12,981 research outputs found
High conductance states in a mean field cortical network model
Measured responses from visual cortical neurons show that spike times tend to
be correlated rather than exactly Poisson distributed. Fano factors vary and
are usually greater than 1 due to the tendency of spikes being clustered into
bursts. We show that this behavior emerges naturally in a balanced cortical
network model with random connectivity and conductance-based synapses. We
employ mean field theory with correctly colored noise to describe temporal
correlations in the neuronal activity. Our results illuminate the connection
between two independent experimental findings: high conductance states of
cortical neurons in their natural environment, and variable non-Poissonian
spike statistics with Fano factors greater than 1.Comment: 7 pages, 3 figures, presented at CNS 2003, to be published in
Neurocomputin
Characterizing synaptic conductance fluctuations in cortical neurons and their influence on spike generation
Cortical neurons are subject to sustained and irregular synaptic activity
which causes important fluctuations of the membrane potential (Vm). We review
here different methods to characterize this activity and its impact on spike
generation. The simplified, fluctuating point-conductance model of synaptic
activity provides the starting point of a variety of methods for the analysis
of intracellular Vm recordings. In this model, the synaptic excitatory and
inhibitory conductances are described by Gaussian-distributed stochastic
variables, or colored conductance noise. The matching of experimentally
recorded Vm distributions to an invertible theoretical expression derived from
the model allows the extraction of parameters characterizing the synaptic
conductance distributions. This analysis can be complemented by the matching of
experimental Vm power spectral densities (PSDs) to a theoretical template, even
though the unexpected scaling properties of experimental PSDs limit the
precision of this latter approach. Building on this stochastic characterization
of synaptic activity, we also propose methods to qualitatively and
quantitatively evaluate spike-triggered averages of synaptic time-courses
preceding spikes. This analysis points to an essential role for synaptic
conductance variance in determining spike times. The presented methods are
evaluated using controlled conductance injection in cortical neurons in vitro
with the dynamic-clamp technique. We review their applications to the analysis
of in vivo intracellular recordings in cat association cortex, which suggest a
predominant role for inhibition in determining both sub- and supra-threshold
dynamics of cortical neurons embedded in active networks.Comment: 9 figures, Journal of Neuroscience Methods (in press, 2008
Attentional modulation of firing rate and synchrony in a model cortical network
When attention is directed into the receptive field of a V4 neuron, its
contrast response curve is shifted to lower contrast values (Reynolds et al,
2000, Neuron 26:703). Attention also increases the coherence between neurons
responding to the same stimulus (Fries et al, 2001, Science 291:1560). We
studied how the firing rate and synchrony of a densely interconnected cortical
network varied with contrast and how they were modulated by attention. We found
that an increased driving current to the excitatory neurons increased the
overall firing rate of the network, whereas variation of the driving current to
inhibitory neurons modulated the synchrony of the network. We explain the
synchrony modulation in terms of a locking phenomenon during which the ratio of
excitatory to inhibitory firing rates is approximately constant for a range of
driving current values. We explored the hypothesis that contrast is represented
primarily as a drive to the excitatory neurons, whereas attention corresponds
to a reduction in driving current to the inhibitory neurons. Using this
hypothesis, the model reproduces the following experimental observations: (1)
the firing rate of the excitatory neurons increases with contrast; (2) for high
contrast stimuli, the firing rate saturates and the network synchronizes; (3)
attention shifts the contrast response curve to lower contrast values; (4)
attention leads to stronger synchronization that starts at a lower value of the
contrast compared with the attend-away condition. In addition, it predicts that
attention increases the delay between the inhibitory and excitatory synchronous
volleys produced by the network, allowing the stimulus to recruit more
downstream neurons.Comment: 36 pages, submitted to Journal of Computational Neuroscienc
Dwelling Quietly in the Rich Club: Brain Network Determinants of Slow Cortical Fluctuations
For more than a century, cerebral cartography has been driven by
investigations of structural and morphological properties of the brain across
spatial scales and the temporal/functional phenomena that emerge from these
underlying features. The next era of brain mapping will be driven by studies
that consider both of these components of brain organization simultaneously --
elucidating their interactions and dependencies. Using this guiding principle,
we explored the origin of slowly fluctuating patterns of synchronization within
the topological core of brain regions known as the rich club, implicated in the
regulation of mood and introspection. We find that a constellation of densely
interconnected regions that constitute the rich club (including the anterior
insula, amygdala, and precuneus) play a central role in promoting a stable,
dynamical core of spontaneous activity in the primate cortex. The slow time
scales are well matched to the regulation of internal visceral states,
corresponding to the somatic correlates of mood and anxiety. In contrast, the
topology of the surrounding "feeder" cortical regions show unstable, rapidly
fluctuating dynamics likely crucial for fast perceptual processes. We discuss
these findings in relation to psychiatric disorders and the future of
connectomics.Comment: 35 pages, 6 figure
A mean-field model for conductance-based networks of adaptive exponential integrate-and-fire neurons
Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of
neocortical processing at mesoscopic scales. Since VSDi signals report the
average membrane potential, it seems natural to use a mean-field formalism to
model such signals. Here, we investigate a mean-field model of networks of
Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based
synaptic interactions. The AdEx model can capture the spiking response of
different cell types, such as regular-spiking (RS) excitatory neurons and
fast-spiking (FS) inhibitory neurons. We use a Master Equation formalism,
together with a semi-analytic approach to the transfer function of AdEx
neurons. We compare the predictions of this mean-field model to simulated
networks of RS-FS cells, first at the level of the spontaneous activity of the
network, which is well predicted by the mean-field model. Second, we
investigate the response of the network to time-varying external input, and
show that the mean-field model accurately predicts the response time course of
the population. One notable exception was that the "tail" of the response at
long times was not well predicted, because the mean-field does not include
adaptation mechanisms. We conclude that the Master Equation formalism can yield
mean-field models that predict well the behavior of nonlinear networks with
conductance-based interactions and various electrophysiolgical properties, and
should be a good candidate to model VSDi signals where both excitatory and
inhibitory neurons contribute.Comment: 21 pages, 7 figure
Towards dynamical network biomarkers in neuromodulation of episodic migraine
Computational methods have complemented experimental and clinical
neursciences and led to improvements in our understanding of the nervous
systems in health and disease. In parallel, neuromodulation in form of electric
and magnetic stimulation is gaining increasing acceptance in chronic and
intractable diseases. In this paper, we firstly explore the relevant state of
the art in fusion of both developments towards translational computational
neuroscience. Then, we propose a strategy to employ the new theoretical concept
of dynamical network biomarkers (DNB) in episodic manifestations of chronic
disorders. In particular, as a first example, we introduce the use of
computational models in migraine and illustrate on the basis of this example
the potential of DNB as early-warning signals for neuromodulation in episodic
migraine.Comment: 13 pages, 5 figure
Cortical Spike Synchrony as a Measure of Input Familiarity
J.G.O. was supported by the Ministerio de Economia y Competividad and FEDER (Spain, project FIS2015-66503-C3-1-P) and the ICREA Academia programme. E.U. acknowledges support from the Scottish Universities Life Sciences Alliance (SULSA) and HPC-Europa2.Peer reviewedPostprin
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