Boris Gutkin

Boris Gutkin
Ecole Normale Supérieure de Paris and Higher School of Economics University Moscow

Doctor of Philosophy

About

273
Publications
43,242
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
4,987
Citations
Introduction
My work is on different themes in computational and mathematical neuroscience, the underlying principle guiding my research is to use the tools of mathematical and computational analysis to build mechanistic links across scales of neuronal organization: from the dynamics of brain cells, circuits and networks to cognitive function and to bring a coherent synthesis to the multitude of collected, and at times disparate, data.
Additional affiliations
September 2013 - present
National Research University Higher School of Economics
Position
  • Researcher
January 2009 - present
Ecole Normale Supérieure de Paris
Position
  • Research Director

Publications

Publications (273)
Preprint
Full-text available
Phase reduction is an important tool for studying coupled and driven oscillators. The question of how to generalize phase reduction to stochastic oscillators remains actively debated. In this work, we propose a method to derive a self-contained stochastic phase equation of the form dϕ = a(ϕ) dt + sqrt(2D(ϕ)) dW (t) that is valid not only for noise-...
Article
Full-text available
Theory predicts that nonlinear summation of synaptic potentials within dendrites allows neurons to perform linearly non-separable computations (LNSCs). Using Boolean analysis approaches, we predicted that both supralinear and sublinear synaptic summation could allow single neurons to implement a type of LNSC, the feature binding problem (FBP), whic...
Article
We are pleased to announce that the presentations and posters of the Annual Computational Neuroscience Meeting (CNS*2023) have become available. Discover the detailed program on the official website https://cns2023.sched.com ... Join us at Annual Computational Neuroscience Meeting.
Preprint
Full-text available
Unraveling how humans effortlessly grasp speech despite diverse environmental challenges has long intrigued researchers in systems and cognitive neuroscience. The interplay between semantic and phonological language structures has been a subject of debate in the linguistics and neurolinguistics literature that, so far, has not been resolved. We see...
Article
We consider the problem of local correlations in the kicked, dual-unitary coupled maps on D-dimensional lattices. We demonstrate that for D≥2, fully dual-unitary systems exhibit ultralocal correlations: The correlations between any pair of operators with a local support vanish in a finite number of time steps. In addition, for D=2, we consider the...
Preprint
Full-text available
Neurological pathologies as e.g. Alzheimer’s Disease or Multiple Sclerosis are often associated to neurodegenerative processes affecting the strength and the transmission speed of long-range inter-regional fiber tracts. Such degradation of Structural Connectivity impacts on large-scale brain dynamics and the associated Functional Connectivity, even...
Article
Full-text available
Recent advances in the field of machine learning have yielded novel research perspectives in behavioural economics and financial markets microstructure studies. In this paper we study the impact of individual trader leaning characteristics on markets using a stock market simulator designed with a multi-agent architecture. Each agent, representing a...
Preprint
Full-text available
Unraveling the mysteries of how humans effortlessly grasp speech amidst diverse environmental challenges has long intrigued researchers in systems and cognitive neuroscience. This study delves into the neural intricacies underpinning robust speech comprehension, giving a computational mechanistic proof for the hypothesis proposing a pivotal role fo...
Article
Full-text available
The neurobiological nature of semantic knowledge, i.e., the encoding and storage of conceptual information in the human brain, remains a poorly understood and hotly debated subject. Clinical data on semantic deficits and neuroimaging evidence from healthy individuals have suggested multiple cortical regions to be involved in the processing of meani...
Article
Many systems in physics, chemistry, and biology exhibit oscillations with a pronounced random component. Such stochastic oscillations can emerge via different mechanisms, for example, linear dynamics of a stable focus with fluctuations, limit-cycle systems perturbed by noise, or excitable systems in which random inputs lead to a train of pulses. De...
Preprint
Full-text available
Theory predicts that nonlinear summation of synaptic potentials within dendrites allows neurons to perform linearly non-separable computations (LNSCs). Using Boolean analysis approaches, we predicted that both supralinear and sublinear synaptic summation could allow single neurons to implement a type of LNSC, the feature binding problem (FBP), whic...
Preprint
Full-text available
Exogenous stimulation is a promising tool for investigating and altering cognitive processes in the brain, with potential clinical applications. Following experimental observations, we hypothesise that the effect of stimulation crucially depends on the endogenous dynamics of the brain. Our study explores how local and global dynamical properties, l...
Preprint
Full-text available
Many systems in physics, chemistry and biology exhibit oscillations with a pronounced random component. Such stochastic oscillations can emerge via different mechanisms, for example linear dynamics of a stable focus with fluctuations, limit-cycle systems perturbed by noise, or excitable systems in which random inputs lead to a train of pulses. Desp...
Article
Full-text available
Seeking and consuming nutrients is essential to survival and the maintenance of life. Dynamic and volatile environments require that animals learn complex behavioral strategies to obtain the necessary nutritive substances. While this has been classically viewed in terms of homeostatic regulation, recent theoretical work proposed that such strategie...
Preprint
Full-text available
Recent technological developments have changed the fundamental ways stock markets function, bringing regulatory instances to assess the benefits of these developments. In parallel, the ongoing machine learning revolution and its multiple applications to trading can now be used to design a next generation of financial models, and thereby explore the...
Preprint
Full-text available
Seeking and consuming nutrients is essential to survival and maintenance of life. Dynamic and volatile environments require that animals learn complex behavioral strategies to obtain the necessary nutritive substances. While this has been classically viewed in terms of homeostatic regulation, where complex nutrient seeking behaviors are triggered b...
Article
Full-text available
Modern well-performing approaches to neural decoding are based on machine learning models such as decision tree ensembles and deep neural networks. The wide range of algorithms that can be utilized to learn from neural spike trains, which are essentially time-series data, results in the need for diverse and challenging benchmarks for neural decodin...
Article
Full-text available
We study quantum and classical correlations between local observables in perturbed coupled cat map model. In spite of fully chaotic dynamics, local correlations can be calculated explicitly due to the presence of spatio-temporal symmetry. This symmetry restricts correlations to the "light rays" because the causality applies both in time and in spac...
Article
Full-text available
Formation of synchronous activity patterns is an essential property of neuronal networks that has been of central interest to synchronization theory. Chimera states, where both synchronous and asynchronous activities of neurons co-exist in a single network, are particularly poignant examples of such patterns, whose dynamics and multistability may u...
Article
Full-text available
The CA1 pyramidal neurons are embedded in an intricate local circuitry that contains a variety of interneurons. The roles these interneurons play in the regulation of the excitatory synaptic plasticity remains largely understudied. Recent experiments showed that recurring cholinergic activation of α7 nACh receptors expressed in oriens-lacunosum-mol...
Article
Full-text available
Brain rhythms emerge from synchronization among interconnected spiking neurons. Key properties of such rhythms can be gleaned from the phase-resetting curve (PRC). Inferring the PRC and developing a systematic phase reduction theory for large-scale brain rhythms remains an outstanding challenge. Here we present a theoretical framework and methodolo...
Preprint
Full-text available
Collective dynamics of spiking networks of neurons has been of central interest to both computation neuroscience and network science. Over the past years a new generation of neural population models based on exact reductions (ER) of spiking networks have been developed. However, most of these efforts have been limited to networks of neurons with si...
Preprint
Despite being aware of negative consequences and wanting to quit, long-term addicts find it difficult to quit seeking and consuming drugs. This inconsistency between the (often compulsive) behavioural patterns and the explicit knowledge of negative consequences represents a cognitive conflict which is a central characteristic of addiction. Neurobio...
Article
Full-text available
In the past, the bottom-up study of financial stock markets relied on first-generation multi-agent systems (MAS) , which employed zero-intelligence agents and often required the additional implementation of so-called noise traders to emulate price formation processes. Nowadays, thanks to the tools developed in cognitive science and machine learning...
Article
Full-text available
In weakly coupled neural oscillator networks describing brain dynamics, the coupling delay is often distributed. We present a theoretical framework to calculate the phase response curve of distributed-delay induced limit cycles with infinite-dimensional phase space. Extending previous works, in which non-delayed or discrete-delay systems were inves...
Preprint
Full-text available
Inhibition of irrelevant information is a crucial process within the working memory (WM) system. In the present study, we explored how inhibition of distractors is learned within a Sternberg-like WM task and how this learning process affects readout performance. We introduced distractors of varying strength as well as novel stimuli to a classic Ste...
Preprint
Full-text available
The nature of semantic knowledge – conceptual information stored in the brain – is highly debated in the field of cognitive science. Experimental and clinical data specify various cortical regions involved in the processing of meaning. Those include semantic hubs that take part in semantic processing in general as well as sensorimotor areas that pr...
Conference Paper
Full-text available
Foreword from the editors. We hosted four keynote speakers: Wolf Singer, Bill Bialek, Danielle Bassett, and Sonja Gruen. They enlightened us about computations in the cerebral cortex, the reduction of high-dimensional data, the emerging field of computational psychiatry, and the significance of spike patterns in motor cortex. From the submissions,...
Chapter
The human brain demonstrates electrical oscillations of various frequency ranges that are associated with a number of cognitive tasks. Here we will focus on the so-called weak (clustered) gamma rhythm (20–80 Hz). Typically, in the cortex, gamma oscillations appear in neuronal networks consisting of excitatory pyramidal cells and inhibitory interneu...
Article
Full-text available
We propose a robust universal approach to identify multiple network dynamical states, including stationary and travelling chimera states based on an adaptive coherence measure. Our approach allows automatic disambiguation of synchronized clusters, travelling waves, chimera states, and asynchronous regimes. In addition, our method can determine the...
Article
Full-text available
Value-based decision making in complex environments, such as those with uncertain and volatile mapping of reward probabilities onto options, may engender computational strategies that are not necessarily optimal in terms of normative frameworks but may ensure effective learning and behavioral flexibility in conditions of limited neural computationa...
Preprint
Full-text available
Homeostasis is a prevalent process by which living beings maintain their internal milieu around optimal levels. Multiple lines of evidence suggest that living beings learn to act to predicatively ensure homeostasis (allostasis). A classical theory for such regulation is drive reduction, where a function of the difference between the current and the...
Article
Full-text available
Nicotinic acetylcholine receptors (nAChRs) modulate the cholinergic drive to a hierarchy of inhibitory neurons in the superficial layers of the PFC, critical to cognitive processes. It has been shown that genetic deletions of the various types of nAChRs impact the properties of ultra-slow transitions between high and low PFC activity states in mice...
Article
Full-text available
Cortical networks show a large heterogeneity of neuronal properties. However, traditional coding models have focused on homogeneous populations of excitatory and inhibitory neurons. Here, we analytically derive a class of recurrent networks of spiking neurons that close to optimally track a continuously varying input online, based on two assumption...
Preprint
Full-text available
According to mechanistic theories of working memory (WM), information is retained as persistent spiking activity of cortical neural networks. Yet, how this activity is related to changes in the oscillatory profile observed during WM tasks remains an open issue. We explore joint effects of input gamma-band oscillations and noise on the dynamics of s...
Article
Full-text available
According to mechanistic theories of working memory (WM), information is retained as stimulus-dependent persistent spiking activity of cortical neural networks. Yet, how this activity is related to changes in the oscillatory profile observed during WM tasks remains a largely open issue. We explore joint effects of input gamma-band oscillations and...
Preprint
Full-text available
We propose a robust universal approach to identify multiple dynamical states, including stationary and travelling chimera states based on an adaptive coherence measure. Our approach allows automatic disambiguation of synchronized clusters, travelling waves, chimera states, and asynchronous regimes. In addition, our method can determine the number o...
Preprint
Full-text available
Brain rhythms emerge as a result of synchronization among interconnected spiking neurons. Key properties of such rhythms can be gleaned from the phase-resetting curve (PRC). Inferring the macroscopic PRC and developing a systematic phase reduction theory for emerging rhythms remains an outstanding theoretical challenge. Here we present a practical...
Article
Full-text available
Quantitative finance has had a long tradition of a bottom-up approach to complex systems inference via multi-agent systems (MAS). These statistical tools are based on modelling agents trading via a centralised order book, in order to emulate complex and diverse market phenomena. These past financial models have all relied on so-called zero-intellig...
Chapter
Full-text available
The history of research in finance and economics has been widely impacted by the field of Agent-based Computational Economics (ACE). While at the same time being popular among natural science researchers for its proximity to the successful methods of physics and chemistry for example, the field of ACE has also received critics by a part of the soci...
Preprint
Full-text available
Working memory (WM) is the brain’s ability to retain information that is not directly available from the sensory systems. WM retention is accompanied by sustained firing rate modulation and changes of the large-scale oscillatory profile. Among other changes, beta-band activity elevates in task-related regions, presumably stabilizing WM retention. A...
Preprint
Full-text available
Hippocampal synaptic plasticity, particularly in the Schaffer collateral (SC) to CA1 pyramidal excitatory transmission, is considered as the cellular mechanism underlying learning. The CA1 pyramidal neurons are embedded in an intricate local circuitry that contains a variety of interneurons. The roles these interneurons play in the regulation of th...
Preprint
Full-text available
In games of incomplete information individual players make decisions facing a combination of structural uncertainty about the underlying parameters of the environment, and strategic uncertainty about the actions undertaken by their partners. How well are human actors able to cope with these uncertainties, and what models best describe their learnin...
Article
Full-text available
The addictive component of tobacco, nicotine, acts via nicotinic acetylcholine receptors (nAChRs). The β2 subunit-containing nAChRs (β2-nAChRs) play a crucial role in the rewarding properties of nicotine and are particularly densely expressed in the mesolimbic dopamine (DA) system. Specifically, nAChRs directly and indirectly affect DA neurons in t...
Article
Full-text available
Muscarinic acetylcholine receptors (mAChRs) are critically involved in hippocampal theta generation, but much less is known about the role of nicotinic AChRs (nAChRs). Here we provide evidence that α7 nAChRs expressed on interneurons, particularly those in oriens lacunosum moleculare (OLM), also regulate hippocampal theta generation. Local hippocam...
Article
Full-text available
Information storage and processing in the brain largely relies on the neural population coding principle. In this framework, information is reflected in the population firing rate that reflects asynchronous irregular spiking of its constituent neurons. Periodic modulations of neural activity can lead to neural activity oscillations. Data indicate t...
Article
Gamma rhythm (20–100 Hz) plays a key role in numerous cognitive tasks: working memory, sensory processing and in routing of information across neural circuits. In comparison with lower frequency oscillations in the brain, gamma-rhythm associated firing of the individual neurons is sparse and the activity is locally distributed in the cortex. Such “...
Article
Nervous system maturation occurs on multiple levels -- synaptic, circuit, and network -- at divergent time scales. For example, many synaptic properties mature gradually, while emergent network dynamics can change abruptly. Here, we combine experimental and theoretical approaches to investigate a sudden transition in spontaneous and sensory evoked...
Article
Full-text available
Gamma rhythm plays a key role in a number of cognitive tasks: working memory, sensory processing and routing of information across neural circuits. In comparison with other (lower frequency) oscillations it is sparser and heterogeneous in space. One way to model such properties of gamma rhythm is to describe it through a neural network consisting o...
Preprint
Full-text available
Cortical networks show a large heterogeneity of neuronal properties. However, traditional coding models have focused on homogeneous populations of excitatory and inhibitory neurons. Here, we analytically derive a class of recurrent networks of spiking neurons that close to optimally track a continuously varying input online, based on two assumption...
Preprint
In the past, financial stock markets have been studied with previous generations of multi-agent systems (MAS) that relied on zero-intelligence agents, and often the necessity to implement so-called noise traders to sub-optimally emulate price formation processes. However recent advances in the fields of neuroscience and machine learning have overal...
Preprint
Recent advances in the fields of machine learning and neurofinance have yielded new exciting research perspectives in practical inference of behavioural economy in financial markets and microstructure study. We here present the latest results from a recently published stock market simulator built around a multi-agent system architecture, in which e...
Preprint
Full-text available
Quantitative finance has had a long tradition of a bottom-up approach to complex systems inference via multi-agent systems (MAS). These statistical tools are based on modelling agents, which trade via a centralised order book to emulate complex and diverse market phenomena. Nevertheless, the issue of agent learning in MAS, which is crucial to price...
Article
Full-text available
Stochastic Resonance (SR) is a well-known noise-induced phenomenon widely reported in dynamical systems with a threshold, while Inverse Stochastic Resonance (ISR) is an opposing phenomenon observed in the dynamical systems which exhibit bistability between a stable node and a stable limit cycle. This study shows a co-occurrence of SR and ISR, in a...
Article
Homeostasis is a problem for all living agents. It entails predictively regulating internal states within the bounds compatible with survival in order to maximise fitness. This can be achieved physiologically, through complex hierarchies of autonomic regulation, but it must also be achieved via behavioural control, both reactive and proactive. Here...
Preprint
Full-text available
Nervous system maturation occurs on multiple levels, synaptic, circuit, and network, at divergent time scales. For example, many synaptic properties mature gradually, while emergent network dynamics, as data show, change abruptly. Here, we combine experimental and theoretical approaches to investigate a sudden transition in spontaneous thalamocorti...
Article
Full-text available
Competition for resources is a fundamental characteristic of evolution. Auctions have been widely used to model competition of individuals for resources, and bidding behavior plays a major role in social competition. Yet how humans learn to bid efficiently remains an open question. We used model‐based neuroimaging to investigate the neural mechanis...