NDLI: Defining time in a minimal hippocampal CA3 model by matching time-span of associative synaptic modification and input pattern duration

Content Provider	IEEE Xplore Digital Library
Author	Mitman, K.E. Laurent, P.A. Levy, W.B.
Copyright Year	2003
Description	Author affiliation: Dept. of NeuroSurg., Virginia Univ., Charlottesville, VA, USA (Mitman, K.E.; Laurent, P.A.)
Abstract	This paper quantifies the time shifting of neuronal codes in a sparse, randomly connected neural network model of hippocampal region CA3. As this network is trained to learn a sequence, the neurons that encode portions of this sequence characteristically fire earlier and earlier over the course of training. Here we systematically investigate the effects of the N-methyl-D-aspartate (NMDA)-governed time-span of synaptic associativity on this shifting process and how this time-span interacts with the duration of each successive external input. The results show that there is an interaction between this synaptic time-span and externally applied pattern duration such that the early shifting effect approaches a maximum asymptotically and that this maximum is very nearly produced when the e-fold decay time-span of synaptic associativity is matched to the duration of individual input patterns. The performance of this model as a sequence prediction device varies with the time-span selected. If too long a time-span is used, overly strong attractors evolve and destroy the sequence prediction ability of the network. Local context cell firing-the learned repetitive firing of neurons that code for a specific subsequence - also varies in duration with these two parameters. Importantly, if the associative time-span is matched to the longevity of each individual external pattern and if time-shifting and local context length are normalized by this same external pattern duration, then time-shifting and local context length are constant across simulations with different parameters. This constancy supports the idea that real time can be mapped into a network of McCulloch-Pitts neurons that lack a time scale for excitation and resetting.
Sponsorship	Int. Neural Network Soc. IEEE Neural Networks Soc
Starting Page	1631
Ending Page	1636
File Size	455082
Page Count	6
File Format	PDF
ISBN	0780378989
ISSN	10987576
DOI	10.1109/IJCNN.2003.1223651
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern matching Sequences Neurons Predictive models Fires Context modeling Neurosurgery Educational institutions Art Neural networks
Content Type	Text
Resource Type	Article

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