Nov 7, 2018

Exponentially-decaying temporal integration in a network model can explain much of the neural sensitivity to stimulus history in primary auditory cortex.

BioRxiv : the Preprint Server for Biology
Monzilur RahmanNicol S. Harper


Auditory neurons encode stimulus history, which is often modelled using a span of time-delays in a spectro-temporal receptive field (STRF). We propose an alternative model for the encoding of stimulus history, which we apply to extracellular recordings of neurons in the primary auditory cortex of anaesthetized ferrets. For a linear-non-linear STRF model (LN model) to achieve a high level of performance in predicting single unit neural responses to natural sounds in the primary auditory cortex, we found that it is necessary to include time delays going back at least 200 ms in the past. This is an unrealistic time span for biological delay lines. We therefore asked how much of this dependence on stimulus history can instead be explained by dynamical aspects of neurons. We constructed a neural-network model whose output is the weighted sum of units whose responses are determined by a dynamic firing-rate equation. The dynamic aspect performs low-pass filtering on each unit’s response, providing an exponentially decaying memory whose time constant is individual to each unit. We find that this dynamic network (DNet) model, when fitted to the neural data using STRFs of only 25 ms duration, can achieve prediction performance on a held-...Continue Reading

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Mentioned in this Paper

Dysembryoplastic Neuroepithelial Tumor
Neural Network Simulation
Lymph Nodes
Receptive Field
Cerebral Cortex

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