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On the Relation between Bursts and Dynamic Synapse Properties: A Modulation-Based Ansatz.

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    ABSTRACT: Dual intracellular recordings from pairs of synaptically connected neurones have demonstrated that the frequency-dependent pattern of transmitter release varies dramatically between different classes of connections. Somewhat surprisingly, these patterns are not determined by the class of neurone supplying the axon alone, but to a large degree by the class of postsynaptic neurone. A wide range of presynaptic mechanisms, some that depress the release of transmitter and others that enhance release have been identified. It is the selective expression of these different mechanisms that determines the unique frequency- and pattern-dependent properties of each class of connection. Although the molecular interactions underlying these several mechanisms have yet to be fully identified, the wealth and complexity of the protein-protein and protein-lipid interactions that have been shown to control the release of transmitter suggest many ways in which the properties of a synapse may be tuned to respond to particular patterns and frequencies.
    Journal of Computational Neuroscience 01/2003; 15(2):159-202. · 2.44 Impact Factor
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    ABSTRACT: Brief bursts of high-frequency action potentials represent a common firing mode of pyramidal neurons, and there are indications that they represent a special neural code. It is therefore of interest to determine whether there are particular spatial and temporal features of neuronal inputs that trigger bursts. Recent work on pyramidal cells indicates that bursts can be initiated by a specific spatial arrangement of inputs in which there is coincident proximal and distal dendritic excitation (Larkum et al., 1999). Here we have used a computational model of an important class of bursting neurons to investigate whether there are special temporal features of inputs that trigger bursts. We find that when a model pyramidal neuron receives sinusoidally or randomly varying inputs, bursts occur preferentially on the positive slope of the input signal. We further find that the number of spikes per burst can signal the magnitude of the slope in a graded manner. We show how these computations can be understood in terms of the biophysical mechanism of burst generation. There are several examples in the literature suggesting that bursts indeed occur preferentially on positive slopes (Guido et al., 1992; Gabbiani et al., 1996). Our results suggest that this selectivity could be a simple consequence of the biophysics of burst generation. Our observations also raise the possibility that neurons use a burst duration code useful for rapid information transmission. This possibility could be further examined experimentally by looking for correlations between burst duration and stimulus variables.
    Journal of Neuroscience 11/2002; 22(20):9053-62. · 6.91 Impact Factor
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    ABSTRACT: We study the effect of competition between short-term synaptic depression and facilitation on the dynamic properties of attractor neural networks, using Monte Carlo simulation and a mean-field analysis. Depending on the balance of depression, facilitation, and the underlying noise, the network displays different behaviors, including associative memory and switching of activity between different attractors. We conclude that synaptic facilitation enhances the attractor instability in a way that (1) intensifies the system adaptability to external stimuli, which is in agreement with experiments, and (2) favors the retrieval of information with less error during short time intervals.
    Neural Computation 11/2007; 19(10):2739-55. · 1.76 Impact Factor

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May 27, 2014