Results 1  10
of
36
The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs
 Journal of Neuroscience
, 1993
"... How random is the discharge pattern of cortical neurons? We examined recordings from primary visual cortex (Vl; Knierim and Van Essen, 1992) and extrastriate cortex (MT; Newsome et al., 1989a) of awake, behaving macaque monkey and compared them to analytical predictions. For nonbursting cells firi ..."
Abstract

Cited by 448 (11 self)
 Add to MetaCart
How random is the discharge pattern of cortical neurons? We examined recordings from primary visual cortex (Vl; Knierim and Van Essen, 1992) and extrastriate cortex (MT; Newsome et al., 1989a) of awake, behaving macaque monkey and compared them to analytical predictions. For nonbursting cells firing at sustained rates up to 300 Hz, we evaluated two indices of firing variability: the ratio of the variance to the mean for the number of action potentials evoked by a constant stimulus, and the ratenormalized coefficient of variation (C,) of the interspike interval distribution. Firing in virtually all Vl and MT neurons was nearly consistent with a completely random process (e.g., C, = 1). We tried to model this high variability by small, independent, and random EPSPs converging onto a leaky integrateandfire neuron (Knight, 1972). Both this and related models
Variability and correlated noise in the discharge of neurons in motor and parietal areas of the primate cortex. J Neurosci 18:1161–1170
, 1998
"... We analyzed the magnitude and interneuronal correlation of the variability in the activity of single neurons that were recorded simultaneously using a multielectrode array in the primary motor cortex and parietal areas 2/5 in rhesus monkeys. The animals were trained to move their arms in one of eigh ..."
Abstract

Cited by 98 (5 self)
 Add to MetaCart
We analyzed the magnitude and interneuronal correlation of the variability in the activity of single neurons that were recorded simultaneously using a multielectrode array in the primary motor cortex and parietal areas 2/5 in rhesus monkeys. The animals were trained to move their arms in one of eight directions as instructed by a visual target. The relationship between variability (SD) and mean of the discharge rate was described by a power function with a similar exponent (�0.57), regardless of the cortical area or the behavioral condition. We examined whether the deviation from mean activity between target onset and the end of the movement was correlated on a trialbytrial basis with variability in activity during the hold period before target onset. In both cortical areas, for about a quarter of the neurons, the neuronal noise of these two periods was positively correlated, whereas significant negative correlations were seldom
Dynamics of Membrane Excitability Determine Interspike Interval Variability: A Link Between Spike Generation Mechanisms and Cortical Spike Train Statistics
, 1998
"... We propose a biophysical mechanism for the high interspike interval variability observed in cortical spike trains. The key lies in the nonlinear dynamics of cortical spike generation, which are consistent with type I membranes where saddlenode dynamics underlie excitability (Rinzel & Ermentrout ..."
Abstract

Cited by 52 (5 self)
 Add to MetaCart
We propose a biophysical mechanism for the high interspike interval variability observed in cortical spike trains. The key lies in the nonlinear dynamics of cortical spike generation, which are consistent with type I membranes where saddlenode dynamics underlie excitability (Rinzel & Ermentrout, 1989). We present a canonical model for type I membranes, the θneuron. The θneuron is a phase model whose dynamics reflect salient features of type I membranes. This model generates spike trains with coefficient of variation (CV) above 0.6 when brought to firing by noisy inputs. This happens because the timing of spikes for a type I excitable cell is exquisitely sensitive to the amplitude of the suprathreshold stimulus pulses. A noisy input current, giving random amplitude “kicks” to the cell, evokes highly irregular firing across a wide range of firing rates; an intrinsically oscillating cell gives regular spike trains. We corroborate the results with simulations of the MorrisLecar (ML) neural model with random synaptic inputs: type I ML yields high CVs. When this model is modified to have type II dynamics (periodicity arises via a Hopf bifurcation), however, it gives regular spike trains (CV below 0.3). Our results suggest that the high CV values such as those observed in cortical spike trains are an intrinsic characteristic of type I membranes driven to firing by “random” inputs. In contrast, neural oscillators or neurons exhibiting type II excitability should produce regular spike trains.
Impact of Correlated Inputs on the Output of the Integrateandfire Model
, 1999
"... For the integrateandfire model with or without reversal potentials, we consider how correlated inputs affect the variability of cellular output. For both models the variability of efferent spike trains measured by coefficient of variation of the interspike interval (abbreviated to CV in the remain ..."
Abstract

Cited by 35 (10 self)
 Add to MetaCart
For the integrateandfire model with or without reversal potentials, we consider how correlated inputs affect the variability of cellular output. For both models the variability of efferent spike trains measured by coefficient of variation of the interspike interval (abbreviated to CV in the remainder of the paper) is a nondecreasing function of input correlation. When the correlation coefficient is greater than 0.09, the CV of the integrateandfire model without reversal potentials is always above 0.5, no matter how strong the inhibitory inputs. When the correlation coefficient is greater than 0.05, CV for the integrateandfire model with reversal potentials is always above 0.5, independent of the strength of the inhibitory inputs. Under a given condition on correlation coefficients we find that correlated Poisson processes can be decomposed into independent Poisson processes. We also develop a novel method to estimate the distribution density of the first passage time of the integ...
Temporally irregular mnemonic persistent activity in prefrontal neurons of monkeys during a delayed response task
 Journal of neurophysiology
, 2003
"... and XiaoJing Wang. Temporally irregular mnemonic persistent activity in prefrontal neurons of monkeys during a delayed response task. J Neurophysiol 90: 3441–3454, 2003. First published May 28, 2003; 10.1152/jn.00949.2002. An important question in neuroscience is whether and how temporal patterns a ..."
Abstract

Cited by 31 (2 self)
 Add to MetaCart
and XiaoJing Wang. Temporally irregular mnemonic persistent activity in prefrontal neurons of monkeys during a delayed response task. J Neurophysiol 90: 3441–3454, 2003. First published May 28, 2003; 10.1152/jn.00949.2002. An important question in neuroscience is whether and how temporal patterns and fluctuations in neuronal spike trains contribute to information processing in the cortex. We have addressed this issue in the memoryrelated circuits of the prefrontal cortex by analyzing spike trains from a database of 229 neurons recorded in the dorsolateral prefrontal cortex of 4 macaque monkeys during the performance of an oculomotor delayedresponse task. For each task epoch, we have estimated their power spectrum together with interspike interval histograms and autocorrelograms. We find that 1) the properties of most (about 60%) neurons approximated the characteristics of a Poisson process. For about 25 % of cells, with characteristics typical of interneurons, the power spectrum
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 ..."
Abstract

Cited by 26 (4 self)
 Add to MetaCart
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 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, and correlation time of the stochastic input. The results of computer simulations were in excellent agreement with these expressions. In these models, an increase in correlation time in general produces an increase in both the average firing rate and the variability of the output spike trains. However, the magnitude of the changes depends differentially on the relative values of the input mean and variance: the increase in firing rate is higher when the variance is large relative to the mean, whereas the increase in variability is higher when the variance is relatively small. In addition, the firing rate always tends to a finite limit value as the correlation time increases toward infinity, whereas the coefficient of variation typically diverges. These results suggest that temporal correlations may play a major role in determining the variability as well as the intensity of neuronal spike trains.
Minimal Models of Adapted Neuronal Response to In Vivo–Like Input Currents
, 2004
"... Rate models are often used to study the behavior of large networks of spiking neurons. Here we propose a procedure to derive rate models that take into account the fluctuations of the input current and firingrate adaptation, two ubiquitous features in the central nervous system that have been previ ..."
Abstract

Cited by 26 (4 self)
 Add to MetaCart
Rate models are often used to study the behavior of large networks of spiking neurons. Here we propose a procedure to derive rate models that take into account the fluctuations of the input current and firingrate adaptation, two ubiquitous features in the central nervous system that have been previously overlooked in constructing rate models. The procedure is general and applies to any model of firing unit. As examples, we apply it to the leaky integrateandfire (IF) neuron, the leaky IF neuron with reversal potentials, and to the quadratic IF neuron. Two mechanisms of adaptation are considered, one due to an afterhyperpolarization current and the other to an adapting threshold for spike emission. The parameters of these simple models can be tuned to match experimental data obtained from neocortical pyramidal neurons. Finally, we show how the stationary model can be used to predict the timevarying activity of a large population of adapting neurons.
`Balancing' of conductances may explain irregular cortical spiking.
"... Five related factors are identified which enable single compartment HodgkinHuxley model neurons to convert random synaptic input into irregular spike trains similar to those seen in in vivo cortical recordings. We suggest that cortical neurons may operate in a narrow parameter regime where synaptic ..."
Abstract

Cited by 14 (2 self)
 Add to MetaCart
Five related factors are identified which enable single compartment HodgkinHuxley model neurons to convert random synaptic input into irregular spike trains similar to those seen in in vivo cortical recordings. We suggest that cortical neurons may operate in a narrow parameter regime where synaptic and intrinsic conductances are balanced to reflect, through spike timing, detailed correlations in the inputs.
Synchronization of the Neural Response to Noisy Periodic Synaptic Input in a Balanced Leaky IntegrateandFire Neuron with Reversal Potentials
 Neural Computation
, 1999
"... Neurons in which the level of excitation and inhibition are roughly balanced are shown to be very sensitive to the coherence of their synaptic input. The behavior of such balanced neurons with reversal potentials is analyzed both analytically and numerically using the leaky integrateandfire neural ..."
Abstract

Cited by 14 (3 self)
 Add to MetaCart
(Show Context)
Neurons in which the level of excitation and inhibition are roughly balanced are shown to be very sensitive to the coherence of their synaptic input. The behavior of such balanced neurons with reversal potentials is analyzed both analytically and numerically using the leaky integrateandfire neural model. The investigation uses the Gaussian approximation with synaptic inputs modeled as inhomogeneous Poisson processes. The results indicate that for balanced neurons with N synaptic inputs, it is only necessary for O( # N) of the synaptic inputs to have a periodicity in order that their spike outputs become phaselocked to this periodic signal.
Kinetic theory for neuronal network dynamics
 Communications in Mathematical Sciences
, 2006
"... Abstract. We present a detailed theoretical framework for statistical descriptions of neuronal networks and derive (1+1)dimensional kinetic equations, without introducing any new parameters, directly from conductancebased integrateandfire neuronal networks. We describe the details of derivation ..."
Abstract

Cited by 11 (1 self)
 Add to MetaCart
(Show Context)
Abstract. We present a detailed theoretical framework for statistical descriptions of neuronal networks and derive (1+1)dimensional kinetic equations, without introducing any new parameters, directly from conductancebased integrateandfire neuronal networks. We describe the details of derivation of our kinetic equation, proceeding from the simplest case of one excitatory neuron, to coupled networks of purely excitatory neurons, to coupled networks consisting of both excitatory and inhibitory neurons. The dimension reduction in our theory is achieved via novel moment closures. We also describe the limiting forms of our kinetic theory in various limits, such as the limit of meandriven dynamics and the limit of infinitely fast conductances. We establish accuracy of our kinetic theory by comparing its prediction with the full simulations of the original pointneuron networks. We emphasize that our kinetic theory is dynamically accurate, i.e., it captures very well the instantaneous statistical properties of neuronal networks under timeinhomogeneous inputs.