A central goal in neuroscience is to understand how orchestrated computations in the brain arise from the properties of single neurons and networks of such neurons. Answering this question requires theoretical advances that shine light into the ‘black box’ of neuronal networks. In this talk, I will demonstrate theoretical approaches that help describe how cognitive and behavioral task implementations emerge from structure in neural populations and from biologically plausible learning rules.

First, I will introduce an analytic theory that connects geometric structures that arise from neural responses (i.e., neural manifolds) to the neural population’s efficiency in implementing a task. In particular, this theory describes how easy or hard it is to discriminate between object categories based the underlying neural manifolds’ structural properties.

Next, I will describe how such methods can, in fact, open the ‘black box’ of neuronal networks, by showing how we can understand a) the role of network motifs in task implementation in neural networks and b) the role of neural noise in adversarial robustness in vision and audition. Finally, I will discuss my recent efforts to develop biologically plausible learning rules for neuronal networks, inspired by recent experimental findings in synaptic plasticity. By extending our mathematical toolkit for analyzing representations and learning rules underlying complex neuronal networks, I hope to contribute toward the long-term challenge of understanding the neuronal basis of behaviors.

 

• 연사 소개: 

정수연 박사님은 현재 Columbia University)의 이론 신경과학 연구소에서 박사 후 연구원으로 재직 중이십니다. Haim Sompolinsky 와 Ryan P. Adams 의 지도 아래 2017 년 Harvard University 에서 응용 물리학 박사를 받으시고, 이후 Massachusetts Institute of Technology 로 옮겨 Brain and Cognitive Science 의 fellow 연구원으로 계셨습니다. 현재까지도 Josh McDermott 와 Jim DiCarlo 등의 연구자들과 함께 공동 연구를 진행하고 계십니다. 생물체의 뉴런 반응과 인공 신경망을 접목 시키는 다양한 이론적 연구를 진행하셨으며, 현재 생물의 뇌 신경망 시스템과 닮은 인공 신경망 (biologically plausible learning rule for neural networks) 연구 수행 중이십니다.