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Tsai, M. C.

Publications and source records attributed to Tsai, M. C..

2 recordsLinked to original sources

Hierarchy of prediction errors shapes the learning of context-dependent sensory representations

How sensory information is interpreted depends on context, yet the neural mechanisms by which context shapes sensory processing remain poorly understood. To address this question, we developed a computational model constrained by in vivo functional imaging of cortical neurons in mice during reversal learning of a tactile sensory discrimination task. During learning, layer 2/3 somatosensory neurons enhanced their response to reward-predictive stimuli. The model accounted for these observations through selective top-down gain amplification of apical dendritic inputs, accompanied by reduced reward-prediction errors and increased confidence in outcome predictions. Upon rule-reversal, the lateral orbitofrontal cortex, through disinhibitory VIP interneurons, encoded a context-prediction error signaling a loss of confidence. The hierarchy of reward- and context-prediction errors across cortical areas is mirrored in top-down signals modulating apical activity of simulated pyramidal neurons in the primary sensory cortex. The model explains how contextual changes are detected and how reward- and context-prediction error signals, originating in different cortical regions, interact to reshape the sensory representation.

neuroscience↗

Dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contextual state. Here, we first demonstrate that thin dendritic branches are well suited to implementing contextual modulation of feedforward processing. Such neuron-specific modulations exploit prior knowledge, encoded in stable feedforward weights, to achieve transfer learning across contexts. In a network of biophysically realistic neuron models with context-independent feedforward weights, we show that modulatory inputs to thin dendrites can solve linearly non-separable learning problems with a Hebbian, error-modulated learning rule. Finally, we demonstrate that local prediction of whether representations originate either from different inputs, or from different contextual modulations of the same input, results in representation learning of hierarchical feedforward weights across processing layers that accommodate a multitude of contexts.

neuroscience↗