Literature DB >> 8521287

Interactions of glutamate and dopamine in a computational model of the striatum.

R Kötter1, J Wickens.   

Abstract

A network model of simplified striatal principal neurons with mutual inhibition was used to investigate possible interactions between cortical glutamatergic and nigral dopaminergic afferents in the neostriatum. Glutamatergic and dopaminergic inputs were represented by an excitatory synaptic conductance and a slow membrane potassium conductance, respectively. Neuronal activity in the model was characterized by episodes of increased action potential firing rates of variable duration and frequency. Autocorrelation histograms constructed from the action potential activity of striatal model neurons showed that reducing peak excitatory conductance had the effect of increasing interspike intervals. On the other hand, the maximum value of the dopamine-sensitive potassium conductance was inversely related to the duration of firing episodes and the maximal firing rates. A smaller potassium conductance restored normal firing rates in the most active neurons at the expense of a larger proportion of neurons showing reduced activity. Thus, a homogeneous network with mutual inhibition can produce equally complex dynamics as have been proposed to occur in a striatal network with two neuron populations that are oppositely regulated by dopamine. Even without mutual inhibition it appears that increased dopamine concentrations could partially compensate for the effects of reduced glutamatergic input in individual neurons.

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Year:  1995        PMID: 8521287     DOI: 10.1007/bf00961434

Source DB:  PubMed          Journal:  J Comput Neurosci        ISSN: 0929-5313            Impact factor:   1.621


  76 in total

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Authors:  J R Wickens; M E Alexander; R Miller
Journal:  Synapse       Date:  1991-05       Impact factor: 2.562

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Journal:  Brain Res       Date:  1988-07-05       Impact factor: 3.252

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Journal:  Neuroscience       Date:  1984-12       Impact factor: 3.590

Review 5.  D1 and D2 dopamine receptor modulation of sodium and potassium currents in rat neostriatal neurons.

Authors:  D J Surmeier; S T Kitai
Journal:  Prog Brain Res       Date:  1993       Impact factor: 2.453

Review 6.  The generation of natural firing patterns in neostriatal neurons.

Authors:  C J Wilson
Journal:  Prog Brain Res       Date:  1993       Impact factor: 2.453

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Journal:  Brain Res       Date:  1982-04-22       Impact factor: 3.252

8.  Electrophysiological characterization of rat striatal neurons in vitro following a unilateral lesion of dopamine cells.

Authors:  M J Twery; L A Thompson; J R Walters
Journal:  Synapse       Date:  1993-04       Impact factor: 2.562

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Authors:  P Calabresi; N B Mercuri; A Stefani; G Bernardi
Journal:  J Neurophysiol       Date:  1990-04       Impact factor: 2.714

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Authors:  Z G Jiang; R A North
Journal:  J Physiol       Date:  1991-11       Impact factor: 5.182

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  10 in total

1.  A predictive reinforcement model of dopamine neurons for learning approach behavior.

Authors:  J L Contreras-Vidal; W Schultz
Journal:  J Comput Neurosci       Date:  1999 May-Jun       Impact factor: 1.621

2.  Striatal mechanism of action of corticoliberin on behavior in dogs in conditions of dopamine deficiency.

Authors:  N L Voilokova; N F Suvorov; V V Rakitskaya; V G Shalyapina
Journal:  Neurosci Behav Physiol       Date:  1999 Nov-Dec

3.  Conditional routing of information to the cortex: a model of the basal ganglia's role in cognitive coordination.

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Journal:  Psychol Rev       Date:  2010-04       Impact factor: 8.934

4.  Computational models of reinforcement learning: the role of dopamine as a reward signal.

Authors:  R D Samson; M J Frank; Jean-Marc Fellous
Journal:  Cogn Neurodyn       Date:  2010-03-21       Impact factor: 5.082

5.  Up and down states in striatal medium spiny neurons simultaneously recorded with spontaneous activity in fast-spiking interneurons studied in cortex-striatum-substantia nigra organotypic cultures.

Authors:  D Plenz; S T Kitai
Journal:  J Neurosci       Date:  1998-01-01       Impact factor: 6.167

6.  Significance of input correlations in striatal function.

Authors:  Man Yi Yim; Ad Aertsen; Arvind Kumar
Journal:  PLoS Comput Biol       Date:  2011-11-17       Impact factor: 4.475

7.  DARPP-32 is a robust integrator of dopamine and glutamate signals.

Authors:  Eric Fernandez; Renaud Schiappa; Jean-Antoine Girault; Nicolas Le Novère
Journal:  PLoS Comput Biol       Date:  2006-11-06       Impact factor: 4.475

8.  A kinetic model of dopamine- and calcium-dependent striatal synaptic plasticity.

Authors:  Takashi Nakano; Tomokazu Doi; Junichiro Yoshimoto; Kenji Doya
Journal:  PLoS Comput Biol       Date:  2010-02-12       Impact factor: 4.475

Review 9.  Solving the Credit Assignment Problem With the Prefrontal Cortex.

Authors:  Alexandra Stolyarova
Journal:  Front Neurosci       Date:  2018-03-27       Impact factor: 4.677

10.  A silent eligibility trace enables dopamine-dependent synaptic plasticity for reinforcement learning in the mouse striatum.

Authors:  Tomomi Shindou; Mayumi Shindou; Sakurako Watanabe; Jeffery Wickens
Journal:  Eur J Neurosci       Date:  2018-04-14       Impact factor: 3.386

  10 in total

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