1,663 research outputs found

    Multiple firing coherence resonances in excitatory and inhibitory coupled neurons

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    The impact of inhibitory and excitatory synapses in delay-coupled Hodgkin--Huxley neurons that are driven by noise is studied. If both synaptic types are used for coupling, appropriately tuned delays in the inhibition feedback induce multiple firing coherence resonances at sufficiently strong coupling strengths, thus giving rise to tongues of coherency in the corresponding delay-strength parameter plane. If only inhibitory synapses are used, however, appropriately tuned delays also give rise to multiresonant responses, yet the successive delays warranting an optimal coherence of excitations obey different relations with regards to the inherent time scales of neuronal dynamics. This leads to denser coherence resonance patterns in the delay-strength parameter plane. The robustness of these findings to the introduction of delay in the excitatory feedback, to noise, and to the number of coupled neurons is determined. Mechanisms underlying our observations are revealed, and it is suggested that the regularity of spiking across neuronal networks can be optimized in an unexpectedly rich variety of ways, depending on the type of coupling and the duration of delays.Comment: 7 two-column pages, 6 figures; accepted for publication in Communications in Nonlinear Science and Numerical Simulatio

    Transient Information Flow in a Network of Excitatory and Inhibitory Model Neurons: Role of Noise and Signal Autocorrelation

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    We investigate the performance of sparsely-connected networks of integrate-and-fire neurons for ultra-short term information processing. We exploit the fact that the population activity of networks with balanced excitation and inhibition can switch from an oscillatory firing regime to a state of asynchronous irregular firing or quiescence depending on the rate of external background spikes. We find that in terms of information buffering the network performs best for a moderate, non-zero, amount of noise. Analogous to the phenomenon of stochastic resonance the performance decreases for higher and lower noise levels. The optimal amount of noise corresponds to the transition zone between a quiescent state and a regime of stochastic dynamics. This provides a potential explanation on the role of non-oscillatory population activity in a simplified model of cortical micro-circuits.Comment: 27 pages, 7 figures, to appear in J. Physiology (Paris) Vol. 9

    Attractors and noise: Twin drivers of decisions and multistability

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    Abstract Perceptual decisions are made not only during goal-directed behavior such as choice tasks, but also occur spontaneously while multistable stimuli are being viewed. In both contexts, the formation of a perceptual decision is best captured by noisy attractor dynamics. Noise-driven attractor transitions can accommodate a wide range of timescales and a hierarchical arrangement with "nested attractors" harbors even more dynamical possibilities. The attractor framework seems particularly promising for understanding higher-level mental states that combine heterogeneous information from a distributed set of brain areas

    Noise and vestibular perception of passive self-motion

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    Noise defined as random disturbances is ubiquitous in both the external environment and the nervous system. Depending on the context, noise can degrade or improve information processing and performance. In all cases, it contributes to neural systems dynamics. We review some effects of various sources of noise on the neural processing of self-motion signals at different stages of the vestibular pathways and the resulting perceptual responses. Hair cells in the inner ear reduce the impact of noise by means of mechanical and neural filtering. Hair cells synapse on regular and irregular afferents. Variability of discharge (noise) is low in regular afferents and high in irregular units. The high variability of irregular units provides information about the envelope of naturalistic head motion stimuli. A subset of neurons in the vestibular nuclei and thalamus are optimally tuned to noisy motion stimuli that reproduce the statistics of naturalistic head movements. In the thalamus, variability of neural discharge increases with increasing motion amplitude but saturates at high amplitudes, accounting for behavioral violation of Weber’s law. In general, the precision of individual vestibular neurons in encoding head motion is worse than the perceptual precision measured behaviorally. However, the global precision predicted by neural population codes matches the high behavioral precision. The latter is estimated by means of psychometric functions for detection or discrimination of whole-body displacements. Vestibular motion thresholds (inverse of precision) reflect the contribution of intrinsic and extrinsic noise to perception. Vestibular motion thresholds tend to deteriorate progressively after the age of 40 years, possibly due to oxidative stress resulting from high discharge rates and metabolic loads of vestibular afferents. In the elderly, vestibular thresholds correlate with postural stability: the higher the threshold, the greater is the postural imbalance and risk of falling. Experimental application of optimal levels of either galvanic noise or whole-body oscillations can ameliorate vestibular function with a mechanism reminiscent of stochastic resonance. Assessment of vestibular thresholds is diagnostic in several types of vestibulopathies, and vestibular stimulation might be useful in vestibular rehabilitation

    Neurocognitive Informatics Manifesto.

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    Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

    Clique of functional hubs orchestrates population bursts in developmentally regulated neural networks

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    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
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