Re-evaluating circuit mechanisms underlying pattern separation

By N. A. Cayco-Gajic, R. Silver. 2019

224 citations recorded by the source corpus.

Summary When animals interact with complex environments their neural circuits must separate overlapping patterns of activity that represent sensory and motor information. Pattern separation is thought to be a key function of several brain regions, including the cerebellar cortex, insect mushroom body and dentate gyrus. However, recent findings have questioned long-held ideas on how these circuits perform this fundamental computation. Here we re-evaluate the functional and structural mechanisms underlying pattern separation. We argue that the dimensionality of the space available for population codes representing sensory and motor information provides a common framework for understanding pattern separation. We then discuss how these three circuits use different strategies to separate activity patterns and facilitate associative learning in the presence of trial-to-trial variability.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Neuroscience

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Citations in the corpus

A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, methodology): “of synaptic plasticity” [3], [4], [77], corresponding to the use of meta-learning [118], [185], [360]; and (4) inhibitory synapses for excited neurons to reduce the activity of other neurons [20], [54], [112], [178], which acts similarly to the binary mask for parameter allocation [375], [427].”
The Drosophila Mushroom Body: From Architecture to Algorithm in a Learning Circuit. (2020, background): “However, one notable difference is that forgetting requires the DopR2 receptor (Berry et al. 2012, Cervantes-Sandoval et al. 2016), which drives potentiation (Cohn et al. 2015, Handler et al. 2019).”
Multiple task-demands flexibly optimize neural geometry in human ventral temporal cortex (2026, unknown): “Taken together, given that both frontal and parietal cortex do not exhibit clear evidence for differential on-task optimization for all three tasks in this study, our results indicate that the neural representations in the fronto-parietal network are not flexibly adjusted according to task demands…”