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Noise in IntegrateandFire Neurons: From Stochastic Input to Escape Rates
 TO APPEAR IN NEURAL COMPUTATION.
, 1999
"... We analyze the effect of noise in integrateandfire neurons driven by timedependent input, and compare the diffusion approximation for the membrane potential to escape noise. It is shown that for timedependent subthreshold input, diffusive noise can be replaced by escape noise with a hazard funct ..."
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Cited by 49 (6 self)
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We analyze the effect of noise in integrateandfire neurons driven by timedependent input, and compare the diffusion approximation for the membrane potential to escape noise. It is shown that for timedependent subthreshold input, diffusive noise can be replaced by escape noise with a hazard
Fast global oscillations in networks of integrateandfire neurons with low firing rates
 Neural Computation
, 1999
"... We study analytically the dynamics of a network of sparsely connected inhibitory integrateandfire neurons in a regime where individual neurons emit spikes irregularly and at a low rate. In the limit when the number of neurons N → ∞, the network exhibits a sharp transition between a stationary and ..."
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Cited by 226 (18 self)
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We study analytically the dynamics of a network of sparsely connected inhibitory integrateandfire neurons in a regime where individual neurons emit spikes irregularly and at a low rate. In the limit when the number of neurons N → ∞, the network exhibits a sharp transition between a stationary
Escape Rate Models for Noisy IntegrateandFire Neurons
, 1999
"... The noisy integrateandre neuron is diÆcult to treat analytically. In particular, interspikeinterval densities have to be computed numerically. We compare here the noisy integrateandre neuron with three escape noise models for neuronal spiking which can be solved analytically. We show that an es ..."
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Cited by 1 (0 self)
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The noisy integrateandre neuron is diÆcult to treat analytically. In particular, interspikeinterval densities have to be computed numerically. We compare here the noisy integrateandre neuron with three escape noise models for neuronal spiking which can be solved analytically. We show
for integrateandfire neurons
"... The most likely voltage path and large deviations approximations ..."
Contour Tracking By Stochastic Propagation of Conditional Density
, 1996
"... . In Proc. European Conf. Computer Vision, 1996, pp. 343356, Cambridge, UK The problem of tracking curves in dense visual clutter is a challenging one. Trackers based on Kalman filters are of limited use; because they are based on Gaussian densities which are unimodal, they cannot represent s ..."
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Cited by 658 (24 self)
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simultaneous alternative hypotheses. Extensions to the Kalman filter to handle multiple data associations work satisfactorily in the simple case of point targets, but do not extend naturally to continuous curves. A new, stochastic algorithm is proposed here, the Condensation algorithm  Conditional
Predictive reward signal of dopamine neurons
 Journal of Neurophysiology
, 1998
"... Schultz, Wolfram. Predictive reward signal of dopamine neurons. is called rewards, which elicit and reinforce approach behavJ. Neurophysiol. 80: 1–27, 1998. The effects of lesions, receptor ior. The functions of rewards were developed further during blocking, electrical selfstimulation, and drugs ..."
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Cited by 717 (12 self)
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Schultz, Wolfram. Predictive reward signal of dopamine neurons. is called rewards, which elicit and reinforce approach behavJ. Neurophysiol. 80: 1–27, 1998. The effects of lesions, receptor ior. The functions of rewards were developed further during blocking, electrical selfstimulation, and drugs
Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora
, 1997
"... ..."
IntegrateandFire Neurons Driven by Correlated Stochastic Input
, 2002
"... Neurons are sensitive to correlations among synaptic inputs. However, analytical models that explicitly include correlations are hard to solve analytically, so their influence on a neuron’s response has been difficult to ascertain. To gain some intuition on this problem, we studied the firing times ..."
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Cited by 26 (4 self)
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of two simple integrateandfire model neurons driven by a correlated binary variable that represents the total input current. Analytic expressions were obtained for the average firing rate and coefficient of variation (a measure of spiketrain variability) as functions of the mean, variance
IntegrateandFire Neurons and Networks
, 2002
"... Most biological neurons communicate by short electrical pulses, called action potentials or spikes. In contrast to the standard neuron model used in artificial neural networks, integrateandfire neurons do not rely on a temporal average over the pulses. In integrateandfire and similar spiking neu ..."
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Cited by 3 (0 self)
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Most biological neurons communicate by short electrical pulses, called action potentials or spikes. In contrast to the standard neuron model used in artificial neural networks, integrateandfire neurons do not rely on a temporal average over the pulses. In integrateandfire and similar spiking
Firing Rate of the Noisy Quadratic IntegrateandFire Neuron
, 2003
"... We calculate the firing rate of the quadratic integrateandfire neuron in response to a colored noise input current. Such an input current is a good approximation to the noise due to the random bombardment of spikes, with the correlation time of the noise corresponding to the decay time of the syna ..."
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Cited by 48 (4 self)
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We calculate the firing rate of the quadratic integrateandfire neuron in response to a colored noise input current. Such an input current is a good approximation to the noise due to the random bombardment of spikes, with the correlation time of the noise corresponding to the decay time
Results 1  10
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139,276