Voltage-gated ion channels in neuronal membranes uctuate randomly between dierent conformational states due to thermal agitation. Fluctuations between conducting and non-conducting states give rise to noisy membrane currents and sub-threshold voltage uctuations and may contribute to variability in spike timing. Here we study sub-threshold voltage uctuations due to active voltage-gated Na + and K + channels as predicted by two commonly used kinetic schemes: the Mainen et al. (MJHS) kinetic scheme, which has been used to model dendritic channels in cortical neurons, and the classical Hodgkin-Huxley (HH) kinetic scheme for the squid giant axon. We compute the magnitudes, amplitude distributions, and power spectral densities of the voltage noise in isopotential membrane patches predicted by these kinetic schemes. For both schemes, noise magnitudes increase rapidly with depolarization from rest. Noise is larger for smaller patch areas but is smaller for increased model temp...

The neuropeptide allatostatin decreases the spike rate in response to time-varying stretches of two different crustacean mechanoreceptors, the gastropyloric receptor 2 in the crab Cancer borealis and the coxobasal chordotonal organ (CBCTO) in the crab Carcinus maenas. In each system, the decrease in firing rate is accompanied by an increase in the timing precision of spikes triggered by discrete temporal features in the stimulus. This was quantified by calculating the standard deviation or “jitter ” in the times of individual identified spikes elicited in response to repeated presentations of the stimulus. Conversely, serotonin increases the firing rate but decreases the timing precision of the CBCTO response. Intracellular recordings from the afferents of this receptor demonstrate that allatostatin increases the conductance of the neurons, consistent with its inhibitory action on spike rate, whereas serotonin decreases the overall membrane conductance. We conclude that spike-timing precision of mechanoreceptor afferents in response to dynamic stimulation can be altered by neuromodulators acting directly on the afferent neurons. Key words: stretch receptor; reliability; coding; neuropeptide; crustacea; STG

nuates the amplitude of quantal synaptic voltages. These results demonstrate that Na amplify the synaptic voltage, enhancing the SNR and contrast sensitivity. Key words: retinal ganglion cell; contrast threshold; QX-314; tetrodotoxin; signal amplification; signal-to-noise ratio Introduction Voltage-gated membrane channels in a spiking neuron are necessary for spike generation, but they add noise to the spike train through several mechanisms. Their voltage gating is stochastic and therefore directly adds noise to the membrane current. In addition, below spike threshold, the voltage sensitivity of Na channels and the resulting modulation of regenerative currents can amplify noise from channels and synaptic inputs. This suggests that the function of voltage-gated channels is a trade-off between coding information and adding noise to the spike train (Schneidman et al., 1998; White et al., 2000; van Rossum et al., 2003). In a retinal ganglion cell, the spike generator limits the e

Introduction The quality of the visual signal transmitted to the brain is important for perception because it sets the minimum detectable stimulus. As a daylight visual signal is processed by the retina, each layer adds noise (Ashmore and Copenhagen, 1983; Freed et al., 2003) so that the signal quality of a ganglion cell is limited by retinal noise sources (Schellart and Spekreijse, 1973; Reich et al., 1977; Levine and Zimmerman, 1991; Troy and Robson, 1992; Croner et al., 1993; Freed, 2000), implying information loss (Geisler, 1989). The loss is thought to originate partly in selective processing of the signal and partly from noise sources such as stochastic vesicle release and channel gating (Barrett and Stevens, 1972; Schneidman et al., 1998; White et al., 2000; van Rossum et al., 2003). To understand how efficiently information is transferred and how neural mechanisms preserve signal quality, one could measure information loss at each retinal stage. One way is to compare the contr

Ion channels are the building blocks of the information processing capability of neurons: any realistic computational model of a neuron must include reliable and effective ion channel components. Sophisticated statistical and computational tools have been developed to study the ion channel structure–function relationship, but this work is rarely incorporated into the models used for single neurons or small networks. The disjunction is partly a matter of convention. Structure–function studies typically use a single Markov model for the whole channel whereas until recently whole-cell modeling software has focused on serial, independent, two-state subunits that can be represented by the Hodgkin–Huxley equations. More fundamentally, there is a difference in purpose that prevents models being easily reused. Biophysical models are typically developed to study one particular aspect of channel gating in detail, whereas neural modelers require broad coverage of the entire range of channel behavior that is often best achieved with approximate representations that omit structural features that cannot be adequately constrained. To bridge the gap so that more recent channel data can be used in neural models requires new computational infrastructure for bringing together diverse sources of data to arrive at bestfit models for whole-cell modeling. We review the current state of channel modeling and explore the developments needed for its conclusions to be integrated into whole-cell modeling.

In vivo studies have shown that neurons in the neocortex can generate action potentials at high temporal precision. The mechanisms controlling timing and reliability of action potential generation in neocortical neurons, however, are still poorly understood. Here we investigated the temporal precision and reliability of spike firing in cortical layer V pyramidal cells at near-threshold membrane potentials. Timing and reliability of spike responses were a function of EPSC kinetics, temporal jitter of population excitatory inputs, and of background synaptic noise. We used somatic current injection to mimic population synaptic input events and measured spike probability and spike time precision (STP), the latter defined as the time window (Dt) holding 80 % of response spikes. EPSC rise and decay times were varied over the known physiological spectrum. At spike threshold level, EPSC decay time had a stronger influence on STP than rise time. Generally, STP was highest (#2.45 ms) in response to synchronous compounds of EPSCs with fast rise and decay kinetics. Compounds with slow EPSC kinetics (decay time constants.6 ms) triggered spikes at lower temporal precision ($6.58 ms). We found an overall linear relationship between STP and spike delay. The difference in STP between fast and slow compound EPSCs could be reduced by incrementing the amplitude of slow compound EPSCs. The introduction of a temporal jitter to compound EPSCs had a comparatively small effect on STP, with a tenfold increase in jitter resulting in only a five fold decrease in STP. In the presence