3,231 research outputs found
Double Inverse Stochastic Resonance with Dynamic Synapses
We investigate the behavior of a model neuron that receives a
biophysically-realistic noisy post-synaptic current based on uncorrelated
spiking activity from a large number of afferents. We show that, with static
synapses, such noise can give rise to inverse stochastic resonance (ISR) as a
function of the presynaptic firing rate. We compare this to the case with
dynamic synapses that feature short-term synaptic plasticity, and show that the
interval of presynaptic firing rate over which ISR exists can be extended or
diminished. We consider both short-term depression and facilitation.
Interestingly, we find that a double inverse stochastic resonance (DISR), with
two distinct wells centered at different presynaptic firing rates, can appear.Comment: 12 pages, 7 figure
Signal processing in local neuronal circuits based on activity-dependent noise and competition
We study the characteristics of weak signal detection by a recurrent neuronal
network with plastic synaptic coupling. It is shown that in the presence of an
asynchronous component in synaptic transmission, the network acquires
selectivity with respect to the frequency of weak periodic stimuli. For
non-periodic frequency-modulated stimuli, the response is quantified by the
mutual information between input (signal) and output (network's activity), and
is optimized by synaptic depression. Introducing correlations in signal
structure resulted in the decrease of input-output mutual information. Our
results suggest that in neural systems with plastic connectivity, information
is not merely carried passively by the signal; rather, the information content
of the signal itself might determine the mode of its processing by a local
neuronal circuit.Comment: 15 pages, 4 pages, in press for "Chaos
Neural networks with dynamical synapses: from mixed-mode oscillations and spindles to chaos
Understanding of short-term synaptic depression (STSD) and other forms of
synaptic plasticity is a topical problem in neuroscience. Here we study the
role of STSD in the formation of complex patterns of brain rhythms. We use a
cortical circuit model of neural networks composed of irregular spiking
excitatory and inhibitory neurons having type 1 and 2 excitability and
stochastic dynamics. In the model, neurons form a sparsely connected network
and their spontaneous activity is driven by random spikes representing synaptic
noise. Using simulations and analytical calculations, we found that if the STSD
is absent, the neural network shows either asynchronous behavior or regular
network oscillations depending on the noise level. In networks with STSD,
changing parameters of synaptic plasticity and the noise level, we observed
transitions to complex patters of collective activity: mixed-mode and spindle
oscillations, bursts of collective activity, and chaotic behaviour.
Interestingly, these patterns are stable in a certain range of the parameters
and separated by critical boundaries. Thus, the parameters of synaptic
plasticity can play a role of control parameters or switchers between different
network states. However, changes of the parameters caused by a disease may lead
to dramatic impairment of ongoing neural activity. We analyze the chaotic
neural activity by use of the 0-1 test for chaos (Gottwald, G. & Melbourne, I.,
2004) and show that it has a collective nature.Comment: 7 pages, Proceedings of 12th Granada Seminar, September 17-21, 201
Can intrinsic noise induce various resonant peaks?
We theoretically describe how weak signals may be efficiently transmitted
throughout more than one frequency range in noisy excitable media by kind of
stochastic multiresonance. This serves us here to reinterpret recent
experiments in neuroscience, and to suggest that many other systems in nature
might be able to exhibit several resonances. In fact, the observed behavior
happens in our (network) model as a result of competition between (1) changes
in the transmitted signals as if the units were varying their activation
threshold, and (2) adaptive noise realized in the model as rapid
activity-dependent fluctuations of the connection intensities. These two
conditions are indeed known to characterize heterogeneously networked systems
of excitable units, e.g., sets of neurons and synapses in the brain. Our
results may find application also in the design of detector devices.Comment: 10 pages, 2 figure
Mammalian Brain As a Network of Networks
Acknowledgements AZ, SG and AL acknowledge support from the Russian Science Foundation (16-12-00077). Authors thank T. Kuznetsova for Fig. 6.Peer reviewedPublisher PD
Effects of Noise in a Cortical Neural Model
Recently Segev et al. (Phys. Rev. E 64,2001, Phys.Rev.Let. 88, 2002) made
long-term observations of spontaneous activity of in-vitro cortical networks,
which differ from predictions of current models in many features. In this paper
we generalize the EI cortical model introduced in a previous paper (S.Scarpetta
et al. Neural Comput. 14, 2002), including intrinsic white noise and analyzing
effects of noise on the spontaneous activity of the nonlinear system, in order
to account for the experimental results of Segev et al.. Analytically we can
distinguish different regimes of activity, depending from the model parameters.
Using analytical results as a guide line, we perform simulations of the
nonlinear stochastic model in two different regimes, B and C. The Power
Spectrum Density (PSD) of the activity and the Inter-Event-Interval (IEI)
distributions are computed, and compared with experimental results. In regime B
the network shows stochastic resonance phenomena and noise induces aperiodic
collective synchronous oscillations that mimic experimental observations at 0.5
mM Ca concentration. In regime C the model shows spontaneous synchronous
periodic activity that mimic activity observed at 1 mM Ca concentration and the
PSD shows two peaks at the 1st and 2nd harmonics in agreement with experiments
at 1 mM Ca. Moreover (due to intrinsic noise and nonlinear activation function
effects) the PSD shows a broad band peak at low frequency. This feature,
observed experimentally, does not find explanation in the previous models.
Besides we identify parametric changes (namely increase of noise or decreasing
of excitatory connections) that reproduces the fading of periodicity found
experimentally at long times, and we identify a way to discriminate between
those two possible effects measuring experimentally the low frequency PSD.Comment: 25 pages, 10 figures, to appear in Phys. Rev.
Clique of functional hubs orchestrates population bursts in developmentally regulated neural networks
It has recently been discovered that single neuron stimulation can impact
network dynamics in immature and adult neuronal circuits. Here we report a
novel mechanism which can explain in neuronal circuits, at an early stage of
development, the peculiar role played by a few specific neurons in
promoting/arresting the population activity. For this purpose, we consider a
standard neuronal network model, with short-term synaptic plasticity, whose
population activity is characterized by bursting behavior. The addition of
developmentally inspired constraints and correlations in the distribution of
the neuronal connectivities and excitabilities leads to the emergence of
functional hub neurons, whose stimulation/deletion is critical for the network
activity. Functional hubs form a clique, where a precise sequential activation
of the neurons is essential to ignite collective events without any need for a
specific topological architecture. Unsupervised time-lagged firings of
supra-threshold cells, in connection with coordinated entrainments of
near-threshold neurons, are the key ingredients to orchestrateComment: 39 pages, 15 figures, to appear in PLOS Computational Biolog
Critical dynamics in homeostatic memory networks
Critical behavior in neural networks characterized by scale-free event distributions and brought about by self-regulatory mechanisms such as short-term synaptic dynamics or homeostatic plasticity, is believed to optimize sensitivity to input and information transfer in the system. Although theoretical predictions of the spike distributions have been confirmed by in-vitro experiments, in-vivo data yield a more complex picture which might be due to the in-homogeneity of the network structure, leakage in currents or massive driving inputs which has so far not been comprehensively covered by analytical or numerical studies.

We address these questions by the study of a neural model of memory that allows for storage and retrieval of patterns and for recombining such patterns as needed for search in problem solving. The model features critical dynamics in the neural assembly as a result of the interplay of synaptic depression and facilitation (Levina e.a 2007, 2009). Model simulations show that the prolonged consolidation of memory patterns induces a bias towards the memories which affects the scale-free spike-frequency distribution. However, selective modification of neuronal circuitry in the form of controlled homeostatic regulation in the form of recalibration of the synaptic weights towards the critical value preserved criticality although characterized by fluctuations between learned random patterns, as observed by the dynamics of stored pattern retrieval quality. The resulting spike statistics depends on the assumed coding scheme, but even sparse or orthogonal memory patterns introduce a typical event size which is incompatible with critical dynamics below the maximal memory capacity. Specifically results obtained for de-correlated patterns show an immediate jump from the sub-critical regime to a state of super-criticality in contrast to a more structured wave-like formation in the avalanche dynamics obtained from a general set of random patterns, pointing towards an eventual evolution of the network connectivity and the optimization of the critical regime. Specifically results obtained for de-correlated patterns show an immediate jump from the sub-critical regime to a state of super-criticality in contrast to a more structured wave-like formation in the avalanche dynamics obtained from a general set of random patterns, pointing towards an eventual evolution of the network connectivity and the optimization of the critical regime (Pearlmutter and Houghton, 2009).

The combination of memory and ongoing dynamics in the model was chosen for its implications in the context of cognitive aging. Following the paradigm of aging as a multi-criteria optimization process, we posit aging effects as a result of an increasing incompatibility of learning goals. In aging, a shift from fluid intelligence (flexibility to recombine memory content) towards crystalline intelligence (optimal memory organization) appears as a lifelong trend against the general decrease of resources. We show that in young age memory and criticality can be maintained simultaneously by a homeostatic leveling of the synaptic conductances. This balance is lost in the aging brain where the memory attractors cannot be kept sufficiently shallow due to neural and synaptic loss, a reduction of activity while experiencing a growth in memories. The value of the memory organization is therefore protected on the cost of the partial loss of the capability of recombining memory patterns in a task-dependent way
Spatially structured oscillations in a two-dimensional excitatory neuronal network with synaptic depression
We study the spatiotemporal dynamics of a two-dimensional excitatory neuronal network with synaptic depression. Coupling between populations of neurons is taken to be nonlocal, while depression is taken to be local and presynaptic. We show that the network supports a wide range of spatially structured oscillations, which are suggestive of phenomena seen in cortical slice experiments and in vivo. The particular form of the oscillations depends on initial conditions and the level of background noise. Given an initial, spatially localized stimulus, activity evolves to a spatially localized oscillating core that periodically emits target waves. Low levels of noise can spontaneously generate several pockets of oscillatory activity that interact via their target patterns. Periodic activity in space can also organize into spiral waves, provided that there is some source of rotational symmetry breaking due to external stimuli or noise. In the high gain limit, no oscillatory behavior exists, but a transient stimulus can lead to a single, outward propagating target wave
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