Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell

Troyer, Todd W.; Miller, Kenneth D.

doi:10.1162/neco.1997.9.5.971

Cited by 234 publications

(219 citation statements)

References 11 publications

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“…3 and 9) suggest that carbachol may also down-regulate SK channels. Regulation of this AHP may play a crucial role in establishing the firing behaviour of the cell, including it's gain (Mehaffey et al, 2005;Troyer and Miller, 1997), and it's frequency-selectivity (Ellis et al, 2007b;Mehaffey et al, 2008b).…”

Section: Neuromodulatory Regulation Of Ahpsmentioning

confidence: 99%

Ionic and neuromodulatory regulation of burst discharge controls frequency tuning

Mehaffey

Ellis

Krahe

et al. 2008

Journal of Physiology-Paris

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Sensory neurons encode natural stimuli by changes in firing rate or by generating specific firing patterns, such as bursts. Many neural computations rely on the fact that neurons can be tuned to specific stimulus frequencies. It is thus important to understand the mechanisms underlying frequency tuning. In the electrosensory system of the weakly electric fish, Apteronotus leptorhynchus, the primary processing of behaviourally relevant sensory signals occurs in pyramidal neurons of the electrosensory lateral line lobe (ELL). These cells encode low frequency prey stimuli with bursts of spikes and high frequency communication signals with single spikes. We describe here how bursting in pyramidal neurons can be regulated by intrinsic conductances in a cell subtype specific fashion across the sensory maps found within the ELL, thereby regulating their frequency tuning. Further, the neuromodulatory regulation of such conductances within individual cells and the consequences to frequency tuning are highlighted. Such alterations in the tuning of the pyramidal neurons may allow weakly electric fish to preferentially select for certain stimuli under various behaviourally relevant circumstances.

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Section: Neuromodulatory Regulation Of Ahpsmentioning

confidence: 99%

Ionic and neuromodulatory regulation of burst discharge controls frequency tuning

Mehaffey

Ellis

Krahe

et al. 2008

Journal of Physiology-Paris

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“…The dynamics of the output neuron may be understood intuitively in the two limits mentioned before, when the drift is positive and much larger than the fluctuations, and when the drift is zero (Troyer and Miller, 1997). If the net drive is positive and is close to 0 (Gerstein and Mandelbrot, 1964;Tuckwell, 1988;Usher et al, 1994;Koch, 1999), Equation 6 is reduced to…”

Section: Two Output Modes: Mean Excitatory Drive Versus Fluctuationsmentioning

confidence: 99%

Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models

2000

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Cortical neurons are typically driven by thousands of synaptic inputs. The arrival of a spike from one input may or may not be correlated with the arrival of other spikes from different inputs. How does this interdependence alter the probability that the postsynaptic neuron will fire? We constructed a simple random walk model in which the membrane potential of a target neuron fluctuates stochastically, driven by excitatory and inhibitory spikes arriving at random times. An analytic expression was derived for the mean output firing rate as a function of the firing rates and pairwise correlations of the inputs. This stochastic model made three quantitative predictions. (1) Correlations between pairs of excitatory or inhibitory inputs increase the fluctuations in synaptic drive, whereas correlations between excitatory-inhibitory pairs decrease them. (2) When excitation and inhibition are fully balanced (the mean net synaptic drive is zero), firing is caused by the fluctuations only. (3) In the balanced case, firing is irregular. These theoretical predictions were in excellent agreement with simulations of an integrate-and-fire neuron that included multiple conductances and received hundreds of synaptic inputs. The results show that, in the balanced regime, weak correlations caused by signals shared among inputs may have a multiplicative effect on the input-output rate curve of a postsynaptic neuron, i.e. they may regulate its gain; in the unbalanced regime, correlations may increase firing probability mainly around threshold, when output rate is low; and in all cases correlations are expected to increase the variability of the output spike train. Key words: random-walk; integrate-and-fire; computer simulation; spike synchrony; oscillations; cross-correlation; balanced inhibition; cerebral cortexThe output of a typical cortical neuron depends on the activity of a large number of synaptic inputs-several thousands of them, as estimated by anatomical techniques (White, 1989; Braitenberg and Shüz, 1997). What kind of response should be expected from a postsynaptic neuron driven by so many inputs? Answering this question in detail requires a deep understanding of dendritic integration, synaptic function, and spike generation mechanisms; but, given the large numbers commonly involved, as a first approximation it is natural to cast the problem in statistical terms. The strategy then is to compute the output responses of a model neuron (or their statistics), given a set of driving inputs with known statistical properties. These inputs may be either independent or temporally correlated. In the latter case, spikes from different input neurons arrive close together in time more often or less often than expected by chance.In general, the situation with independent inputs is easier to analyze, and for many applications it is probably a good approximation. However, there are at least three reasons why the effects of correlations on single cells should be fully characterized. First, correlations in spike counts have indeed been o...

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“…The neurons are simulated cortical neurons (Troyer and Miller, 1997), the neuron parameters, large scale background synaptic activation parameters and dynamics of the connections from 1 → 2 and 1 → 3 are the same as and described in detail in Halliday (2015). In brief, the model neurons have a time constant of 20 ms, and all receive independent excitatory and inhibitory inputs, 4000 excitatory inputs/sec, 300µV EPSPs and 1000 inhibitory inputs /sec, shunting inhibition, simulating the balanced large scale background synaptic activation seen in vivo (Destexhe et al, 2003).…”

Section: Simulated 3 Neuron Networkmentioning

confidence: 99%

Non-parametric directionality analysis – Extension for removal of a single common predictor and application to time series

Halliday

Senik

Stevenson

et al. 2016

Journal of Neuroscience Methods

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Article ReuseItems deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request.Non parametric directionality analysis -extension for removal of a single common predictor and application to time series. AbstractBackground. The ability to infer network structure from multivariate neuronal signals is central to computational neuroscience. Directed network analyses typically use parametric approaches based on auto-regressive (AR) models, where networks are constructed from estimates of AR model parameters. However, the validity of using low order AR models for neurophysiological signals has been questioned. A recent article introduced a non parametric approach to estimate directionality in bivariate data, non parametric approaches are free from concerns over model validity.New Method. We extend the non parametric framework to include measures of directed conditional independence, using scalar measures that decompose the overall partial correlation coefficient summatively by direction, and a set of functions that decompose the partial coherence summatively by direction. A time domain partial correlation function allows both time and frequency views of the data to be constructed. The conditional independence estimates are conditioned on a single predictor.Results. The framework is applied to simulated cortical neuron networks and mixtures of Gaussian time series data with known interactions. It is applied to experimental data consisting of local field potential recordings from bilateral hippocampus in anaesthetised rats.Comparison with Existing Method(s). The framework offers a non parametric approach to estimation of directed interactions in multivariate neuronal recordings, and increased flexibility in dealing with both spike train and time series data.Conclusions. The framework offers a novel alternative non parametric approach to estimate directed interactions in multivariate neuronal recordings, and is applicable to spike train and time series data

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Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell

Cited by 234 publications

References 11 publications

Ionic and neuromodulatory regulation of burst discharge controls frequency tuning

Ionic and neuromodulatory regulation of burst discharge controls frequency tuning

Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models

Non-parametric directionality analysis – Extension for removal of a single common predictor and application to time series

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