Open Access. Powered by Scholars. Published by Universities.®
Biological and Chemical Physics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Publication Type
Articles 1 - 2 of 2
Full-Text Articles in Biological and Chemical Physics
A Causal Inference Approach For Spike Train Interactions, Zach Saccomano
A Causal Inference Approach For Spike Train Interactions, Zach Saccomano
Dissertations, Theses, and Capstone Projects
Since the 1960s, neuroscientists have worked on the problem of estimating synaptic properties, such as connectivity and strength, from simultaneously recorded spike trains. Recent years have seen renewed interest in the problem coinciding with rapid advances in experimental technologies, including an approximate exponential increase in the number of neurons that can be recorded in parallel and perturbation techniques such as optogenetics that can be used to calibrate and validate causal hypotheses about functional connectivity. This thesis presents a mathematical examination of synaptic inference from two perspectives: (1) using in vivo data and biophysical models, we ask in what cases the …
Rules And Mechanisms For Efficient Two-Stage Learning In Neural Circuits, Tiberiu Teşileanu, Bence Ölveczky, Vijay Balasubramanian
Rules And Mechanisms For Efficient Two-Stage Learning In Neural Circuits, Tiberiu Teşileanu, Bence Ölveczky, Vijay Balasubramanian
Publications and Research
Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-related circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in ‘tutor’ circuits (e.g., LMAN) should match plasticity mechanisms in ‘student’ circuits (e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching …