Tai Sing Lee

This paper develops a computational model of recurrent cortical circuits that explain familiarity suppression in the visual system and show how it enhances robust encoding of familiar stimuli.

The paper investigates computational constraints shaping the topological map of macaque V4, showing self-organizing principles can explain its structure and functional domains.

The paper investigates cue-invariance in neural representations of visual patterns in macaque V1 and V2, finding that population geometry provides a more robust cue-invariant representation than individual neuron tuning.

This study shows that familiarity training increases neural straightening in macaques, supporting the role of learning in predictive processing through neural trajectory refinement.

This paper proposes a spiking neural circuit model that explains the emergence of congruent and opposite neurons in multi-sensory integration and suggests it may be a canonical circuit in cortical areas.

This study shows that early postnatal visual experience enhances natural scene discriminability in V1 by reducing low-spatial frequency grating-responsive neurons and increasing natural scene responsiveness.

This paper develops a model of coupled neural circuits that perform distributed Bayesian inference in high-dimensional stimulus spaces using Langevin sampling.

The paper introduces a model where congruent and opposite neurons emerge through Hebbian and anti-Hebbian learning rules, reproducing experimentally observed tuning properties in multisensory integration.

This paper introduces a network model that learns opposite neurons using Hebbian and Anti-Hebbian rules, explaining their formation and role in multisensory integration.

The paper proposes a neural model for concurrent multisensory integration and segregation using congruent and opposite neurons to rapidly recognize stimuli based on sensory cues.

This study evaluates CNN models for modeling V1 neuron responses to complex patterns, finding that thresholding nonlinearity and convolution are key factors in their superior performance.

This study uses two-photon imaging in awake monkeys to show that V1 superficial layer neurons exhibit super-sparse population codes, with only 0.5% responding strongly to any given image.