Improving Associative Memory in a Network of Spiking Neurons

Hunter, Russell; Cobb, Stuart; Graham, Bruce

doi:10.1007/978-3-540-87559-8_66

Cited by 5 publications

(1 citation statement)

References 94 publications

(187 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many computational models have been advanced over the years trying to understand how memories are stored and retrieved in the hippocampus [12], [14], [23], [21], [6], [5], [9].…”

Section: Introductionmentioning

confidence: 99%

Encoding and Retrieval in a CA1 Microcircuit Model of the Hippocampus

Cutsuridis

Cobb

Graham

Artificial Neural Networks - ICANN 2008

Self Cite

View full text Add to dashboard Cite

Recent years have witnessed a dramatic accumulation of knowledge about the morphological, physiological and molecular characteristics, as well as connectivity and synaptic properties of neurons in the mammalian hippocampus. Despite these advances, very little insight has been gained into the computational function of the different neuronal classes; in particular, the role of the various inhibitory interneurons in encoding and retrieval of information remains elusive. Mathematical and computational models of microcircuits play an instrumental role in exploring microcircuit functions and facilitate the dissection of operations performed by diverse inhibitory interneurons. A model of the CA1 microcircuitry is presented using biophysical representations of its major cell types: pyramidal, basket, axo-axonic, bistratified and oriens lacunosummoleculare cells. Computer simulations explore the biophysical mechanisms by which encoding and retrieval of spatio-temporal input patterns are achieved by the CA1 microcircuitry. The model proposes functional roles for the different classes of inhibitory interneurons in the encoding and retrieval cycles.

show abstract

“…Many computational models have been advanced over the years trying to understand how memories are stored and retrieved in the hippocampus [12], [14], [23], [21], [6], [5], [9].…”

Section: Introductionmentioning

confidence: 99%