Theoretical and Computational Neuroscience Summer School
CNeuro2025 Faculty
Rava Azeredo da Silveira
Institute of Molecular & Clinical Ophthalmology in Basel, Switzerland and University of Zürich, Switzerland
Rava Azeredo da Silveira’s lab focuses on a range of topics in theoretical and computational neuroscience and cognitive science. These topics, however, are tied together through a central question: How does the brain represent and manipulate information?
Research Interests: ​Computational Neuroscience & Neurotechnology, Deciphering (Patho-) Physiological Mechanisms, Visual System.
Among the more concrete approaches to this question, the lab analyzes and models neural activity in circuits that can be identified, recorded from, and perturbed experimentally. On a more abstract level, the lab investigates the representation of information in populations of neurons, from a statistical and algorithmic — rather than mechanistic — point of view, through theories of coding and data analyses. In the context of cognitive studies, the lab investigates mental processes such as inference, learning, and decision-making, through both theoretical developments and behavioral experiments. A particular focus is the study of neural constraints and limitations and, further, their impact on mental processes.
CNeuro2025 - Lecture Topics:
Basic Lecture: tbd
Advanced Lecture: tbd
Albert Compte
I studied Physics in the Autonomous University of Barcelona both at the undergraduate and graduate levels. My postdoctoral research was in the laboratory of Prof. Xiao-Jing Wang at Brandeis University in the framework of an Alfred P. Sloan fellowship for Theoretical Neurobiology. From 2002 to 2006 I led the Computational Neuroscience group in the Instituto de Neurociencias de Alicante, where I held a research position within the Ramón y Cajal program. In 2007 I moved to IDIBAPS, where I am currently leading, together with Jaime de la Rocha, the Brain Circuits and Behavior Lab.
Research Interests: Systems Neuroscience, Computational Neuroscience.
We investigate these questions with a combination of experimental and computational methods. We employ behavioral tasks that can be adapted for humans and animals and use mathematical models to characterize behavior and to interpret neural recordings obtained from EEG, fMRI in humans or neural intracranial recordings in animals.
The laboratory is the result of a merge between the Theoretical Neurobiology Group led by Albert Compte and the Cortical Circuit Dynamics group led by Jaime de la Rocha. The team integrates researchers with different backgrounds ranging from biomedicine, bioengineering, physics, mathematics, computer science and psychology. Our group belongs to the Clinical and Experimental Neuroscience research Area of the IDIBAPS, a clinical research center in Barcelona associated with the University of Barcelona and the Hospital Clinic. Our laboratory is also an active node of the Barcelona Computational Cognitive and Systems Neuroscience (BARCCSYN) community, which integrates around twenty laboratories scattered across multiple local institutions.
CNeuro2025 - Lecture Topic:
Basic Lecture: tbd
Advanced Lecture: tbd
Haiping Huang
Sun Yat-Sen University, Guangzhou, China
Prof. Huang received his PhD degree in theoretical physics in 2011, from the Institute of Theoretical Physics, Chinese Academy of Science. He then visited the Hong Kong University of Science and Technology as a visiting scholar, and became later a JSPS fellow in the Tokyo Institute of Technology (2012-2014). In 2014, he moved to the RIKEN Brain Science Institute as a research scientist. Since 2018, he lead the physics, machine and intelligence lab at the School of Physics, Sun Yat-Sen University, focusing on physics basis of various kinds of neural computations. His book "Statistical Mechanics of Neural Networks" was recently published by Springer in 2022.
Research Interests: Replica theory, cavity method, dynamical mean field theory, phase transitions, restricted Boltzmann machine, recurrent neural networks, Bayesian computation, supervised/unsupervised learning in deep networks
CNeuro2025 - Lecture Topics:
Basic Lecture: Introduction to Hopfield Paradigm: Learning Efficiency and Representation Geometry
Advanced Lecture: Non-Hopfieldian Dynamics: Challenges and Opportunities
Markus Meister
Dr. Meister studied physics at the Technische Universität in München, Germany, then at Caltech, where he received a Ph.D. for research on bacterial motion with Howard Berg. He was introduced to the beauty and mysteries of the retina during post-doctoral research with Denis Baylor at Stanford University. In 1991, Dr. Meister took a professorship at Harvard University, where he worked until his return to Caltech in 2012. Dr. Meister studies the function of large neuronal circuits, and the animal behaviors that they sustain. Early in his career he focused on the visual and olfactory sensory systems. He pioneered the use of multi-electrode arrays for parallel recording from many of the retina's output neurons. Together with new approaches to visual stimulation, this helped reveal how much visual processing is accomplished in the retina. His work extended to both smaller and larger scales of organization: on the one hand the circuit mechanisms of visual computations, on the other the role of neural computation for visually guided behavior. To understand the next stage of visual processing, Meister's group has been exploring population coding in the mammalian superior colliculus. A more recent thread of research is aimed at phenomena of rapid learning in animals, from both a behavioral and theoretical perspective. Meister has also served on advisory boards of research organizations and foundations including the Allen Brain Institute, the Howard Hughes Medical Institute, the Max Planck Institute for Neurobiology, Cold Spring Harbor Laboratory, the Pew Scholars Program, the Helen Hay Whitney Foundation, and the McKnight Endowment Fund for Neuroscience.
Research Interests: ​neural circuits and computations in sensory systems: vision, olfaction and vomeronasal sensation.
My goal is to understand how large circuits of neurons work. By "circuit" I mean a brain structure with many neurons that has some anatomical and functional identity, and exchanges signals with other brain circuits. "Understanding" such a neural circuit will require answers to the following: What does the circuit do? Find the function that relates the inputs to the outputs of this part of the brain. How does it do that thing? Spell out the mechanism behind this computation in terms of signals flowing through neurons and synapses. Why does the circuit do that? Explain how the functions of this circuit fit into the larger brain and relate its role to the animal's behavior. For some time the lab's focus was on circuits for visual processing, in particular retina and superior colliculus. We have also worked on circuits for olfaction. I maintain an interest in these sensory areas, but our current research has turned toward problems that are comparatively less well understood: (1) Mechanisms of rapid learning: Animals can learn a complex sequence of actions after just one or a few successful episodes. (2) Task control: Many behaviors require a rapid switching between different tasks. How is that coordinated? In all these pursuits, we try to use the full toolkit of modern neuroscience: electrophysiology, optophysiology, molecular genetics, psychophysics, theory and modeling.
CNeuro2025 - Lecture Topic:
Basic Lecture: The Unbearable Slowness of Being: Why Do We Live at 10 bits/s?
Advanced Lecture: Rapid Learning of Complex Tasks - from Phenomena to Algorithms
Bin Min
I am a principal investigator at the Lin Gang Laboratory. I was a postdoctoral fellow with Xiao-Jing Wang and David Cai at New York University, and I obtained both my bachelor and PhD degrees at the School of Mathematical Sciences in Applied Mathematics at Peking University.
Research Interests: Sequence learning, Cognitive Control, Decision-Making, and Planning
As a computational neuroscientist, I am dedicated to understanding the neural computations underlying higher cognitive functions, including sequence learning, cognitive control, decision-making, and planning. My work bridges theory and practice by (1) constructing interpretable deep neural network models, (2) developing cutting-edge data analysis techniques, and (3) collaborating closely with experimental labs.
CNeuro2025 - Lecture Topic:
Basic Lecture: tbd
Advanced Lecture: tbd
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.
CNeuro2025 - Lecture Topics:
Basic Lecture: tbd
Advanced Lecture: tbd
Shanshan Qin
Shanghai Jiaotong University, China
I am an Associate Research Scientist at the Center for Computational Neuroscience, Flatiron Institute. Driven by a broad interest in theoretical and computational neuroscience, my research seeks to understand the fundamental principles driving neural computation and cognition.
Current Research Focus:
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Sensory processing and representation: How does the brain efficiently perceive and encode the external world?
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Memory organization and update: How does the brain store and dynamically update memories to support adaptive behavior and lifelong learning?
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Algorithmic foundations of computation: What are the underlying algorithms the brain uses to solve complex problems?
Research Interests: Modeling Theoretical and Computational, Neuroscience, Quantiative and Systems Biology, Physics of life
Prior to joining Flatiron Institute, I held a postdoctoral fellowship at Harvard’s John A. Paulson School of Engineering and Applied Sciences where I worked with Cengiz Pehlevan. I earned my PhD in condensed matter physics from Peking University where I was advised by Chao Tang and Yuhai Tu at the Center for Quantitative Biology. Before that, I pursued my undergraduate studies in physics at Central China Normal University in Wuhan.
I will join the Institute of Natural Sciences at Shanghai Jiao Tong University as a tenure-track Associate Professor starting from January 2025.
CNeuro2025 - Lecture Topics:
Basic Lecture: tbd
Advanced Lecture: tbd
Yuxiu Shao
Beijing Normal University, China
I am an assistant professor at the School of Systems Science at Beijing Normal University. Before that, I was a post-doc researcher at Group for Neural Theory, École Normale Supérieure (ENS, Paris), where I work with Srdjan Ostojic and the whole incredible ‘LOW-RANK’ team. Before that, I completed my Ph.D. in Computational Neuroscience at Peking University (PKU) in Beijing, China, under the supervision of Louis Tao. Before that I got my bachelor degree in Mechanical Engineering & Automation. So you see, I do have a quite interdisciplinary training background.
Research Interests: Random matrix theory, statistical physics, graph theory, mean-field theory, low-rank RNN, neural population dynamics, Reinforcement learning, recurrent neural network, theory of latent dynamics(for lrRNNs).
As a theoretical neuroscientist and artificial intelligence enthusiast, my passion lies in advancing the understanding of biologically constrained network connectivity and neuronal computations. My research focuses on the intersection of biological and artificial neural networks, with the goal of establishing theoretical frameworks and biologically rational algorithms that can shed light on the structures, dynamics, and relationships between these two domains; as well as identify neural computational mechanisms that support flexible behavior using both artificial networks and analytical framework. Through my work, I aim to contribute to a more comprehensive understanding of the brain, and, ultimately, to the development of more effective and biologically inspired network systems.
CNeuro2025 - Lecture Topics:
Basic Lecture: tbd
Advanced Lecture: tbd
Qianli Yang
Institute of Neuroscience (CEBSIT), CAS, China
Dr. Qianli Yang’s primary focus is on explaining brain’s cognitive function by constructing quantitative theories of how distributed nonlinear dynamic neural computation implements principles of statistical reasoning. He obtained his B.S. degree in the Department of Physics at Nanjing University. He received his Ph.D. in Electrical and Computer Engineering at Rice University, Houston, Texas, investigating theory and statistical methods to reveal the nonlinear neural computation from animals’ naturalistic tasks, under the advice of Dr. Xaq Pitkow. He’s currently doing his postdoctoral research with Dr. Tianming Yang at Institute of Neuroscience, CAS, Shanghai, studying the neural mechanism of strategy learning in complicated decision-making tasks.
Research Interests: Computational Neuroscience, Cognitive Neuroscience, Decision-making, Perception.
CNeuro2025 - Lecture Topics:
Basic Lecture: The Bayesian Brain: From Probabilistic Inference to Neural Codes
Advanced Lecture: Build a Bayesian Brain from Scratch: Multisensory Perception through
Probabilistic Neural Codes
Xinyu Zhao
Tsinghua University, Beijing, China
Currently, I am an Assistant Professor at the Tsinghua-Peking Center for Life Sciences, IDG/McGovern Institute for Brain Research, Tsinghua University in Beijing, China, I obtained my Ph.D at the ECE department of Northwestern University and my bachelor degree from the University of Electronic Science and Technology of China (UESTC).
Research Interests: Visual Processing, Computer Vision and Machine Learning.
How does the brain learn? With a combination of experimental and computational approaches, we primarily focus on two questions: 1. What is the algorithm that updates connection weights between neurons during learning? 2. How does a neural network transfer its previously learned knowledge to new tasks? We will investigate these questions in mice with multi-disciplinary technologies, including electrophysiology, imaging, optogenetics, computational neuroscience, and machine learning. We believe in a positive feedback loop between experimental and computational studies, which will eventually facilitate both the understanding of the brain and the development of novel artificial intelligent systems.
CNeuro2025 - Lecture Topics:
Basic Lecture: tbd
Advanced Lecture: tbd
Pengcheng Zhou
Shenzhen University of Advanced Technology, China
Zhou Pengcheng, Ph.D. is currently an assistant professor at the School of Life Sciences and Health, Shenzhen University of Technology (under construction) and an associate researcher at the Shenzhen Institutes of Advanced Technology. He received his Bachelor of Science in Physics from the University of Science and Technology of China (2010), and his PhD in Neural Computation and Machine Learning from Carnegie Mellon University (2016). He then conducted postdoctoral research at the Department of Statistics and the Center for Computational Neuroscience at Columbia University (2020).
Research Interests: Computational Neuroscience and Machine Learning.
Pengcheng's research direction is the intelligent intersection of life sciences and computers. He mainly engages in the application of computational neuroscience and machine learning in brain science, especially the statistical modeling and automated analysis and processing of large-scale brain science data. Its series of methods for calcium fluorescence data processing have been widely used in the field. Recent work is devoted to developing and integrating a series of computational tools in the field of neuroscience to create a complete automated process that integrates big data storage, transmission, visualization, intelligent processing and statistical analysis. The papers were published in high-impact journals such as eLife, Nature Methods, Nature Neuroscience, Neuron, and have been cited more than 1,000 times.
CNeuro2025 - Lecture Topics:
Basic Lecture: tbd
Advanced Lecture: tbd
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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.