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dc.contributor.authorHoward, Marc W.en_US
dc.contributor.authorLuzardo, Andreen_US
dc.contributor.authorTiganj, Zoranen_US
dc.date.accessioned2019-10-08T16:04:22Z
dc.date.available2019-10-08T16:04:22Z
dc.date.issued2018
dc.identifierhttp://arxiv.org/abs/1806.04122v2
dc.identifier.citationMarc W Howard, Andre Luzardo, Zoran Tiganj. 2018. "Evidence accumulation in a Laplace domain decision space." Computational Brain & Behavior, Volume 1, pp. 237 - 251. https://doi.org/10.1007/s42113-018-0016-2
dc.identifier.urihttps://hdl.handle.net/2144/38218
dc.description.abstractEvidence accumulation models of simple decision-making have long assumed that the brain estimates a scalar decision variable corresponding to the log likelihood ratio of the two alternatives. Typical neural implementations of this algorithmic cognitive model assume that large numbers of neurons are each noisy exemplars of the scalar decision variable. Here, we propose a neural implementation of the diffusion model in which many neurons construct and maintain the Laplace transform of the distance to each of the decision bounds. As in classic findings from brain regions including LIP, the firing rate of neurons coding for the Laplace transform of net accumulated evidence grows to a bound during random dot motion tasks. However, rather than noisy exemplars of a single mean value, this approach makes the novel prediction that firing rates grow to the bound exponentially; across neurons, there should be a distribution of different rates. A second set of neurons records an approximate inversion of the Laplace transform; these neurons directly estimate net accumulated evidence. In analogy to time cells and place cells observed in the hippocampus and other brain regions, the neurons in this second set have receptive fields along a “decision axis.” This finding is consistent with recent findings from rodent recordings. This theoretical approach places simple evidence accumulation models in the same mathematical language as recent proposals for representing time and space in cognitive models for memory.en_US
dc.format.extent237 - 251en_US
dc.relation.ispartofComputational Brain & Behavior
dc.subjectq-bio.NCen_US
dc.subjectq-bio.NCen_US
dc.subjectq-bio.NCen_US
dc.subjectq-bio.NCen_US
dc.subjectNeuroscienceen_US
dc.subjectEvidence accumulationen_US
dc.subjectDiffusion modelen_US
dc.subjectLaplace transformen_US
dc.subjectNeurophysiological models of cognitionen_US
dc.subjectNeurologyen_US
dc.titleEvidence accumulation in a Laplace domain decision spaceen_US
dc.typeArticleen_US
dc.description.versionAccepted manuscripten_US
dc.identifier.doi10.1007/s42113-018-0016-2
pubs.elements-sourcemanual-entryen_US
pubs.notesRevised for CBBen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Psychological & Brain Sciencesen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0002-1478-1237 (Howard, Marc W)
dc.identifier.mycv391631


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