Effects of noise in a cortical neural model (original) (raw)
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Noise and periodic modulations in neural excitable media
Physical Review E Statistical Physics Plasmas Fluids and Related Interdisciplinary Topics, 1999
We have analyzed the interplay between noise and periodic modulations in a mean field model of a neural excitable medium. For this purpose, we have considered two types of modulations, namely, variations of the resistance and oscillations of the threshold. In both cases, stochastic resonance is present, irrespective of whether the system is monostable or bistable.
Inhibition and modulation of rhythmic neuronal spiking by noise
Physical Review E, 2009
We investigated the effects of noise on periodic firing in the Hodgkin-Huxley nonlinear system. With mean input current as a bifurcation parameter, a bifurcation to repetitive spiking occurs at a critical value c Ϸ 6.44. The firing behavior was studied as a function of the mean and variance of the input current, firstly with initial resting conditions. Noise of a small amplitude can turn off the spiking for values of close to c , and the number of spikes undergoes a minimum as a function of the noise level. The robustness of these phenomena was confirmed by simulations with random initial conditions and with random time of commencement of the noise. Furthermore, their generality was indicated by their occurrence when additive noise was replaced by conductance-based noise. For long periods of observation, many frequent transitions may occur from spiking to nonspiking activity when the noise is sufficiently strong. Explanations of the above phenomena are sought in terms of the underlying bifurcation structure and the probabilities that noise shifts the process from the basin of attraction of a stable limit cycle to that of a stable rest state. The waiting times for such transitions depend strongly on the values of and and on the forms of the basins of attraction. The observed effects of noise are expected to occur in diverse fields in systems with the same underlying dynamical structure.
Stochastic resonance in mammalian neuronal networks
Chaos: An Interdisciplinary Journal of Nonlinear Science, 1998
We present stochastic resonance observed in the dynamics of neuronal networks from mammalian brain. Both sinusoidal signals and random noise were superimposed into an applied electric field. As the amplitude of the noise component was increased, an optimization ͑increase then decrease͒ in the signal-to-noise ratio of the network response to the sinusoidal signal was observed. The relationship between the measures used to characterize the dynamics is discussed. Finally, a computational model of these neuronal networks that includes the neuronal interactions with the electric field is presented to illustrate the physics behind the essential features of the experiment.
Stochastic resonance in neuron models
Journal of Statistical Physics, 1993
Periodically stimulated sensory neurons typically exhibit a kind of "statistical phase locking" to the stimulus: they tend to fire at a preferred phase of the stimulus cycle, but not at every cycle. Hence, the histogram of interspike intervals (ISIH), i.e., of times between successive firings, is multimodal for these neurons, with peaks centered at integer multiples of the driving period. A particular kind of residence time histogram for a large class of noisy bistable systems has recently been shown to exhibit the major features of the neural data. In the present paper, we show that an excitable cell model, the Fitzhugh-Nagumo equations, also reproduces these features when driven by additive periodic and stochastic forces. This model exhibits its own brand of stochastic resonance as the peaks of the ISIH successively go through a maximum when the noise intensity is increased. Further, the presence of a noise-induced limit cycle introduces a third time scale in the problem. This limit cycle is found to modify qualitatively the phase-locking picture, e.g., by suppressing certain peaks in the IS1H. Finally, the role of noise and possibly of stochastic resonance (SR) in the neural encoding of sensory information is discussed.
Naturwissenschaften, 2009
The effects of noise on neuronal dynamical systems are of much current interest. Here, we investigate noise-induced changes in the rhythmic firing activity of single Hodgkin-Huxley neurons. With additive input current, there is, in the absence of noise, a critical mean value µ=µ c above which sustained periodic firing occurs. With initial conditions as resting values, for a range of values of the mean µ near the critical value, we have found that the firing rate is greatly reduced by noise, even of quite small amplitudes. Furthermore, the firing rate may undergo a pronounced minimum as the noise increases. This behavior has the opposite character to stochastic resonance and coherence resonance. We found that these phenomena occurred even when the initial conditions were chosen randomly or when the noise was switched on at a random time, indicating the robustness of the results. We also examined the effects of conductance-based noise on Hodgkin-Huxley neurons and obtained similar results, leading to the conclusion that the phenomena occur across a wide range of neuronal dynamical systems. Further, these phenomena will occur in diverse applications where a stable limit cycle coexists with a stable focus.
Journal of Neurophysiology, 2009
There is great interest in the role of coherent oscillations in the brain. In some cases, high-frequency oscillations (HFOs) are integral to normal brain function, whereas at other times they are implicated as markers of epileptic tissue. Mechanisms underlying HFO generation, especially in abnormal tissue, are not well understood. Using a physiological computer model of hippocampus, we investigate random synaptic activity (noise) as a potential initiator of HFOs. We explore parameters necessary to produce these oscillations and quantify the response using the tools of stochastic resonance (SR) and coherence resonance (CR). As predicted by SR, when noise was added to the network the model was able to detect a subthreshold periodic signal. Addition of basket cell interneurons produced two novel SR effects: 1) improved signal detection at low noise levels and 2) formation of coherent oscillations at high noise that were entrained to harmonics of the signal frequency. The periodic signal was then removed to study oscillations generated only by noise. The combined effects of network coupling and synaptic noise produced coherent, periodic oscillations within the network, an example of CR. Our results show that, under normal coupling conditions, synaptic noise was able to produce gamma (30-100 Hz) frequency oscillations. Synaptic noise generated HFOs in the ripple range (100-200 Hz) when the network had parameters similar to pathological findings in epilepsy: increased gap junctions or recurrent synaptic connections, loss of inhibitory interneurons such as basket cells, and increased synaptic noise. The model parameters that generated these effects are comparable with published experimental data. We propose that increased synaptic noise and physiological coupling mechanisms are sufficient to generate gamma oscillations and that pathologic changes in noise and coupling similar to those in epilepsy can produce abnormal ripples.
Frequency characteristics and intrinsic oscillations in a neuronal network
The phenomena of frequency sensitivity in weak signal detection and the 40 Hz oscillation in a neuronal network have been interpreted based on the intrinsic oscillations of the system. There exists a most sensitive frequency range of 20-60 Hz, over which the signal-to-noise ratio has a large value. This results from the resonance between the subthreshold intrinsic oscillation and the periodic signal. The network can exhibit the synchronous 40 Hz oscillation only with constant bias, which is due to the intrinsic features of neurons and long-range interactions between them. q
Correlation Detection and Resonance in Neural Systems with Distributed Noise Sources
Physical Review Letters, 2001
We investigated the resonance behavior in model neurons receiving a large number of random synaptic inputs, whose distributed nature permits one to introduce correlations between them and investigate its effect on cellular responsiveness. A change in the strength of this background led to enhanced responsiveness, consistent with stochastic resonance. Altering the correlation revealed a type of resonance behavior in which the neuron is sensitive to statistical properties rather than the strength of the noise. Remarkably, the neuron could detect weak correlations among the distributed inputs within millisecond time scales.
Stochastic Resonance in a Neuronal Network from Mammalian Brain
Physical Review Letters, 1996
Stochastic resonance, a nonlinear phenomenon in which random noise optimizes a system's response to a signal, has been postulated to provide a role for noise in information processing in the brain. In these experiments, a time varying electric field was used to deliver both signal and noise directly to a network of neurons from mammalian brain. As the magnitude of the stochastic component of the field was increased, resonance was observed in the response of the neuronal network to a weak periodic signal. This is the first demonstration of stochastic resonance in neuronal networks from the brain.