Neural Computation · 2017 · 42 citations · 22 references
Life SciencesNeurobiologyNeuroscienceBehavioral NeuroscienceCognitive NeuroscienceMemoryCognitive ModelingDecision-making CircuitsDecision-making ModelsNeural Basis Of MemoryAttractor DynamicsAttractor NetworksEvidence IntegrationPostdecision Evidence IntegrationPrefrontal Cortex CircuitsPrefrontal Cortex FunctionPrefrontal Cortex InvolvementPrefrontal Cortical Networks
Two-node attractor networks are flexible models for neural activity during decision making. Depending on the network configuration, these networks can model distinct aspects of decisions including evidence integration, evidence categorization, and decision memory. Here, we use attractor networks to model recent causal perturbations of the frontal orienting fields (FOF) in rat cortex during a perceptual decision-making task (Erlich, Brunton, Duan, Hanks, & Brody, 2015 ). We focus on a striking feature of the perturbation results. Pharmacological silencing of the FOF resulted in a stimulus-independent bias. We fit several models to test whether integration, categorization, or decision memory could account for this bias and found that only the memory configuration successfully accounts for it. This memory model naturally accounts for optogenetic perturbations of FOF in the same task and correctly predicts a memory-duration-dependent deficit caused by silencing FOF in a different task. Our results provide mechanistic support for a "postcategorization" memory role of the FOF in upcoming choices.
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Context-dependent computation by recurrent dynamics in prefrontal cortex
Valerio Mante, David Sussillo, Krishna V. Shenoy et al. · Nature · 2013 · 2.1K citations
Molecular and Cellular Approaches for Diversifying and Extending Optogenetics
Viviana Gradinaru, Feng Zhang, Charu Ramakrishnan et al. · Cell · 2010 · 1K citations · Full text