34,001 research outputs found
Effect of noise on neuron transient response
A good approximation to the integrate-and-fire model with diffusive noise can be obtained using a noisy threshold model. This allows the response of a population of noisy neurons to a current transient to be described using a linear filter. Here we apply these analytical results to the peristimulus time histogram (PSTH) of a single neuron. The effect of the noise on the PSTH in our model is similar to that seen in experimental findings of Poliakov et al. (J. Physiol., Part 1,495 (1996) 143–157) on hypoglossal and cat lumbar motoneurons and could help in interpreting their results
Detection of subthreshold pulses in neurons with channel noise
Neurons are subject to various kinds of noise. In addition to synaptic noise,
the stochastic opening and closing of ion channels represents an intrinsic
source of noise that affects the signal processing properties of the neuron. In
this paper, we studied the response of a stochastic Hodgkin-Huxley neuron to
transient input subthreshold pulses. It was found that the average response
time decreases but variance increases as the amplitude of channel noise
increases. In the case of single pulse detection, we show that channel noise
enables one neuron to detect the subthreshold signals and an optimal membrane
area (or channel noise intensity) exists for a single neuron to achieve optimal
performance. However, the detection ability of a single neuron is limited by
large errors. Here, we test a simple neuronal network that can enhance the
pulse detecting abilities of neurons and find dozens of neurons can perfectly
detect subthreshold pulses. The phenomenon of intrinsic stochastic resonance is
also found both at the level of single neurons and at the level of networks. At
the network level, the detection ability of networks can be optimized for the
number of neurons comprising the network.Comment: 14 pages, 9 figure
Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control
It is widely accepted that the complex dynamics characteristic of recurrent
neural circuits contributes in a fundamental manner to brain function. Progress
has been slow in understanding and exploiting the computational power of
recurrent dynamics for two main reasons: nonlinear recurrent networks often
exhibit chaotic behavior and most known learning rules do not work in robust
fashion in recurrent networks. Here we address both these problems by
demonstrating how random recurrent networks (RRN) that initially exhibit
chaotic dynamics can be tuned through a supervised learning rule to generate
locally stable neural patterns of activity that are both complex and robust to
noise. The outcome is a novel neural network regime that exhibits both
transiently stable and chaotic trajectories. We further show that the recurrent
learning rule dramatically increases the ability of RRNs to generate complex
spatiotemporal motor patterns, and accounts for recent experimental data
showing a decrease in neural variability in response to stimulus onset
Stochastic Resonance of Ensemble Neurons for Transient Spike Trains: A Wavelet Analysis
By using the wavelet transformation (WT), we have analyzed the response of an
ensemble of (=1, 10, 100 and 500) Hodgkin-Huxley (HH) neurons to {\it
transient} -pulse spike trains () with independent Gaussian noises.
The cross-correlation between the input and output signals is expressed in
terms of the WT expansion coefficients. The signal-to-noise ratio (SNR) is
evaluated by using the {\it denoising} method within the WT, by which the noise
contribution is extracted from output signals. Although the response of a
single (N=1) neuron to sub-threshold transient signals with noises is quite
unreliable, the transmission fidelity assessed by the cross-correlation and SNR
is shown to be much improved by increasing the value of : a population of
neurons play an indispensable role in the stochastic resonance (SR) for
transient spike inputs. It is also shown that in a large-scale ensemble, the
transmission fidelity for supra-threshold transient spikes is not significantly
degraded by a weak noise which is responsible to SR for sub-threshold inputs.Comment: 20 pages, 4 figure
Synthetic reverberating activity patterns embedded in networks of cortical neurons
Synthetic reverberating activity patterns are experimentally generated by
stimulation of a subset of neurons embedded in a spontaneously active network
of cortical cells in-vitro. The neurons are artificially connected by means of
conditional stimulation matrix, forming a synthetic local circuit with a
predefined programmable connectivity and time-delays. Possible uses of this
experimental design are demonstrated, analyzing the sensitivity of these
deterministic activity patterns to transmission delays and to the nature of
ongoing network dynamics.Comment: 8 pages, 5 figure
Revisiting chaos in stimulus-driven spiking networks: signal encoding and discrimination
Highly connected recurrent neural networks often produce chaotic dynamics,
meaning their precise activity is sensitive to small perturbations. What are
the consequences for how such networks encode streams of temporal stimuli? On
the one hand, chaos is a strong source of randomness, suggesting that small
changes in stimuli will be obscured by intrinsically generated variability. On
the other hand, recent work shows that the type of chaos that occurs in spiking
networks can have a surprisingly low-dimensional structure, suggesting that
there may be "room" for fine stimulus features to be precisely resolved. Here
we show that strongly chaotic networks produce patterned spikes that reliably
encode time-dependent stimuli: using a decoder sensitive to spike times on
timescales of 10's of ms, one can easily distinguish responses to very similar
inputs. Moreover, recurrence serves to distribute signals throughout chaotic
networks so that small groups of cells can encode substantial information about
signals arriving elsewhere. A conclusion is that the presence of strong chaos
in recurrent networks does not prohibit precise stimulus encoding.Comment: 8 figure
Emergence of Synchronous Oscillations in Neural Networks Excited by Noise
The presence of noise in non linear dynamical systems can play a constructive
role, increasing the degree of order and coherence or evoking improvements in
the performance of the system. An example of this positive influence in a
biological system is the impulse transmission in neurons and the
synchronization of a neural network. Integrating numerically the Fokker-Planck
equation we show a self-induced synchronized oscillation. Such an oscillatory
state appears in a neural network coupled with a feedback term, when this
system is excited by noise and the noise strength is within a certain range.Comment: 12 pages, 18 figure
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
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