10,960 research outputs found
Response variability in balanced cortical networks
We study the spike statistics of neurons in a network with dynamically
balanced excitation and inhibition. Our model, intended to represent a generic
cortical column, comprises randomly connected excitatory and inhibitory leaky
integrate-and-fire neurons, driven by excitatory input from an external
population. The high connectivity permits a mean-field description in which
synaptic currents can be treated as Gaussian noise, the mean and
autocorrelation function of which are calculated self-consistently from the
firing statistics of single model neurons. Within this description, we find
that the irregularity of spike trains is controlled mainly by the strength of
the synapses relative to the difference between the firing threshold and the
post-firing reset level of the membrane potential. For moderately strong
synapses we find spike statistics very similar to those observed in primary
visual cortex.Comment: 22 pages, 7 figures, submitted to Neural Computatio
Are the input parameters of white-noise-driven integrate-and-fire neurons uniquely determined by rate and CV?
Integrate-and-fire (IF) neurons have found widespread applications in
computational neuroscience. Particularly important are stochastic versions of
these models where the driving consists of a synaptic input modeled as white
Gaussian noise with mean and noise intensity . Different IF models
have been proposed, the firing statistics of which depends nontrivially on the
input parameters and . In order to compare these models among each
other, one must first specify the correspondence between their parameters. This
can be done by determining which set of parameters (, ) of each model
is associated to a given set of basic firing statistics as, for instance, the
firing rate and the coefficient of variation (CV) of the interspike interval
(ISI). However, it is not clear {\em a priori} whether for a given firing rate
and CV there is only one unique choice of input parameters for each model. Here
we review the dependence of rate and CV on input parameters for the perfect,
leaky, and quadratic IF neuron models and show analytically that indeed in
these three models the firing rate and the CV uniquely determine the input
parameters
Regulation of Irregular Neuronal Firing by Autaptic Transmission
The importance of self-feedback autaptic transmission in modulating
spike-time irregularity is still poorly understood. By using a biophysical
model that incorporates autaptic coupling, we here show that self-innervation
of neurons participates in the modulation of irregular neuronal firing,
primarily by regulating the occurrence frequency of burst firing. In
particular, we find that both excitatory and electrical autapses increase the
occurrence of burst firing, thus reducing neuronal firing regularity. In
contrast, inhibitory autapses suppress burst firing and therefore tend to
improve the regularity of neuronal firing. Importantly, we show that these
findings are independent of the firing properties of individual neurons, and as
such can be observed for neurons operating in different modes. Our results
provide an insightful mechanistic understanding of how different types of
autapses shape irregular firing at the single-neuron level, and they highlight
the functional importance of autaptic self-innervation in taming and modulating
neurodynamics.Comment: 27 pages, 8 figure
Coherent response of the Hodgkin-Huxley neuron in the high-input regime
We analyze the response of the Hodgkin-Huxley neuron to a large number of
uncorrelated stochastic inhibitory and excitatory post-synaptic spike trains.
In order to clarify the various mechanisms responsible for noise-induced spike
triggering we examine the model in its silent regime. We report the coexistence
of two distinct coherence resonances: the first one at low noise is due to the
stimulation of "correlated" subthreshold oscillations; the second one at
intermediate noise variances is instead related to the regularization of the
emitted spike trains.Comment: 5 pages - 5 eps figures, contribution presented to the conference CNS
2006 held in Edinburgh (UK), to appear on Neurocomputin
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
Inhibitory synchrony as a mechanism for attentional gain modulation
Recordings from area V4 of monkeys have revealed that when the focus of
attention is on a visual stimulus within the receptive field of a cortical
neuron, two distinct changes can occur: The firing rate of the neuron can
change and there can be an increase in the coherence between spikes and the
local field potential in the gamma-frequency range (30-50 Hz). The hypothesis
explored here is that these observed effects of attention could be a
consequence of changes in the synchrony of local interneuron networks. We
performed computer simulations of a Hodgkin-Huxley type neuron driven by a
constant depolarizing current, I, representing visual stimulation and a
modulatory inhibitory input representing the effects of attention via local
interneuron networks. We observed that the neuron's firing rate and the
coherence of its output spike train with the synaptic inputs was modulated by
the degree of synchrony of the inhibitory inputs. The model suggest that the
observed changes in firing rate and coherence of neurons in the visual cortex
could be controlled by top-down inputs that regulated the coherence in the
activity of a local inhibitory network discharging at gamma frequencies.Comment: J.Physiology (Paris) in press, 11 figure
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