A Computational Model of the Nucleus Accumbens: Network Properties and their Functional Implications

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Computational Modeling
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Afferent Input
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Schizophrenia

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Wolf, John A.

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The Nucleus accumbens integrates convergent input from a number of limbic structures, and has been implicated in a variety of behavioral disorders including addiction and schizophrenia. The bistable membrane properties of the principal cell in the NAcb, the GABAergic medium spiny projection neuron (MSP), have been proposed to mediate afferent integration. To investigate how intrinsic properties may underlie this mechanism, we constructed a model of an MSP neuron in GENESIS, which preserves the main morphological features and relevant ionichynaptic currents. The model captures the major properties of in vivo neurons, including a non-linear response to the number of afferent inputs. In order to examine network properties of the NAcb and its response to varying patterns of afferent input, a 100- cell network with modifiable levels of gap junctions and GABAergic synaptic connectivity was constructed. Afferent inputs were modeled as Poisson-distributed spike trains. Addition of lateral inhibition in the network led to a decrease in spike output for cells receiving less synchronized input, suggesting that this may be a mechanism for increasing the signal to noise ratio. Dopaminergic modulation of the whole network led to a slight increase in overall synchronization, but did not further segregate cells that were already receiving synchronous input.

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2003-03-20

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2023-05-16T21:44:46.000

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Copyright 2003 IEEE. Reprinted from Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering 2003, pages 214-217. Publisher URL:http://ieeexplore.ieee.org/xpl/tocresult.jsp?isNumber=26900&page=3 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.

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