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CNeuro2024 Faculty
Sukbin Lim
Ruben Moreno Bote
Dmitri (Mitya) Chklovskii

Dmitri (Mitya) Chklovskii

Flatiron Institute at Simons Foundation in New York, USA

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Dmitri Chklovskii received his Ph.D. degree in theoretical physics from the Massachusetts Institute of Technology, Cambridge. From 1994 to 1997, he was a junior fellow at the Harvard Society of Fellows. He transitioned to neuroscience at the Salk Institute for Biological Studies, San Diego, California. From 1999 to 2007, he was an assistant/associate professor at Cold Spring Harbor Laboratory, New York. Then, as a group leader at Janelia Research Campus, Ashburn, Virginia, he led the team that assembled the largest-at-the-time connectome. He is a group leader for neuroscience at the Flatiron Institute, New York, and a research associate professor at New York University Medical Center. Informed by the function and structure of the brain, his group develops online-learning algorithms for big data.

Research Interests: ​Computational Biology, Imaging, Systems, Cognitive, & Computational Neuroscience.

The goal of Mitya Chklovskii’s research is to reverse engineer the brain on the algorithmic level. Informed by anatomical and physiological neuroscience data, his group develops algorithms that model brain computation and solve machine learning tasks.

CNeuro2024 - Lecture Topics:


 
Basic Lecture: The Normative Approach to Understanding Neural Computation

Advanced Lecture: What Can Connectomes Tell Us about Neural Computation?

Nathaniel Daw

Nathaniel Daw

Nathaniel Daw is a Professor of Neural Science and Psychology at Princeton University. He received his Ph.D. in computer science from Carnegie Mellon University and at the Center for the Neural Basis of Cognition, before conducting postdoctoral research at the Gatsby Computational Neuroscience Unit at UCL. His research concerns computational approaches to reinforcement learning and decision making, and particularly the application of computational models in the laboratory, to the design of experiments and the analysis of behavioral and neural data. He is the recipient of a McKnight Scholar Award, a NARSAD Young Investigator Award, a Scholar Award in Understanding Human Cognition from the MacDonnell Foundation, and the Young Investigator Award from the Society for Neuroeconomics.

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Research Interests: Computational Models, Neuroscientific Experiments, Decision-Making Systems, Learning and Neuromodulation.

I study how people and animals learn from trial and error (and from rewards and punishments) to make decisions, combining computational, economic, neural, and behavioral perspectives. I focus on understanding how subjects cope with computationally demanding decision situations, notably choice under uncertainty and in tasks (such as mazes or chess) requiring many decisions to be made sequentially. In engineering, these are the key problems motivating reinforcement learning and Bayesian decision theory. I am particularly interested in using these computational frameworks as a basis for analyzing and understanding biological decision making. Some ongoing projects include:

Computational models in neuroscientific experiments: Computational models (such as reinforcement learning algorithms) are more than cartoons: they can provide detailed trial-by-trial hypotheses about how subjects might approach tasks such as decision making. By fitting such models to behavioral and neural data, and comparing different candidates, we can understand in detail the processes underlying subjects’ choices. I am interested in developing new techniques for such analyses, and applying them in behavioral and functional imaging experiments to study human decision making.

Interactions between multiple decision-making systems: The idea that the brain contains multiple, separate decision systems is as ubiquitous (in psychology, neuroscience, and even behavioral economics) as it is bizarre. For instance, much evidence points to competition between more cognitive and more automatic processes associated with different brain systems. Such competition has often been implicated in self-control issues such as drug addiction. But (as these examples suggest) having multiple solutions to the problem of making decisions actually compounds the decision problem, by requiring the brain to arbitrate between the systems. We are pursuing this arbitration using a combination of computational and experimental methods.

Learning and neuromodulation: Much evidence has amassed for the idea that the neuromodulator dopamine serves as a teaching signal for reinforcement learning. This relatively good characterization can now provide a foothold for extending in a number of exciting new directions. These include computational (e.g., how can this system balance the need to explore unfamiliar options versus exploit old favorites), behavioral (how is dopaminergically mediated learning manifest; how is it deficient in pathologies such as drug addiction or Parkinson’s disease), and neural (what is the contribution of systems that interact with dopamine, such as serotonin and the prefrontal cortex).

CNeuro2024 - Lecture Topic:

Basic Lecture: Reinforcement Learning: Basic Algorithms, Brain, and Behaviour

Advanced Lecture:
Advanced Reinforcement Learning: Replay, Temporal Abstraction, and Function Approximation

Pulin Gong

Pulin Gong

University of Sydney, Australia

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Dr. Pulin Gong is an Associate Professor at the Faculty of Science at the University of Sydney, Australia. 

His research work focuses on better understanding the self-organizing mechanisms of spatiotemporal dynamics of neural circuits and the principles underlying how these dynamics implement neural computation, as he further explains below.

Research Interests: Cognition, Visual Perception, Decision-Making, Neuroscience, Neuronal Networks, Behavior.

Distributed dynamic computation: We have proposed that propagating neural waves and their interactions enable neural systems to carry out distributed dynamic computation (DDC). We work on using DDC to understand specific perceptual and cognitive functions such as visual feature integration, associative learning and memory.

Complex neuronal dynamics: Cortical neurons in vivo fire very irregularly. Understanding the origin of such irregularity is of fundamental importance to unravel neural coding principles. We work on developing a unified theoretical account of irregular neural dynamics, including the variability of spike timing, non-Gaussian fluctuations of membrane potential.

Dynamic spatiotemporal patterns: Recently, we have successfully developed a method that is effective in detecting and characterizing coherent spatiotemporal patterns from large-scale data such as MEA recorded spikes and local field potentials. Based on this method, we have found a richer than expected repertoire of coherent structures; beside the plane waves that have been a subject of recent interests, we have found standing waves that are complemented by waves that radiate out or converge to phase singularities, or spiral around them (spiral waves). We investigate the functional nature of these patterns by combining experimental and modeling studies.

CNeuro2024 - Lecture Topics:


 
Basic Lecture: Spatiotemporal Activity Patterns in the Brain: Dynamical Properties and Functional Roles

Advanced Lecture: Fractional Neural Sampling (FNS) 

Vincent Hakim

Vincent Hakim

Vincent Hakim is a French theoretical physicist (biophysics, neuroscience, statistical and nonlinear physics).. He received his doctorate from University Paris-Sud in 1985. Currently, he is a Centre national de la recherche scientifique (CNRS) research director at the Laboratoire de Physique de l'Ecole Normale (LPENS).

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Hakim's work covers a broad spectrum from quantum physics, nonlinear physics and biological modeling. His most recent research has led him to analyze the observed propagation of waves of oscillatory activity in the motor cortex, to propose models of post-synaptic domain formation and to try and understand how learning proceeds in the brain, focusing on the cerebellum as a particular case.

CNeuro2024 - Lecture Topic:

Basic Lecture: The Cerebellum: a Computational Perspective

Advanced Lecture:
The Credit Assignment Problem and the Cerebellum

Ole Jensen

Ole Jensen

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Prof. Ole Jensen received his MSc degree in electrical engineering in 1993 from the Technical University of Denmark. He then pursued his PhD at Brandeis University in the United states under the supervision of professor John E. Lisman. In 1998 he obtained his PhD degree in neuroscience specializing in computational modeling of oscillatory networks. The modeling approach was used to account for electrophysiological and behavioral findings on memory in rats and humans. As a postdoctoral fellow he applied magnetoencephalography (MEG) to address questions on brain dynamics and human cognition at the Brain Research Unit, Low Temperature Laboratory. Helsinki University of Technology. He primarily worked with Dr. Claudia Tesche and professor Riitta Hari.

In 2002 he was employed as head of the MEG laboratory at the Donders Institute for Brain, Cognition and Behavior and promoted to principal investigator in 2003. In 2013 he was appointed professor at the Faculty of Science, Radboud University Nijmegen. In 2016 he started a new position as professor in translational neuroscience at University of Birmingham where he is co-director of the new established Centre for Human Brain Health (CHBH). In 2016 he received the Royal Society Wolfson Research Merit Award and in 2018 The Joseph Chamberlain Award for Academic Advancement at University of Birmingham

Research Interests: Brain, Electroencephalography, Magnetoencephalography, Short-Term Memory, Temporal Lobe, Psychological Power, Visual Cortex. 

 

Ole Jensen’s work focuses on linking oscillatory brain activity to cognition: how does oscillatory brain activity shapes the functional architecture of the working brain in the context of memory and attention. To this end he is using magnetoenceohalography (MEG) in combination with other techniques. Recently he established a facility for the application and development of optically pumped magnetometers (OPMs).

Specifically, the core hypothesis states that neuronal communication is gated by inhibitory alpha oscillations in task-irrelevant regions, thus routing information to task-relevant regions. According to this framework the brain can be studied as a network by investigating cross-frequency interactions between gamma and alpha activity.

The research tools applied by Jensen’s group include computational modeling, MEG, EEG combined with fMRI, EEG combined with TMS and intracranial recordings. These tools are applied to investigate and interpret data from humans and animals performing attention and memory tasks. Furthermore the group investigates these mechanism to understand the basis of attention problems in ADHD patients and the aging population.

Recently he established a Optically Pumped Magnetometers laboratory at the Centre for Brain Health to developed new types of MEG sensors. 

CNeuro2024 - Lecture Topic:

Basic Lecture: Linking Human EEG and MEG Data to Deep Neural Networks

Advanced Lecture: Pipelining in the Brain by Coupled Oscillations

Sukbin Lim

Sukbin Lim

New York University Shanghai, Shanghai, China

Sukbin Lim is an Assistant Professor of Neural Science at NYU Shanghai and a Global Network Assistant Professor at NYU. Prior to joining NYU Shanghai, she was a postdoctoral researcher at University of California, Davis and University of Chicago. She holds a PhD from NYU and a BS from Seoul National University.

Research Interests: Modeling and Analysis of Neuronal SystemsComputational Neuroscience, Learning and memory, Network interactions, Dynamical systems.

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Utilizing a broad spectrum of dynamical systems theory, the theory of stochastic processes, and information and control theories, Professor Lim develops and analyzes neural network models and synaptic plasticity rules for learning and memory. It accompanies the analysis of neural data and collaboration with experimentalists to provide and test biologically plausible models.

CNeuro2024 - Lecture Topics:

Basic Lecture: Attractor Network for Working Memory and Decision-Making

Advanced Lecture: Modular Architecture for Understanding Cognitive Interactions

Ruben Moreno Bote
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Rubén Moreno Bote

Universitat Pompeu Fabra, Barcelona, Spain

Dr. Rubén Moreno Bote is a Serra Hunter Professor at the department since 2015.

Recipient of a PhD Extraordinary Prize in Physics in 2005 and a bachelor degree in Physics in 1999 by the Universidad Autónoma de Madrid, he was awarded a Ramón y Cajal Award in 2010 to create the first computational neuroscience group at the Foundation Sant Joan de Deu, before moving to the UPF.

Dr. Moreno Bote is co-organizer of the conference Barcsyn and co-founder of the 1st Summer School in Theoretical and Computational Neuroscience in Barcelona. He is one of the leading scientists in population coding and neuronal dynamics approaches to brain functions, with special emphasis on the study of the computations of spiking neuronal networks. His theoretical work investigating the dynamics of neuronal networks has had a deep impact on the emergent field of Theoretical and Computational Neuroscience, as witnessed by the high number of citations and numerous invitations to give lectures in the most important research institutes, such as the NIH and the Max Planck Institute. His work has been published in highly prestigious research journals such as Nature Neuroscience, Physical Review Letters and PNAS.

Research Interests: Cognition, Visual Perception, Decision-Making, Neuroscience, Neuronal Networks, Behavior.

How do we perceive and decide? Neuroscientists study the brain to understand how we perceive time, make confident decisions or simulate the future. With new theories and recording techniques, the time is ripe for the uncovering of the neuronal mechanisms that define and limit our cognition and self.

In his lab, Dr. Bote and his team thus combine computational and cognitive neuroscience to theorize about and to study the neuronal mechanisms that underlie cognitive functions. Physics, machine learning, psychology and neuroscience are used to understand the computational principles of the brain.

CNeuro2024 - Lecture Topics:


 
Basic Lecture: Introduction to Soft Reinforcement Learning

Advanced Lecture: Behavior Without Rewards: the Maximum Occupancy Principle (MOP)

Naoshige Uchida

Havard University, USA

Naoshige Uchida

Naoshige Uchida is a professor at the Center for Brain Science and Department of Molecular and Cellular Biology at Harvard University. He received his Ph.D. from Kyoto University in Japan, where he worked on the molecular mechanism of synaptic adhesions in Masatoshi Takeichi’s laboratory. He first began studying olfactory coding in Kensaku Mori’s laboratory at the RIKEN Center for Brain Science in Japan. He then joined Zachary F. Mainen’s laboratory at Cold Spring Harbor Laboratory, where he developed psychophysical olfactory decision tasks in rodents. He started his laboratory at Harvard University in 2006.

Uchida’s current research focuses on the neurobiology of decision-making and learning, including neural computation in the midbrain dopamine system, functions of the cortico-basal ganglia circuit, foraging decisions and motor learning. His research combines quantitative rodent behaviors with multi-neuronal recordings, two-photon microscopy, computational modeling and modern tools such as optogenetics and viral neural circuit tracing.

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The ability of efficiently acquiring sensory information and selecting an appropriate action is the essence of flexible behaviors. To make good decisions, it is also crucial to evaluate the consequences of previous actions (gains and costs) and adjust strategies for future decisions. Our laboratory is interested in neuronal processes by which sensory information and memory about previous experiences guide behavior of the animal. Our main questions are:

How is odor information coded and processed by an ensemble of neurons?
What kinds of circuit dynamics underlie decision-making processes?
What are the mechanisms for learning based on rewards and punishments?
We have developed an odor-guided perceptual decision task in rats and mice. This behavioral paradigm was recently combined with a multi-electrode recording technique (tetrodes) which allows us to monitor activity of multiple neurons simultaneously while animals are performing a behavioral task. Particular emphasis will be made on the use of behavior. First, behavioral paradigms specify computations (e.g. extracting relevant information in the sensory stimulus) and behavioral goals (e.g. maximizing rewards in a certain period) to be achieved. This in turn guides the way neuronal activities are analyzed. Furthermore, behavioral experiments are necessary to test relevance of certain activity of neurons or hypothesis about “neural codes” obtained in neuronal recordings. We will use various molecular and genetic tools available in mice to further test specific hypotheses experimentally. By combining the above approaches, we wish to establish a causal link between activity of specific neuronal circuits and the dynamics of behavior and learning.

CNeuro2024 - Lecture Topics:

Basic Lecture: Dopamine and Reinforcement Learning (I)

Advanced Lecture: Dopamine and Reinforcement Learning (II)

Ninglong Xu

Ninglong Xu

Institute of Neuroscience, Shanghai, China

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Dr. Ning-long Xu is the Head of the Laboratory of Neural Basis of Perception. He received his B.S. degree in Bioengineering from Si Chuan University (1999) and his Ph.D. degree in Neurobiology from the Institute of Neuroscience, Chinese Academy of Sciences in 2006. After completing his Ph.D., Dr. Xu conducted postdoctoral research with Dr. Zach Mainen at the Cold Spring Harbor Laboratory (2006-2008) and with Dr. Jeff Magee at Howard Hughes Medical Institute, Janelia Research Campus (2008-2013). Dr. Xu joined ION as a Principal Investigator in August 2013, and was promoted as Senior Investigator in 2020. His research combines novel behavioral task designs with advanced neuronal imaging and circuit manipulation technologies to investigate neural circuit mechanisms underlying fundamental cognitive functions, including perception and decision-making.

Research Interests: Dendrites, Circuits and Perceptual Decision-Making.

The goal of our research is to understand how the architectures and functional operations of neuronal circuits give rise to conscious perception in the mammalian brain.

During conscious perception, our brain actively interprets the sensory data based on internal brain states, such as prior experiences, expectations and attention. Such internal modulation is implemented through long-range feedback and neuromodulatory projections, which crucially influences how we adaptively and dynamically perceive the world. Our laboratory is interested in understanding the underlying neuronal circuits and biophysical mechanisms of the internal modulation during perceptual behavior. Currently using mouse auditory system as the model system, we focus on the following questions: 1. How does internal modulation impact auditory perception at the behavior level?; 2. What are the neural substrate (circuits and neural codes) of internal modulation?; and 3. At the cellular and circuit level, how does internal modulation interact with sensory input and influence perceptual decisions during well-defined perceptual tasks?

We use innovative optical imaging methods, including in vivo two-photon functional imaging and genetically-encoded fluorescent indicators, to record detailed neuronal activity at subcellular resolution in the neocortex of head-fixed mice performing sensory discrimination tasks. For example, we have recently established the methods to image dendritic and axonal calcium signals in defined neural circuits in task performing mice and unraveled a circuit and cellular mechanism for active tactile sensation. These methods allow us to examine the interaction and coordination between circuit elements in the context of precisely quantified stimuli and behaviors. Meanwhile, the neuronal recordings will be complemented by specific optogenetic and pharmaco-genetic perturbations to determine the causal contribution of the circuit operations to specific behaviors.

Our approach combines advanced imaging technologies with powerful molecular and genetic tools and highly sensitive behavioral paradigms, which allows us to tackle challenging problems in neuroscience, and will ultimately help understanding the fundamental neuronal mechanisms that govern our conscious perception.

CNeuro2024 - Lecture Topics:


 
Basic Lecture: Neuronal and Circuit Mechanisms for Information Processing during Sensory and Motor Behaviors

Advanced Lecture: Dendritic and Circuit Computation for an Intelligent Behavior in Mammalian Brain

Tianming Yang

Tianming Yang

Institute of Neuroscience, Shanghai, China

Dr. Tianming Yang obtained his B.S. degree in the Department of Biochemistry at Fudan University. He received his Ph. D. in neuroscience at the Baylor College of Medicine, Houston, Texas, investigating the neural plasticity in visual cortices under the advice of Dr. John Maunsell. He then did his postdoctoral research with Dr. Michael Shadlen then at the University of Washington, Seattle, studying the neural mechanism underlying probabilistic reasoning. In 2008, Dr. Yang became a staff scientist in the Section on Neurobiology of Learning and Memory at the National Institute of Mental Health, USA, working on the reward circuitry in the brain. Since 2013, Dr. Yang works at the Institute of Neuroscience as Investigator and Head of the Laboratory of Neural Mechanisms of Decision Making and Cognition.

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Research Interests: Neurobiology, Biophysics and Artificial Intelligence.

Dr. Yang's lab aims to address the problem of understanding how the brain handles the challenge of life by carrying out various forms of decision making based on fuzzy or incomplete information. On the one hand, the fuzzy initially perceived information can be mathematically quantified with probabilities. They are thus interested in finding out how the brain uses such probabilistic information to make decisions. On the other hand, they also use electrophysiology and fMRI techniques to study the neuronal activities in certain parts of the brain, including areas in the parietal cortex and the prefrontal cortex, to find out the link between behavior and the neural structures in the brain.

However, since the broad field of decision-making has benefited from studies across many different areas, including neuroscience, psychology, mathematics, economy, engineering, and many more, they are continously trying to combine knowledge from diverse fields and put together a framework that not only illustrates how neural networks in the brain carry out decision making, but also provide insights back to other fields. Last but not least, their study will help the treatment of brain diseases that affected the decision-making ability.

CNeuro2024 - Lecture Topics:


 
Basic Lecture: Neural Mechanism of Decision-Making - Basic Frameworks: Signal Detection Theory and Drift-Diffusion Models

Advanced Lecture: Attention, Value, and Confidence - Extending Drift-Diffusion Model

Hang Zhang

Peking University, Peking, China

Dr. Hang Zhang received her Bachelor’s degree in Engineering Physics from Tsinghua University in 2002 and PhD in Cognitive Psychology from the Institute of Psychology of Chinese Academy of Sciences in 2008. She was a postdoctoral fellow in the Department of Psychology at New York University during 2008 and 2014. She joined Peking University in 2014 as Principal Investigator at the School of Psychological and Cognitive Sciences, PKU-IDG/McGovern Institute for Brain Research, and Peking-Tsinghua Center for Life Sciences.

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Hang Zhang

Research Interests: Theoretical Neuroscience, Neural Networks, Dynamical Systems.

Every life is a series of decisions. Even a bee needs to balance the rewards a flower provides and the uncertainty that it may contain nothing. Following a decision-theoretic approach, Dr. Zhang studies a wide range of problems in perception, action, and cognition. The central question here is: how does the brain represent and compute uncertainty in decision-making?

She uses psychophysics and computational modeling, and will integrate brain-imaging techniques, to seek an understanding from behavior, to computational algorithm, to neural basis.

CNeuro2024 - Lecture Topics:

Basic Lecture: Bayesian Observer Models

Advanced Lecture: Structured Priors and Bounded Rationality in Human Learning and Decision-Making

Lusha Zhu

Lusha Zhu

Peking University, Peking, China

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Lusha Zhu is a Peking University Boya Distinguished Professor and Principal Investigator at the School of Psychological and Cognitive Sciences, She has also works at the IDG/McGovern Institute for Brain Research, and the PKU-Tsinghua Center for Life Sciences at Peking University, China.

Research Interests: Decision Neuroscience and Neuroeconomics.

Decision-making in the presence of other competitive intelligent agents is fundamental for social behavior. Such decisions require agents to behave strategically, where in addition to learning about the rewards and punishments available in the environment, they also need to anticipate and respond to actions or consequences of others competing for the same rewards. Whereas strategic learning has been extensively investigated at both behavioral and algorithmic levels in the fields of game theory and artificial intelligence, neurocognitive mechanisms underlying strategic behavior remain to be explored. 

Research in our lab aims at developing mechanistic understanding about brain regions that facilitate such decision-making in either simple interpersonal settings or complex social environments where the brain uses simple heuristics to guide behavior. For example,  based on an economic game called “patent race”, in which individuals compete for the same monetary reward during repeated interactions, we identified separable learning signals encoded in partially overlapping but distinct brain regions. Prediction errors arising from trial and error for learning the available rewards and punishments 

(reinforcement learning) are processed in the ventral striatum; whereas error signals arising from predicting, interpreting, and responding to actions of social opponents (belief learning) are encoded in both the ventral striatum and rostral anterior cingulate. These results suggest that decisions made within competitive environments are guided by inputs from parallel neural processes––one that is common to adaptive behaviors across a wide range of non-social settings and one that is specific to social interactions. We further tested whether these neural regions were necessary for strategic learning by examining the performance of patients with focal lesions in the regions identified in our fMRI study. We found preserved capacity to learn in economic games following the basal ganglia damage, which suggests a model where higher-order learning processes are dissociable from trial-and-error learning and can be preserved despite basal ganglia damage. Ongoing research projects in our lab aim at exploring the interplay between strategic learning and social structures. 

CNeuro2024 - Lecture Topics:


 
Basic Lecture: Social Decision Making: Games, Algorithms, and Brain

Advanced Lecture: Mental Representation and Decision Making on Social Networks

Each year, the organisers of the CNeuro summer school make every effort to recruit faculty members from a diverse background, including all genders and ethnic groups.

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