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Statistical Inference of Low-dimensional Cognitive Computation from Neural Recordings

Prof. Il Memming Park

September 19(Thu) - September 19(Thu), 2019

12:00 - 13:00

# 86120

ABC Seminar

 

Date: 12:00 pm Thursday, September 19th


 
Place: #86120

Speaker: Prof. Il Memming Park

Department of Neurobiology and Behavior,

Department of Applied Mathematics and Statistics,

Institute for Advanced Computational Science,

Institute of AI-driven Discovery and Innovation,

Department of Electrical and Computer Engineering,

Stony Brook University


http://catniplab.github.io/

http://memming.wordpress.com


 

Title: "Statistical Inference of Low-dimensional Cognitive Computation from Neural Recordings" 

 

Abstract:
Our capacity to construct and intuit mathematical models is central to scientific understanding of natural systems. By controlling and analyzing the input and output to the brain -- the sensory stimulus and the behavior -- we have made great advances in modeling cognitive computation which is often well described by simple mathematical models such as a network of noisy leaky integrators and comparators. However, despite extensive efforts, how the brain implements those computations as a biophysical system is still largely unknown. One of the main obstacles is the subsampling problem: there are many (hundreds to hundreds of millions of) neurons across brain areas involved in transforming sensory information to producing the behavior, however, our experimental technology has been limited to observing neural signals from a small fraction of neurons in a handful of areas at the same time. This vast subsampling has limited our ability to infer the physical implementation of cognitive computation in the brain. We had to heavily rely on theoretical principles, intuition, and imagination as to how they might be implemented by a complex network of spiking neurons. Fortunately, recent advances in neural recording technology is allowing us access to several orders of magnitude more neurons at a high temporal precision. This is opening up new opportunities to directly infer the neural implementations: how external and internal information is represented in the population, and how it is transformed over time and across areas. Even for the simplest cognitive processes, such a bottom-up approach has not been successful so far in uncovering the underlying neural dynamics. In this presentation, I lay a principled approach that can tackle the subsampling problem by exploiting the low-dimensional structure of tasks and neural variability. This new approach to studying neural codes and computation is possible through advances in statistical and machine learning techniques aimed at extracting interpretable, i.e., scientifically useful, mathematical models. We have developed interpretable probabilistic models and Bayesian inference algorithms suitable for reverse engineering neural computation from large-scale population data.


Host: Prof. Choong-Wan Woo