54 research outputs found
Death and rebirth of neural activity in sparse inhibitory networks
In this paper, we clarify the mechanisms underlying a general phenomenon
present in pulse-coupled heterogeneous inhibitory networks: inhibition can
induce not only suppression of the neural activity, as expected, but it can
also promote neural reactivation. In particular, for globally coupled systems,
the number of firing neurons monotonically reduces upon increasing the strength
of inhibition (neurons' death). However, the random pruning of the connections
is able to reverse the action of inhibition, i.e. in a sparse network a
sufficiently strong synaptic strength can surprisingly promote, rather than
depress, the activity of the neurons (neurons' rebirth). Thus the number of
firing neurons reveals a minimum at some intermediate synaptic strength. We
show that this minimum signals a transition from a regime dominated by the
neurons with higher firing activity to a phase where all neurons are
effectively sub-threshold and their irregular firing is driven by current
fluctuations. We explain the origin of the transition by deriving an analytic
mean field formulation of the problem able to provide the fraction of active
neurons as well as the first two moments of their firing statistics. The
introduction of a synaptic time scale does not modify the main aspects of the
reported phenomenon. However, for sufficiently slow synapses the transition
becomes dramatic, the system passes from a perfectly regular evolution to an
irregular bursting dynamics. In this latter regime the model provides
predictions consistent with experimental findings for a specific class of
neurons, namely the medium spiny neurons in the striatum.Comment: 19 pages, 10 figures, submitted to NJ
Unfolding times for proteins in a force clamp
The escape process from the native valley for proteins subjected to a
constant stretching force is examined using a model for a Beta-barrel. For a
wide range of forces, the unfolding dynamics can be treated as one-dimensional
diffusion, parametrized in terms of the end-to-end distance. In particular, the
escape times can be evaluated as first passage times for a Brownian particle
moving on the protein free-energy landscape, using the Smoluchowski equation.
At strong forces, the unfolding process can be viewed as a diffusive drift away
from the native state, while at weak forces thermal activation is the relevant
mechanism. An escape-time analysis within this approach reveals a crossover
from an exponential to an inverse Gaussian escape-time distribution upon
passing from weak to strong forces. Moreover, a single expression valid at weak
and strong forces can be devised both for the average unfolding time as well as
for the corresponding variance. The analysis offers a possible explanation of
recent experimental findings for ddFLN4 and ubiquitin.Comment: 6 pages, 4 figures, submitted for pubblication to Physical Review
Letter
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
Reconstructing the free energy landscape of a mechanically unfolded model protein
The equilibrium free energy landscape of an off-lattice model protein as a
function of an internal (reaction) coordinate is reconstructed from
out-of-equilibrium mechanical unfolding manipulations. This task is
accomplished via two independent methods: by employing an extended version of
the Jarzynski equality (EJE) and the protein inherent structures (ISs). In a
range of temperatures around the ``folding transition'' we find a good
quantitative agreement between the free energies obtained via EJE and IS
approaches. This indicates that the two methodologies are consistent and able
to reproduce equilibrium properties of the examined system. Moreover, for the
studied model the structural transitions induced by pulling can be related to
thermodynamical aspects of folding
Coherence resonance due to correlated noise in neuronal models
We study the regularity of noise-induced excitations in the FitzHugh-Nagumo (FHN) neuronal model subject to excitatory and inhibitory high-frequency input with and without correlations. For each value of the correlation a relative maximum of spike coherence can be observed for intermediate noise strengths (coherence resonance). Moreover, the FHN system exhibits an absolute maximum of coherent spiking for intermediate values of both the noise amplitude and the strength of correlation (double coherence resonance). The underlying mechanisms can be explained by means of the discrete input statistics
Collective behavior of heterogeneous neural networks
We investigate a network of integrate-and-fire neurons characterized by a
distribution of spiking frequencies. Upon increasing the coupling strength, the
model exhibits a transition from an asynchronous regime to a nontrivial
collective behavior. At variance with the Kuramoto model, (i) the macroscopic
dynamics is irregular even in the thermodynamic limit, and (ii) the microscopic
(single-neuron) evolution is linearly stable.Comment: 4 pages, 5 figure
Double coherence resonance in neuron models driven by discrete correlated noise
We study the influence of correlations among discrete stochastic excitatory
or inhibitory inputs on the response of the FitzHugh-Nagumo neuron model. For
any level of correlation the emitted signal exhibits at some finite noise
intensity a maximal degree of regularity, i.e., a coherence resonance.
Furthermore, for either inhibitory or excitatory correlated stimuli a {\it
Double Coherence Resonance} (DCR) is observable. DCR refers to a (absolute)
maximum coherence in the output occurring for an optimal combination of noise
variance and correlation. All these effects can be explained by taking
advantage of the discrete nature of the correlated inputs.Comment: 4 pages, 3 figures in eps, to appear in Physical Review Letter
Synchronous dynamics in the presence of short-term plasticity
We investigate the occurrence of quasisynchronous events in a random network of excitatory leaky integrate-and-fire neurons equipped with short-term plasticity. The dynamics is analyzed by monitoring both the evolution of global synaptic variables and, on a microscopic ground, the interspike intervals of the individual neurons. We find that quasisynchronous events are the result of a mixture of synchronized and unsynchronized motion, analogously to the emergence of synchronization in the Kuramoto model. In the present context, disorder is due to the random structure of the network and thereby vanishes for a diverging network size (i.e., in the thermodynamic limit), when statistical fluctuations become negligible. Remarkably, the fraction of asynchronous neurons remains strictly larger than zero for arbitrarily large . This is due to the presence of a robust homoclinic cycle in the self-generated synchronous dynamics. The nontrivial large- behavior is confirmed by the anomalous scaling of the maximum Lyapunov exponent, which is strictly positive in a finite network and decreases as {N}^{\ensuremath{-}0.27}. Finally, we have checked the robustness of this dynamical phase with respect to the addition of noise, applied to either the reset potential or the leaky current
Death and rebirth of neural activity in sparse inhibitory networks
Inhibition is a key aspect of neural dynamics playing a fundamental role for the emergence of neural rhythms and the implementation of various information coding strategies. Inhibitory populations are present in several brain structures, and the comprehension of their dynamics is strategical for the understanding of neural processing. In this paper, we clarify the mechanisms underlying a general phenomenon present in pulse-coupled heterogeneous inhibitory networks: inhibition can induce not only suppression of neural activity, as expected, but can also promote neural re-activation. In particular, for globally coupled systems, the number of firing neurons monotonically reduces upon increasing the strength of inhibition (neuronal death). However, the random pruning of connections is able to reverse the action of inhibition, i.e. in a random sparse network a sufficiently strong synaptic strength can surprisingly promote, rather than depress, the activity of neurons (neuronal rebirth). Thus, the number of firing neurons reaches a minimum value at some intermediate synaptic strength. We show that this minimum signals a transition from a regime dominated by neurons with a higher firing activity to a phase where all neurons are effectively sub-threshold and their irregular firing is driven by current fluctuations. We explain the origin of the transition by deriving a mean field formulation of the problem able to provide the fraction of active neurons as well as the first two moments of their firing statistics. The introduction of a synaptic time scale does not modify the main aspects of the reported phenomenon. However, for sufficiently slow synapses the transition becomes dramatic, and the system passes from a perfectly regular evolution to irregular bursting dynamics. In this latter regime the model provides predictions consistent with experimental findings for a specific class of neurons, namely the medium spiny neurons in the striatum
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