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Author Koulakov, Alexei ♦ Hromadka, Tomas ♦ Zador, Anthony M.
Source arXiv.org
Content type Text
File Format PDF
Date of Submission 2008-09-09
Language English
Subject Domain (in DDC) Natural sciences & mathematics ♦ Life sciences; biology
Subject Keyword Quantitative Biology - Neurons and Cognition ♦ q-bio
Abstract Two recent experimental observations pose a challenge to many cortical models. First, the activity in the auditory cortex is sparse, and firing rates can be described by a lognormal distribution. Second, the distribution of non-zero synaptic strengths between nearby cortical neurons can also be described by a lognormal distribution. Here we use a simple model of cortical activity to reconcile these observations. The model makes the experimentally testable prediction that synaptic efficacies onto a given cortical neuron are statistically correlated, i.e. it predicts that some neurons receive many more strong connections than other neurons. We propose a simple Hebb-like learning rule which gives rise to both lognormal firing rates and synaptic efficacies. Our results represent a first step toward reconciling sparse activity and sparse connectivity in cortical networks.
Educational Use Research
Learning Resource Type Article


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