ArticlePLoS computational biology2025
Decoding decision-making behavior from sparse neural spiking activity.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
Funding
Abstract
Decoding animal decision-making behavior from neural spike data emerges as a particularly challenging problem in neuroscience. To address this, we have devised a decision-making decoding model, incorporating a channel attention bi-directional long short-term memory network (CA-BiLSTM), with the aim of effectively parsing sparse neural spike data across multiple brain regions. Notably, the attention mechanism embedded within this model serves the crucial purpose of adeptly localizing neurons integral to decision-making stability within the task at hand. Specifically, when applied to the reproducible electrophysiology dataset from the International Brain Laboratory (IBL), our proposed model has demonstrated a remarkable capacity for accurately forecasting decision-making behavior in mice. Consequently, this investigation furnishes a novel perspective for unraveling the intricacies of neural decision-making mechanisms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.