University of Colorado Boulder in Colorado, USA
Dr. Ahmed's research focuses on understanding how the brain controls movement. She uses a neuroeconomic approach that combines techniques from neuroscience, economics, psychology and engineering to investigate the costs and constraints underlying human sensorimotor decision-making, learning, and control.
Alaa Ahmed is the recipient of an NSF CAREER Award and a DARPA Young Faculty Award presented to “rising research stars in junior faculty positions at U.S. academic institutions”. Her work has been featured in Forbes, Wired, Time, PBS, and other national and international media outlets.
Research Interests: Biomechanics, Neural Control of Movement, Motor Learning and Decision Making, Neuroeconomics
Dr. Ahmed's Neuromechanics Lab is part of the Department of Mechanical Engineering and Biomedical Engineering Program at the University of Colorado Boulder. The goal of their research program is to use a neuroeconomic approach to gain fundamental knowledge about the subjective costs and rewards underlying movement control. They try to reverse engineer how the brain controls movement using a combination of approaches involving virtual reality, robotic interfaces, kinetic and kinematic analyses. Coupled with computational models, these investigations will provide greater insight into the interplay between the biomechanical and sensorimotor processes underlying human movement control and decision-making.
Silvia Arber is a Swiss neuroscientist recognized for her work on the organization and function of neuronal circuits controlling movement. Arber was born and grew up in Switzerland. She studied biology at the Biozentrum, University of Basel, Switzerland and obtained her Ph.D. working in the laboratory of Pico Caroni at the Friedrich Miescher Institute for Biomedical Research (FMI) in Basel in 1996. She then pursued a postdoctoral fellowship in the laboratory of Thomas Jessell at Columbia University in New York, where she delineated mechanisms important for motor neuron identity and sensory synaptic input specificity. Arber returned to Basel in 2000 to establish her independent research group at the Biozentrum and the FMI, where she has been examining how neuronal circuits in the spinal cord and brain develop and control body movement. She has been recognized for her pioneering research with numerous prizes, including the Pfizer Research Prize (1998), the Latsis Prize (2003), the Schellenberg Prize (2003), the Friedrich Miescher Award (2008), the Otto Nägeli Award (2014), the Louis Jeantet Prize for Medicine (2017) and the NAS Pradel Research Award (2018), the Louis Jeantet Prize for Medicine (2017), the NAS Pradel Research Award (2018), and the Brain Prize (2022).
Research Interests: Neuronal Circuits, Motor Behavior, Genetics
Silvia Arber’s laboratory is interested in the identification of principles by which neuronal circuits orchestrate accurate and timely control of motor behavior. The work addresses how the nervous system can produce a large repertoire of movement patterns, covering diverse motor actions from locomotion to skilled forelimb tasks. To decipher how motor circuits engage in the control of movement and contribute to the generation of diverse actions, the group unravels how neuronal subpopulations are organized into specific circuits, and studies the function of identified circuits in execution and learning of motor programs. Using multi-facetted approaches combining many technologies, they study nervous-system wide neuronal circuits involved in motor control, including how brain circuits interact with executive circuits in the spinal cord to produce and regulate body movements. The work also aims at the discovery of mechanisms involved in motor circuit assembly during development, as well as circuit plasticity during motor learning and in response to disease or injury.
Ruben Moreno Bote
Edifici Mercè Rodoreda, 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.
Kameron Decker Harris
Western Washington University, USA
Dr. Kameron Decker Harris is an assistant professor of Computer Science at Western Washington University working on computational neuroscience and related mathematical problems. As a computational scientist and applied mathematician, he has an interest in networked dynamical systems, particularly in neural systems. These systems are often adaptive, meaning that they can learn from their environment, and exhibit complex behavior.
Research Interests: Computational neuroscience, networks, graph theory, and applied mathematics
In his lab, Dr. Harris and his team has found it illuminating to ask: what changes when you add structures that are found in successful biological neuronal systems? For example, they have discovered that sparsity, an important conserved property of cerebellum and mushroom body brain areas, leads to the network approximating additive functions. This constraint further leads to faster learning from fewer examples in such networks. Similarly, allowing the weights to reproduce known neuronal tuning properties from V1 leads to improved performance on simple image recognition tasks (work with Biraj Pandey and Bing Brunton).
Thus, their view of networks as function approximators is uncommon in computational neuroscience, but offers many advantages. And they have found that the kernel associated with a network architecture tells you important geometrical properties about the functions that it can approximate.
Wigner Research Centre for Physics, Hungarian Academy of Sciences, Hungary
Gergő Orbán is a Lendület Research Fellow of the Hungarian Academy of Sciences. He earned his PhD from Eötvös University under the supervision of Péter Érdi. His postdoctoral work was with Eörs Szathmáry in Collegium Budapest, Institute for Advanced Study and later joined József Fiser’s lab at the Volen Centre at the Brandeis University as a Swartz Postdoctoral Fellow. He later was awarded a Marie Curie Posdoctoral Fellowship and joined Daniel Wolpert and Máté Lengyel at the Computational and Biological Learning Lab at the University of Cambridge.
At the Computational Systems Neuroscience Lab, Gergő's and his fellow lab members' research focuses on two levels of computation. Models of high-level computation aim at understanding the representation that humans use to learn about their environment. The way information is represented constrains both the ways new information can be acquired and how learned information can be exploited for achieving various (e.g. behavioral) goals. Since learning has to be performed on high-dimensional, noisy and ambiguous stimuli, probabilistic models are adequate tools as these models can handle all of these issues. Furthermore, Bayesian probabilistic models provide a normative theory for learning, which enables them to compare model performance with human data. They test theories by analyzing behavior of humans in experiments: by following participants’ eye movement they analyze how learning affects the design of efficient movement strategies.
Additionally, their investigations in low-level computations address how neurons deal with the problems imposed by the extremely rich stimuli. Optimal inference and learning requires that neurons also represent the uncertainty related to the inferred features of the environment besides the actual values of the features. The focus here is, thus, on how a proper representation can be built and how these principles affect neural responses. Probabilistic models are used to model evoked and spontaneous activities in the visual system.
The Hebrew University of Jerusalem, Jerusalem, Israel
Prof.Yonatan Loewenstein has an interest in understanding the computational principles underlying complex behaviors and cognitive processes, and how these emerge from the (relatively) simple microscopic physical processes in the brain. In particular, he is interested in the neural basis and computational principles underlying operant learning. To that goal, he studies the behavior of humans and animals in controlled and natural conditions, and develop computational and mechanistic neural networks models to explain these behaviors.
Research Interests: Decision-making and reinforcement learning, Reinforcement learning and cellular plasticity, The neuronal mechanisms underlying contraction bias, the functional architecture of the cerebellar cortex..
Specifically, in his lab, Loewenstein's research focuses on two levels of computation. Models of high-level computation aim at understanding the representation that humans use to learn about their environment. The way information is represented constrains both the ways new information can be acquired and how learned information can be exploited for achieving various (e.g. behavioral) goals. Since learning has to be performed on high-dimensional, noisy and ambiguous stimuli, probabilistic models are adequate tools as these models can handle all of these issues. Furthermore, Bayesian probabilistic models provide a normative theory for learning, which enables them to compare model performance with human data. They test theories by analyzing behavior of humans in experiments: by following participants’ eye movement they analyze how learning affects the design of efficient movement strategies.
Furthermore, in his lab, investigations in low-level computations addresses how neurons deal with the problems imposed by the extremely rich stimuli. Optimal inference and learning requires that neurons also represent the uncertainty related to the inferred features of the environment besides the actual values of the features. The focus is, thus, on how a proper representation can be built and how these principles affect neural responses. Probabilistic models are used to model evoked and spontaneous activities in the visual system.
University College London, London, United Kingdom
Maneesh Sahani is Professor of Theoretical Neuroscience and Machine Learning at the Gatsby Computational Neuroscience Unit at University College London (UCL). Graduating with a B.S. in physics from Caltech, he stayed to earn his Ph.D. in the Computation and Neural Systems program, supervised by Richard Andersen and John Hopfield. After periods of postdoctoral work at the Gatsby Unit and the University of California, San Francisco, he returned to the faculty at Gatsby in 2004 and was elected to a personal chair at UCL in 2013. His work spans the interface of the fields of machine learning and neuroscience, with particular emphasis on the types of computation achieved within the sensory and motor cortical systems. He has helped to pioneer analytic methods which seek to characterize and visualize the dynamical computational processes that underlie the measured joint activity of populations of neurons. He has also worked on the link between the statistics of the environment and neural computation, machine-learning based signal processing, and neural implementations of Bayesian and approximate inference.
Research Interests: Computational methods, Perception, Auditory processing, Visual perception, Psychoacoustics, Vision, Hearing, Attention, Neocortex, Coding, Behaviour, Cognitive, Statistics, Neural Circuits/Networks, Psychophysics
Prof. Sahani's research work focuses on behavioural investigations dating from the time of Helmholtz. These have demonstrated that animals and humans process sensory information close to optimality, often employing subtle and powerful algorithms to do so. Their understanding of these computations at the neural level is, by contrast, quite simplistic. The goal of research in his group is to help bridge this gap, using both data-driven and theoretical approaches to understand how information is represented in neural systems, and how this representation underlies computation and learning. On the one hand, they collaborate closely with physiologists to advance the technology of neural data collection and analysis. These studies have the potential to introduce powerful new theoretically-motivated ways of looking at neural data. At the same time, they examine neural information representation from a more theoretical point of view, addressing questions of how the brain might encode the richness of information needed to explain perceptual capabilities, what purpose might be served by adaptation in neural activities, and how experience-driven plasticity in representations is related to perceptual learning. Both the data analytic and the theoretical aspects of their neuroscience research are closely connected to the field of machine learning, which provides the tools needed for the first, and a structural framework for the second.
Institute of Neuroscience, 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.
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.
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.
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.
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.