scholarly journals Estimating Excitatory and Inhibitory Synaptic Conductances from Spike Trains using a Recursive Bayesian Approach

2017 ◽  
Author(s):  
Mila Lankarany
Keyword(s):  
Membrane Potential ◽  
Expectation Maximization ◽  
Spike Trains ◽  
Spike Rate ◽  
Gaussian Kernel ◽  
Simulation Studies ◽  
Optimal Linear ◽  
Different Sources ◽  
Optimal Linear Filtering ◽  
Synaptic Conductances

AbstractInference of excitatory and inhibitory synaptic conductances (SCs) from the spike trains is poorly addressed in the literature due to the complexity of the problem. As recent technological advancements make recording spikes from multiple (neighbor) neurons of a behaving animal (in some rare cases from humans) possible, this paper tackles the problem of estimating SCs solely from the recorded spike trains. Given an ensemble of spikes corresponding to population of neighbor neurons, we aim to infer the average excitatory and inhibitory SCs underlying the shared neural activity. In this paper, we extended our previously established Kalman filtering (KF)–based algorithm to incorporate the voltage-to-spike nonlinearity (mapping from membrane potential to spike rate). Having estimated the instantaneous spike rate using optimal linear filtering (Gaussian kernel), our proposed algorithm uses KF followed by expectation maximization (EM) algorithm in a recursive fashion to infer the average SCs. As the dynamics of SCs and membrane potential is included in our model, the proposed algorithm, unlike other related works, considers different sources of stochasticity, i.e., the variabilities of SCs, membrane potential, and spikes. Moreover, it is worth mentioning that our algorithm is blind to the external stimulus, and it performs only based on observed spikes. We validate the accuracy and practicality of our technique through simulation studies where leaky integrate and fire (LIF) model is used to generate spikes. We show that the estimated SCs can precisely track the original ones. Moreover, we show that the performance of our algorithm can be further improved given enough number of trials (spikes). As a rule of thumb, 50 trials of neurons with the average firing rate of 5 Hz can guarantee the accuracy of our proposed algorithm.

Bursts and recurrences of bursts in the spike trains of spontaneously active striate cortex neurons

1985 ◽  
Vol 53 (4) ◽  
pp. 926-939 ◽  
Author(s):  
C. R. Legendy ◽  
M. Salcman
Keyword(s):  
Standard Deviation ◽  
Spike Train ◽  
Striate Cortex ◽  
Quantitative Measure ◽  
Spike Trains ◽  
Spike Rate ◽  
Occurrence Rate ◽  
Implanted Electrodes ◽  
Firing Rates ◽  
The Mean

Simultaneous recordings were made from small collections (2-7) of spontaneously active single units in the striate cortex of unanesthetized cats, by means of chronically implanted electrodes. The recorded spike trains were computer scanned for bursts of spikes, and the bursts were catalogued and studied. The firing rates of the neurons ranged from 0.16 to 32 spikes/s; the mean was 8.9 spikes/s, the standard deviation 7.0 spikes/s. Bursts of spikes were assigned a quantitative measure, termed Poisson surprise (S), defined as the negative logarithm of their probability in a random (Poisson) spike train. Only bursts having S greater than 10, corresponding to an occurrence rate of about 0.01 bursts/1,000 spikes in a random spike train, were considered to be of interest. Bursts having S greater than 10 occurred at a rate of about 5-15 bursts/1,000 spikes, or about 1-5 bursts/min. The rate slightly increased with spike rate; averaging about 2 bursts/min for neurons having 3 spikes/s and about 4.5 bursts/min for neurons having 30 spikes/s. About 21% of the recorded units emitted significantly fewer bursts than the rest (below 1 burst/1,000 spikes). The percentage of these neurons was independent of spike rate. The spike rate during bursts was found to be about 3-6 times the average spike rate; about the same for longer as for shorter bursts. Bursts typically contained 10-50 spikes and lasted 0.5-2.0 s. When the number of spikes in the successively emitted bursts was listed, it was found that in some neurons these numbers were not distributed at random but were clustered around one or more preferred values. In this sense, bursts occasionally "recurred" a few times in a few minutes. The finding suggests that neurons are highly reliable. When bursts of two or more simultaneously recorded neurons were compared, the bursts often appeared to be temporally close, especially between pairs of neurons recorded by the same electrode; but bursts seldom started and ended simultaneously on two channels. Recurring bursts emitted by one neuron were occasionally accompanied by time-locked recurring bursts by other neurons.

scholarly journals The accuracy of membrane potential reconstruction based on spiking receptive fields

2012 ◽  
Vol 107 (8) ◽  
pp. 2143-2153 ◽  
Author(s):  
Deepankar Mohanty ◽  
Benjamin Scholl ◽  
Nicholas J. Priebe
Keyword(s):  
Membrane Potential ◽  
Receptive Fields ◽  
Sensory Stimulation ◽  
Spike Rate ◽  
Rate Data ◽  
Noise Stimulus ◽  
Synaptic Inputs ◽  
Common Technique ◽  
The Relationship ◽  
Membrane Potential Fluctuations

A common technique used to study the response selectivity of neurons is to measure the relationship between sensory stimulation and action potential responses. Action potentials, however, are only indirectly related to the synaptic inputs that determine the underlying, subthreshold, response selectivity. We present a method to predict membrane potential, the measurable result of the convergence of synaptic inputs, based on spike rate alone and then test its utility by comparing predictions to actual membrane potential recordings from simple cells in primary visual cortex. Using a noise stimulus, we found that spike rate receptive fields were in precise correspondence with membrane potential receptive fields ( R2 = 0.74). On average, spike rate alone could predict 44% of membrane potential fluctuations to dynamic noise stimuli, demonstrating the utility of this method to extract estimates of subthreshold responses. We also found that the nonlinear relationship between membrane potential and spike rate could also be extracted from spike rate data alone by comparing predictions from the noise stimulus with the actual spike rate. Our analysis reveals that linear receptive field models extracted from noise stimuli accurately reflect the underlying membrane potential selectivity and thus represent a method to generate estimates of the underlying average membrane potential from spike rate data alone.

scholarly journals A Method to Estimate Synaptic Conductances From Membrane Potential Fluctuations

2004 ◽  
Vol 91 (6) ◽  
pp. 2884-2896 ◽  
Author(s):  
Michael Rudolph ◽  
Zuzanna Piwkowska ◽  
Mathilde Badoual ◽  
Thierry Bal ◽  
Alain Destexhe
Keyword(s):  
Membrane Potential ◽  
Computational Models ◽  
Network Activity ◽  
Intracellular Recordings ◽  
Neocortical Neurons ◽  
Underlying Network ◽  
Analytic Expression ◽  
Synaptic Conductances

In neocortical neurons, network activity can activate a large number of synaptic inputs, resulting in highly irregular subthreshold membrane potential ( Vm) fluctuations, commonly called “synaptic noise.” This activity contains information about the underlying network dynamics, but it is not easy to extract network properties from such complex and irregular activity. Here, we propose a method to estimate properties of network activity from intracellular recordings and test this method using theoretical and experimental approaches. The method is based on the analytic expression of the subthreshold Vm distribution at steady state in conductance-based models. Fitting this analytic expression to Vm distributions obtained from intracellular recordings provides estimates of the mean and variance of excitatory and inhibitory conductances. We test the accuracy of these estimates against computational models of increasing complexity. We also test the method using dynamic-clamp recordings of neocortical neurons in vitro. By using an on-line analysis procedure, we show that the measured conductances from spontaneous network activity can be used to re-create artificial states equivalent to real network activity. This approach should be applicable to intracellular recordings during different network states in vivo, providing a characterization of the global properties of synaptic conductances and possible insight into the underlying network mechanisms.

scholarly journals Calculating Event-Triggered Average Synaptic Conductances From the Membrane Potential

2007 ◽  
Vol 97 (3) ◽  
pp. 2544-2552 ◽  
Author(s):  
Martin Pospischil ◽  
Zuzanna Piwkowska ◽  
Michelle Rudolph ◽  
Thierry Bal ◽  
Alain Destexhe
Keyword(s):  
Membrane Potential ◽  
Cortical Neurons ◽  
Time Course ◽  
Spike Generation ◽  
Nonlinear Contribution ◽  
Central Neurons ◽  
Time Courses ◽  
Model Subject ◽  
Synaptic Conductances

The optimal patterns of synaptic conductances for spike generation in central neurons is a subject of considerable interest. Ideally such conductance time courses should be extracted from membrane potential ( Vm) activity, but this is difficult because the nonlinear contribution of conductances to the Vm renders their estimation from the membrane equation extremely sensitive. We outline here a solution to this problem based on a discretization of the time axis. This procedure can extract the time course of excitatory and inhibitory conductances solely from the analysis of Vm activity. We test this method by calculating spike-triggered averages of synaptic conductances using numerical simulations of the integrate-and-fire model subject to colored conductance noise. The procedure was also tested successfully in biological cortical neurons using conductance noise injected with dynamic clamp. This method should allow the extraction of synaptic conductances from Vm recordings in vivo.

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