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DTSTAMP:20250921T181201Z
DESCRIPTION:Predictive coding with cortical cell types and connectivity\n\n
 Hannah Choi\, Georgia Tech\n	Tuesday October 7\, 12-1pm\n	Zoom Link: https:/
 /mcgill.zoom.us/j/87078928687\n	In Person: 550 Sherbrooke\, Room 189\n	\n	Abs
 tract: The brain efficiently processes sensory information by generating a
 n internal model of the environment\, which is continuously updated throug
 h prediction errors and the suppression of expected information— a process
  known as predictive coding. While experimental evidence supports cortical
  implementation of predictive coding\, it remains unclear how this process
  is shaped by connectivity across the cortical hierarchy\, layers\, and di
 verse neuronal subtypes.\n\nI will discuss recent work from my group that 
 investigates cortical mechanisms underlying predictive coding. By construc
 ting a biologically grounded network model with realistic connectivity amo
 ng cortical cell types\, our work uncovers context-dependent functional co
 nnectivity that reflects the communication of predictions and prediction e
 rrors across the cortical circuits. Furthermore\, by mapping algorithmic c
 omponents of predictive coding onto neuronal subtypes\, our study generate
 s experimentally testable predictions about cell-type-specific responses t
 o expected and unexpected stimuli. We show how different computational obj
 ectives such as prediction error minimization\, energy efficiency\, and re
 ward maximization shape cell-type-specific responses to distinct forms of 
 expectation violation\, including contextual\, absolute\, and omission nov
 elty. Our study thus integrates a multi-objective normative framework with
  a biological circuit model\, offering a unified account of how predictive
  coding\, energy efficiency\, and reinforcement learning map onto cell-typ
 e-specific mechanisms.\n
DTSTART:20251007T160000Z
DTEND:20251007T170000Z
SUMMARY:QLS Seminar Series - Hannah Choi
URL:/science/channels/event/qls-seminar-series-hannah-
 choi-367779
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