How does a neuron vary its mean output firing rate if the input changes from random to coherent activity? What are the critical parameters of the neuronal dynamics and input statistics? To answer these questions, we investigate the coincidence detection properties of an integrate-and-fire neuron. We derive an expression indicating how coincidence detection depends on neuronal parameters. Specifically, (i) we show how coincidence detection depends on the shape of the postsynaptic response function, the number of synapses, and the input statistics, and (ii) we demonstrate that there is an optimal threshold. Our considerations can be used to predict from neuronal parameters whether and to what extent a neuron can act as a coincidence detector and thus can convert a temporal code into a rate code. Physik-Department der TU Munchen (T35), D-85747 Garching bei Munchen, Germany y Swiss Federal Institute of Technology, Center of Neuromimetic Systems, EPFL-DI, CH-1015 Lausanne, Switz...

this article, we show that for a general class of spiking neuron models, which includes, in particular, the leaky integrate-and-#re model (Lapicque, 1907; Knight, 1972) as well as nonlinear spiking models, all three cases can occur if the input current is periodic, while aperiodic currents induce reproducible responses. In addition to numerical simulations, we put forth a theoretical explanation of this property for aperiodic currents that oscillate around threshold. The conditions required for our explanation are not ful#lled by the nonleaky integrate-and-#re model---also called perfect integrator---which is never reliable

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 integrate-and-fire 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 phase-locked to this periodic signal.

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Precise temporal coding is a hallmark of the auditory system. Selective pressures to improve accuracy or encode more rapid changes have produced a suite of convergent physiological and morphological features that contribute to temporal coding. Comparative studies of temporal coding also point to shared computational strategies, and suggest how selection acts to improve coding. Both the avian cochlear nucleus angularis and the mammalian cochlear nuclei have heterogeneous cell populations, and similar responses to sound. These shared characteristics may represent convergent responses to similar selective pressures to encode features of airborne sound.

The auditory systems of birds and mammals use timing information from each ear to detect interaural time difference (ITD). To determine whether the Jeffress-type algorithms that underlie sensitivity to ITD in birds are an evolutionarily stable strategy, we recorded from the auditory nuclei of crocodilians, who are the sister group to the birds. In alligators, precisely timed spikes in the first-order nucleus magnocellularis (NM) encode the timing of sounds, and NM neurons project to neurons in the nucleus laminaris (NL) that detect interaural time differences. In vivo recordings from NL neurons show that the arrival time of phase-locked spikes differs between the ipsilateral and contralateral inputs. When this disparity is nullified by their best ITD, the neurons respond maximally. Thus NL neurons act as coincidence detectors. A biologically detailed model of NL with alligator parameters discriminated ITDs up to 1 kHz. The range of best ITDs represented in NL was much larger than in birds, however, and extended from 0 to 1000 �s contralateral, with a median ITD of 450 �s. Thus, crocodilians and birds employ similar algorithms for ITD detection, although crocodilians have larger heads.

www.elsevier.com/locate/jphysparis Coding interaural time differences at low best frequencies in the barn owl

Neurons compute mainly with action potentials or “spikes”, which are stereotypical electrical impulses. Over the last century, the operating function of neurons has been mainly described in terms of firing rates, with the timing of spikes bearing little information. More recently, experimental evidence and theoretical studies have shown that the relative spike timing of inputs has an important effect both on computation and learning in neurons. This evidence has triggered considerable interest for spiking neuron models in computational neuroscience, but the theory of computation in those models is sparse. Spiking neuron models are hybrid dynamical systems, combining differential equations and discrete events. I have developed specific theoretical approaches to study this particular type of models. In particular, two specific properties seem to be relevant for computation: spiking models can encode time-varying inputs into trains of precisely timed spikes, and they are more likely fire to when input spike trains are tightly correlated. To simulate spiking models efficiently, we have developed specific techniques, which can now be used in an open source simulator (Brian). These theoretical and methodological investigations now allow us to address spike-based modeling at a more global and functional level. Since the mechanisms of synaptic plasticity tend to favor synchronous inputs, I propose to investigate computational mechanisms based on neural synchrony in sensory modalities. Contents

Transduction of sound in mammalian ears is mediated by basilar-membrane waves exhibiting delays that increase systematically with distance from the cochlear base. Most contemporary accounts of such Btraveling-wave ^ delays in humans have ignored postmortem basilar-membrane measurements in favor of indirect in vivo estimates derived from brainstem-evoked responses, compound action potentials, and otoacoustic emissions. Here, we show that those indirect delay estimates are either flawed or inadequately calibrated. In particular, we argue against assertions based on indirect estimates that basilar-membrane delays are much longer in humans than in experimental animals. We also estimate in vivo basilar-membrane delays in humans by correcting postmortem measurements in humans according to the effects of death on basilar-membrane vibrations in other mammalian species. The estimated in vivo basilar-membrane delays in humans are similar to delays in the hearing organs of other tetrapods, including those in which basilar membranes do not sustain traveling waves or that lack basilar membranes altogether.