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58
A synchronizationdesynchronization code for natural communication signals. Neuron 52
, 2006
"... Synchronous spiking of neural populations is hypothesized to play important computational roles in forming neural assemblies and solving the binding problem. Although the opposite phenomenon of desynchronization is well known from EEG studies, it is largely neglected on the neuronal level. We here p ..."
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Synchronous spiking of neural populations is hypothesized to play important computational roles in forming neural assemblies and solving the binding problem. Although the opposite phenomenon of desynchronization is well known from EEG studies, it is largely neglected on the neuronal level. We here provide an example of in vivo recordings from weaklyelectric fish demonstrating that, depending on the social context, different types of natural communication signals elicit transient desynchronization as well as synchronization of the electroreceptor population without changing the mean firing rate. We conclude that, in general, both positive and negative changes in the degree of synchrony can be the relevant signals for neural information processing.
Spikefrequency adapting neural ensembles: Beyond mean adaptation and renewal theories
 Neural Computation
, 2007
"... We propose a Markov process model for spikefrequency adapting neural ensembles which synthesizes existing meanadaptation approaches, population density methods, and inhomogeneous renewal theory, resulting in a unied and tractable framework which goes beyond renewal and meanadaptation theories by ..."
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Cited by 19 (1 self)
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We propose a Markov process model for spikefrequency adapting neural ensembles which synthesizes existing meanadaptation approaches, population density methods, and inhomogeneous renewal theory, resulting in a unied and tractable framework which goes beyond renewal and meanadaptation theories by accounting for correlations between subsequent interspike intervals. A method for eciently generating inhomogeneous realizations of the proposed Markov process is given, numerical methods for solving the population equation are presented, and an expression for the rstorder interspike interval correlation is derived. Further, we show that the full vedimensional master equation for a conductancebased integrateandre neuron with spikefrequency adaptation and a relative refractory mechanism driven by Poisson spike trains can be reduced to a twodimensional generalization of the proposed Markov process by an adiabatic elimination of fast variables. For static and dynamic stimulation, negative serial interspike interval correlations and transient population responses respectively of MonteCarlo simulations of the full vedimensional system can be accurately described by the proposed twodimensional Markov process. 1
Fractional differentiation by neocortical pyramidal neurons
 Nat. Neurosci
, 2008
"... Neural systems adapt to changes in stimulus statistics. However, it is not known how stimuli with complex temporal dynamics drive the dynamics of adaptation and the resulting firing rate. For single neurons, it has often been assumed that adaptation has a single time scale. Here, we show that single ..."
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Cited by 18 (0 self)
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Neural systems adapt to changes in stimulus statistics. However, it is not known how stimuli with complex temporal dynamics drive the dynamics of adaptation and the resulting firing rate. For single neurons, it has often been assumed that adaptation has a single time scale. Here, we show that single rat neocortical pyramidal neurons adapt with a time scale that depends on the time scale of changes in stimulus statistics. This multiple time scale adaptation is consistent with fractional order differentiation, such that the neuron’s firing rate is a fractional derivative of slowly varying stimulus parameters. Biophysically, even though neuronal fractional differentiation effectively yields adaptation with many time scales, we find that its implementation requires only a few, properly balanced known adaptive mechanisms. Fractional differentiation provides single neurons with a fundamental and general computation that can contribute to efficient information processing, stimulus anticipation, and frequency independent phase shifts of oscillatory neuronal firing.
Limits of Linear Rate Coding of Dynamic Stimuli by
, 2007
"... We estimated the frequencyintensity (fI) curves of Punit electroreceptors using 4Hz random amplitude modulations (RAMs) and using the covariance method (50Hz RAMs). Both methods showed that P units are linear encoders of stimulus amplitude with additive noise; the gain of the fI curve was, on ..."
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Cited by 5 (1 self)
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We estimated the frequencyintensity (fI) curves of Punit electroreceptors using 4Hz random amplitude modulations (RAMs) and using the covariance method (50Hz RAMs). Both methods showed that P units are linear encoders of stimulus amplitude with additive noise; the gain of the fI curve was, on average, 0.32 and 2.38 spikes�s �1 ��V �1 for the low and highfrequency cutoffs, respectively. There were two sources of apparent noise in the encoding process: the first was the variability of baseline Punit discharge and the second was the variation of receptor discharge due to variability of the stimulus slope independent of its intensity. The covariance method showed that a linear combination of eigenvectors representing the timeweighted stimulus intensity (E1) and its derivative (E2) could account for, on average, 92 % of the total response variability; E1 by itself accounted for 76 % of the variability. The low gain of the lowfrequency fI curve implies that detection of small (1 �V) signals would require integration over many receptors (�1,200) and time (200 ms); even then, signals that elicit behavioral responses could not be detected using rate coding with the estimated gain and noise levels. Weak signals at the limit of behavioral thresholds could be detected if the animal were able to extract E1 from the population of responding P units; we propose a tentative mechanism for this operation although there is no evidence as to whether it is actually implemented in the nervous system of these fish.
PULSE BIFURCATIONS IN STOCHASTIC NEURAL FIELDS
"... Abstract. We study the effects of additive noise on traveling pulse solutions in spatially extended neural fields with linear adaptation. Neural fields are evolution equations with an integral term characterizing synaptic interactions between neurons at different spatial locations of the network. We ..."
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Abstract. We study the effects of additive noise on traveling pulse solutions in spatially extended neural fields with linear adaptation. Neural fields are evolution equations with an integral term characterizing synaptic interactions between neurons at different spatial locations of the network. We introduce an auxiliary variable to model the effects of local negative feedback and consider random fluctuations by modeling the system as a set of spatially extended Langevin equations whose noise term is a QWiener process. Due to the translation invariance of the network, neural fields can support a continuum of spatially localized bump solutions that can be destabilized by increasing the strength of the adaptation, giving rise to traveling pulse solutions. Near this criticality, we derive a stochastic amplitude equation describing the dynamics of these bifurcating pulses when the noise and the deterministic instability are of comparable magnitude. Away from this bifurcation, we investigate the effects of additive noise on the propagation of traveling pulses and demonstrate that noise induces wandering of traveling pulses. Our results are complemented with numerical simulations. Key words. neural field equations, traveling pulses, noise, amplitude equations, stochastic pitchfork bifurcation AMS subject classifications. 92C20; 35R60 1. Introduction. Spatially
Computational modeling of spike generation in serotonergic neurons of the dorsal raphe nucleus
, 2012
"... Serotonergic neurons of the dorsal raphe nucleus, with their extensive innervation of limbic and higher brain regions and interactions with the endocrine system have important modulatory or regulatory eects on many cognitive, emotional and physiological processes. They have been strongly implicated ..."
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Serotonergic neurons of the dorsal raphe nucleus, with their extensive innervation of limbic and higher brain regions and interactions with the endocrine system have important modulatory or regulatory eects on many cognitive, emotional and physiological processes. They have been strongly implicated in responses to stress and in the occurrence of major depressive disorder and other pyschiatric disorders. In order to quantify some of these eects, detailed mathematical models of the activity of such cells are required which describe their complex neurochemistry and neurophysiology. We consider here a singlecompartment model of these neurons which is capable of describing many of the known features of spike generation, particularly the slow rhythmic pacemaking activity often observed in these cells in a variety of species. Included in the model are ten kinds of voltage dependent ion channels as well as calciumdependent potassium current. Calcium dynamics includes buering and pumping. In sections 39, each component is con
Complex spike event pattern of transient ON/OFF retinal ganglion cells
"... running head: Spike event pattern of ON/OFF retinal ganglion cells ..."
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running head: Spike event pattern of ON/OFF retinal ganglion cells
a K.E. Stephan, a R.B. Reilly,
, 2007
"... We present a neural mass model of steadystate membrane potentials measured with local field potentials or electroencephalography in the frequency domain. This model is an extended version of previous dynamic causal models for investigating eventrelated potentials in the timedomain. In this paper, ..."
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We present a neural mass model of steadystate membrane potentials measured with local field potentials or electroencephalography in the frequency domain. This model is an extended version of previous dynamic causal models for investigating eventrelated potentials in the timedomain. In this paper, we augment the previous formulation with parameters that mediate spikerate adaptation and recurrent intrinsic inhibitory connections. We then use linear systems analysis to show how the model's spectral response changes with its neurophysiological parameters. We demonstrate that much of the interesting behaviour depends on the nonlinearity which couples mean membrane potential to mean spiking rate. This nonlinearity is analogous, at the population level, to the firing rate–input curves often used to characterize singlecell responses. This function depends on the model's gain and adaptation currents which, neurobiologically, are influenced by the activity of modulatory neurotransmitters. The key contribution of this paper is to show how neuromodulatory effects can be modelled by adding adaptation currents to a simple phenomenological model of EEG. Critically, we show that these effects are expressed in a systematic way in the spectral density of EEG recordings. Inversion of the model, given such noninvasive recordings, should allow one to quantify pharmacologically induced changes in adaptation currents. In short, this work establishes a forward or generative model of electrophysiological recordings for psychopharmacological studies. © 2007 Elsevier Inc. All rights reserved.
c,e
, 2008
"... Bayesian estimation of synaptic physiology from the spectral responses of neural masses R.J. Moran, a,b,⁎ K.E. Stephan, ..."
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Bayesian estimation of synaptic physiology from the spectral responses of neural masses R.J. Moran, a,b,⁎ K.E. Stephan,
c,e
, 2008
"... Bayesian estimation of synaptic physiology from the spectral responses of neural masses R.J. Moran, a,b,⁎ K.E. Stephan, ..."
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Bayesian estimation of synaptic physiology from the spectral responses of neural masses R.J. Moran, a,b,⁎ K.E. Stephan,