale dynamics of the phenomenon (in this case from synapse to behavior)
and also the computations involved in the task (in this case evidence
accumulation process). frankLinkingLevelsComputation2015 [335] and colleagues
also studied the decision making and cognitive control through reinforcement
learning models and biophysical modeling of a single cortico-basal ganglia
circuit and similarly, they could gain an integrative understating of the
involved computation and also biophysical and dynamical characteristics that
have been observed during such tasks. A key in both examples was exploiting
the tasks that have been comprehended from a wide range of perspectives
(normative modeling, biophysical modeling, measuring electrophysiological
activity of involved circuits).

One example of such tasks that has been studied from a wide range of
perspectives and wide range of tools is the _bistable perception_. On one
hand, a large body of computational studies focus on explaining the dynamics
of bistable perception [336, 337, 74, 338, 339]; On the other hand, another
class of computational models which tried to explain the phenomenon with
normative approaches centered around the computation that the brain might need
to perform pertaining to perception [71, 340, 341, 342, 343]. Notably, most of
these studies are centered around Bayesian model of the brain [344, 345].

Next to this extensive computational models (which include both normative and
biophysical models) there is a large body of psychophysical (for review see
[346]), electrophysiological and imaging (for review see [189, 190]),
pharmacological [347, 348], and genetic studies [349, 350, 351, 352].
Particularly, as briefly discussed in Chapter 4, from electrophysiological and
imaging we learn that a distributed network of neurons is involved in the
phenomenon and therefore this is inherently a multi-scale problem.

We believe a wide range of perspectives toward the phenomenon of bistable
perception, that led to this immense range of studies and their resulting
insight, justify bistable perception as one of the ideal tasks to be studied
with the purpose of relating behavior (and their accompanied computation) to
multi-scales brain dynamics [203]. In this thesis, we approach the phenomenon
of binocular rivalry differently from the conventional approaches (see Chapter
4), and our initial results (see Paper 7@ and Paper 8@) justified the
usefulness of our proposed mesoscopic scale observation of the brain during a
binocular rivalry task. Indeed, a meso-scale observation can also be the first
step for understanding the multi-scale dynamics of binocular rivalry. In
Chapter 2 we introduced a set of novel methodologies for cross-scale and
multi-scale analysis of neural data, in particular mesoscopic signals like
LFPs. Transient and cooperative neural activities in hippocampus (such as
sharp wave-ripples) have been studied extensively. As exemplified in Section
6, such characteristic events can co-occur with well-coordinated activity in
smaller scales (scale of neurons and population of neurons), and a larger
scale (whole brain) as well. Therefore, investigating the presence of such
events in the mesoscopic activity of neurons during binocular rivalry
[assuming their existence] and the relationship between these neural events
and behavior can potentially bridge the multi-scale dynamics of the brain and
behavior (which is binocular rivalry in this case).

Indeed, recent electrophysiological studies in the cortex also revealed neural
activities with cooperative and transient nature that are involved in
cognitive functions other than memory consolidation. For instance,
womelsdorfBurstFiringSynchronizes2014 [353] reported burst firing events in
Prefrontal Cortex accompanied with particular large-scale synchronization
patterns and attention switches.

What has been discussed can be a potential road map to bridge the multi-scale
dynamics of the brain and behavior in binocular rivalry, but still the
connection to computation remains elusive. Regarding the computations that
brain presumably needs to perform, as mentioned earlier, there are already
computational models [71, 340, 341, 342, 343]. Some of these models can even
explain many aspects of binocular rivalry psychophysics and some aspects of
neural dynamics [354, Chapter 3]. Certainly, bridging the multi-scale dynamics
and computations explicitly, should be investigated in the next steps.

#####  16.3 A principled framework for data fusion

One of the core components of the proposed goal, _relating behavior to multi-
scale brain dynamics_ , is relating dynamics of the brain across scales even
independent of behavior and computation. Indeed, in Chapter 2, we introduced
novel methodologies for the very same purpose – bridging the scales.
Nevertheless, most of such methodologies (including the ones introduced in
this thesis) are designed for particular choices of data modalities (e. g.
spike-LFP coupling, LFP-BOLD relationship). This implies, for each pair of
modalities, we tend to develop a set of tools accustomed to the nature of that
particular type of data (which is a reasonable choice for the first try). Of
course, such modality-specific methodologies have been insightful and
certainly will be, but having a general framework which is capable of
embedding or allowing the investigation of different datasets in a common
space can potentially bring a wider range of opportunities for investigating
brain dynamics across scales and ultimately relate them to the behavior and
computation.

Indeed, a few frameworks exploiting kernel-based methods [110, 111, 355, 356]
and topological data analysis [357] have been proposed, that are potentially
capable of fusing multi-modal data in a principled fashion. Next steps should
include broad investigation of such frameworks for various modalities
including the ones accessible via invasive recording techniques such as spikes
and extracellular field potentials (as they are less explored compared to non-
invasive ones). In particular, data modalities that can be better represented
by point processes (such as spike trains) are more challenging to be fused
with the other kinds of neural data which are continuous in nature (should be
noted that there have been some efforts in this direction based on kernel-
based methods [358, 359, 360, 361], and for a review see
parkKernelMethodsSpike2013a [362]).

####  17 Understating the neuro-principles through dysfunctions

Understanding the brain dysfunctions, in addition to its humanistic aspects
and potential societal impacts can also be insightful for gaining a
mechanistic understanding of the brain. In particular, understanding cognition
and behavior is one of the most important goals of the brain science, and
among brain dysfunctions, psychiatric disorders are specifically connected to
the malfunctioned cognition and disorders of behavior [363]. A window for
understanding the machinery behind cognitive capabilities and neural
correlates of behavior can happen through the understanding of when and why
they malfunction i. e. _mechanistically_ to understand the syndromes we
observe in psychiatric disorders.

Furthermore, Psychiatry is unique from various other perspectives. Approaches
used for understanding the psychiatric disorders are extremely diverse. In
terms of scales or levels of organization [8, Chapter 1], psychiatric
disorders have been studied from their genetic basis [364, 365, 366] all the
way to their roots in the social interactions [367, 368, 369] In terms of
[Marr] levels of understanding [309], psychiatric disorders have been attacked
in all three levels [370, Chapter 5][363].

The mentioned diversity of approaches goes beyond the conventional research in
the systems neuroscience. As the last example, it is worth mentioning the
research on psychiatric disorders for establishing the connection between the
nervous system and the immune system. Recently, a peculiar connection between
psychiatric disorders (in particular depression and schizophrenia) and
dysfunctions of the immune system has been established [371, 372, 373, 374,
375, 376, 377, 378] and more generally the interaction between the immune
system and the brain has been receiving more attention and support recently
([372, 379, 380, 381, 382, 383, 384, 385, 386]).

Despite this diversity, there are also potential connections and bridges
between them. For instance, in many brain dysfunctions we have clues about
both impaired computation and brain dynamics. Whether there is a connection
between them, it needs to be thoroughly investigated. However, at least the
current state of [Computational] Psychiatry is not clueless about integration
of neural computation and neural dynamics. For instance, [387], based on their
implementation of circular inference, have suggested that pathological
inference attributed in schizophrenia can be mapped into excitation-inhibition
imbalance in the neural circuit implementing the inference.

Overall, we believe, understating the brain dysfunction is an intriguing
window for gaining an integrative understating of the brain function given the
richness and diversity of the empirical data in the field.

lof.0lof.0List of FiguresList of Figureslof.0

###### List of Figures

  1. 1.1 Kuramoto model (animation, need Okular or Adobe Acrobat Reader) These animation demonstrate the dynamic of Kuramoto model consisting of 100 oscillators. Each dot represent an oscillator and the colors code for oscillator’s intrinsic frequency. On the left, the oscillators do not interact with each other as the coupling parameter is set to zero ($\kappa=0$). On the righ, the oscillators do interact with each other as the coupling parameter is non-zero ($\kappa=0.5$).
  2. 1.2 Kuramoto model (snapshots) Snapshots from animations of Figure 1.1. These snapshots (each row, one snapshot) demonstrate the dynamic of Kuramoto model consisting of 100 oscillators. Each dot represent an oscillator and the colors code for oscillator’s intrinsic frequency. On the left, the oscillators do not interact with each other as the coupling parameter is set to zero ($\kappa=0$). On the righ, the oscillators do interact with each other as the coupling parameter is non-zero ($\kappa=0.5$). The first row is a snapshot from the initial condition of the simulation, the second row is a snapshot from an intermediate state of the simulation, and the last row is the last snapshot of this simulation.
  3. 2.1 Schematic depiction of levels of organization Demonstrate extremely variable spatial scales at which anatomical organizations can be identified. Icons to the right represent structures at distinct levels: (top) a subset of visual areas in visual cortex; (middle) a network model of how ganglion cells could be connected to simple cells in visual cortex, and (bottom) a chemical synapse. Figure is adopted from churchlandPerspectivesCognitiveNeuroscience1988 [76] with permission.
  4. 2.2 Spatio-temporal resolution of measurement methods in neuroscience Demonstrate the spatial and temporal resolution of measurement methods being used in neuroscience (up to 2014). Each box depict the spatial (y-axis) and temporal (x-axis) of one measurement method. Open regions represent measurement techniques and filled regions, perturbation techniques. Inset, a cartoon rendition of the methods available in 1988. The regions allocated to each domain are somewhat arbitrary and represent the estimate of sejnowskiPuttingBigData2014 [97]. Abbreviations used in the figure: EEG, electroencephalography; MEG, magnetoencephalography; PET, positron emission tomography; VSD, voltage-sensitive dye; TMS, transcranial magnetic stimulation; 2-DG, 2-deoxyglucose. Figure is adopted from sejnowskiPuttingBigData2014 [97] with permission.

### Acronyms

BOLD

    Blood-Oxygen-Level Dependent
BFS

    Binocular Flash Suppression
CAS

    Complex Adaptive System
CCA

    Canonical Correspondence Analysis
fMRI

    functional Magnetic Resonance Imaging
LFP

    Local Field Potential
LGN

    Lateral Geniculate Nucleues
LIF

    Leaky-Integrate and Fire
LPFC

    Lateral Prefrontal Cortex
vlPFC

    ventro lateral Prefrontal Cortex
PFC

    Prefrontal Cortex
PLV

    Phase Locking Value
MUA

    Multi Unit Activity
NET-fMRI

    Neural-Event-Triggered functional Magnetic Resonance Imaging
NMF

    Non-negative Matrix Factorization
OKN

    Optokinetic Nystagmus
REM

    Rapid-Eye-Movement
RG

    Renormalization Group
STFT

    Short-Term Fourier Transform
SUA

    Single Unit Activity
SFC

    Spike Field Coherence
SNR

    Signal to Noise Ratio
SVD

    Singular Value Decomposition

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This appendix of the full thesis [1] includes the PDF of all the published
papers, preprints and in-preparation manuscripts. They appear as they appeared
in Part II, with the following order:

  1. 1.

safaviUnivariateMultivariateCoupling2021 [229, Neural Computation 2021]

  2. 2.

safaviUncoveringOrganizationNeural2023 [129, PLoS Computational Biology 2023]

  3. 3.

Besserve et al.; preliminary manuscript is available in the appendix, (Paper
3)

  4. 4.

safaviSignaturesCriticalityEfficient2023 [388, BioRxiv 2023]

  5. 5.

safaviFrontalLobeInvolved2014 [210, Front. Psychol. 2014]

  6. 6.

safaviNonmonotonicSpatialStructure2018 [126, PNAS 2018]

  7. 7.

kapoorDecodingInternallyGenerated2022 [195, Nature Communcations 2022]

  8. 8.

dwarakanathBistabilityPrefrontalStates2023 [224, Neuron 2023]

##  Part IV Manuscripts

colophon.0colophon.0ColophonColophoncolophon.0

### Colophon

This document was typeset using the typographical look-and-feel classicthesis
developed by André Miede, and modified by Shervin Safavi for the purpose of
this thesis. The style was inspired by Robert Bringhurst’s seminal book on
typography “ _The Elements of Typographic Style_ ”. classicthesis is available
for both LaTeX and LYX:

http://code.google.com/p/classicthesis/