Is cortical connectivity optimized for storing information?
Cortical networks are thought to be shaped by experience-dependent synaptic plasticity. Theoretical studies have shown that synaptic plasticity allows a network to store a memory of patterns of activity such that they become attractors of the dynamics of the network. Here we study the properties of the excitatory synaptic connectivity in a network that maximizes the number of stored patterns of activity in a robust fashion. We show that the resulting synaptic connectivity matrix has the following properties: it is sparse, with a large fraction of zero synaptic weights ('potential' synapses); bidirectionally coupled pairs of neurons are over-represented in comparison to a random network; and bidirectionally connected pairs have stronger synapses on average than unidirectionally connected pairs. All these features reproduce quantitatively available data on connectivity in cortex. This suggests synaptic connectivity in cortex is optimized to store a large number of attractor states in a robust fashion.
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Related Subject Headings
- Neuronal Plasticity
- Neurology & Neurosurgery
- Neural Networks, Computer
- Models, Neurological
- Memory
- Cerebral Cortex
- 5202 Biological psychology
- 3209 Neurosciences
- 1702 Cognitive Sciences
- 1701 Psychology
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Neuronal Plasticity
- Neurology & Neurosurgery
- Neural Networks, Computer
- Models, Neurological
- Memory
- Cerebral Cortex
- 5202 Biological psychology
- 3209 Neurosciences
- 1702 Cognitive Sciences
- 1701 Psychology