Literature DB >> 12022505

Synapses as dynamic memory buffers.

Wolfgang Maass1, Henry Markram.   

Abstract

This article throws new light on the possible role of synapses in information transmission through theoretical analysis and computer simulations. We show that the internal dynamic state of a synapse may serve as a transient memory buffer that stores information about the most recent segment of the spike train that was previously sent to this synapse. This information is transmitted to the postsynaptic neuron through the amplitudes of the postsynaptic response for the next few spikes. In fact, we show that most of this information about the preceding spike train is already contained in the postsynaptic response for just two additional spikes. It is demonstrated that the postsynaptic neuron receives simultaneously information about the specific type of synapse which has transmitted these pulses. In view of recent findings by Gupta et al. [Science, 287 (2000) 273] that different types of synapses are characteristic for specific types of presynaptic neurons, the postsynaptic neuron receives in this way partial knowledge about the identity of the presynaptic neuron from which it has received information. Our simulations are based on recent data about the dynamics of GABAergic synapses. We show that the relatively large number of synaptic release sites that make up a GABAergic synaptic connection makes these connections suitable for such complex information transmission processes.

Mesh:

Year:  2002        PMID: 12022505     DOI: 10.1016/s0893-6080(01)00144-7

Source DB:  PubMed          Journal:  Neural Netw        ISSN: 0893-6080


  16 in total

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5.  Developmental shift of short-term synaptic plasticity in cortical organotypic slices.

Authors:  W X Chen; D V Buonomano
Journal:  Neuroscience       Date:  2012-04-19       Impact factor: 3.590

6.  A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity.

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7.  Stochastic computations in cortical microcircuit models.

Authors:  Stefan Habenschuss; Zeno Jonke; Wolfgang Maass
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8.  A novel learning rule for long-term plasticity of short-term synaptic plasticity enhances temporal processing.

Authors:  Tiago P Carvalho; Dean V Buonomano
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9.  Computational aspects of feedback in neural circuits.

Authors:  Wolfgang Maass; Prashant Joshi; Eduardo D Sontag
Journal:  PLoS Comput Biol       Date:  2006-10-24       Impact factor: 4.475

10.  A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback.

Authors:  Robert Legenstein; Dejan Pecevski; Wolfgang Maass
Journal:  PLoS Comput Biol       Date:  2008-10-10       Impact factor: 4.475

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