ArticleJournal of mathematical neuroscience2016
Responses of Leaky Integrate-and-Fire Neurons to a Plurality of Stimuli in Their Receptive Fields.
Article in Journal of mathematical neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Neural decoding with visual attention using sequential Monte Carlo for leaky integrate-and-fire neurons.PloS one · 2019Article
- Attentional weights in vision as products of spatial and nonspatial components.Psychonomic bulletin & review · 2018Article
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Authors and funding
3 authors.
Funding
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Abstract
A fundamental question concerning the way the visual world is represented in our brain is how a cortical cell responds when its classical receptive field contains a plurality of stimuli. Two opposing models have been proposed. In the response-averaging model, the neuron responds with a weighted average of all individual stimuli. By contrast, in the probability-mixing model, the cell responds to a plurality of stimuli as if only one of the stimuli were present. Here we apply the probability-mixing and the response-averaging model to leaky integrate-and-fire neurons, to describe neuronal behavior based on observed spike trains. We first estimate the parameters of either model using numerical methods, and then test which model is most likely to have generated the observed data. Results show that the parameters can be successfully estimated and the two models are distinguishable using model selection.
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