scholarly journals The problem of perfect predictors in statistical spike train models

2021 ◽  
Author(s):  
Sahand Farhoodi ◽  
Uri T. Eden
Keyword(s):  
Spike Train

A determination of excitability changes in dorsal spinocerebellar tract neurons from spike-train analysis

1977 ◽  
Vol 40 (3) ◽  
pp. 626-646 ◽  
Author(s):  
C. K. Knox ◽  
S. Kubota ◽  
R. E. Poppele
Keyword(s):  
Spike Train ◽  
Afferent Input ◽  
Complex Interactions ◽  
Group I ◽  
Long Latency ◽  
Cross Correlation Analysis ◽  
Output Spike ◽  
Type 3 ◽  
Stimulation Of

1. Responses of DSCT neurons to random electrical stimulation of peripheral nerves of the hindleg at group I intensity were studied using cross-correlation analysis of the output spike train with the stimulus. Three types of response were found: type 1 was due to monosynaptic activation of DSCT cells, type 2 resulted from inhibition of those cells, and type 3 was due to a long-latency excitation that was probably polysynaptic. 2. Most of the units studied responded to stimulation of both proximal and distal flexor and extensor nerves. The extensive convergence of afferent input on DSCT cells is much greater than has been observed previously, with type 2 and type 3 responses totaling 80% of the observed responses. We attribute this to the sensitivity of the analysis in detecting small changes in postsynaptic excitability. 3. The results of the study, particularly the derivation of postsynaptic excitability changes, generally confirm those of earlier work employing intracellular recording. 4. By varying stimulus rate and stimulus intensity in the group 1 range and simulating the resulting correlations, we conclude that excitability changes in DSCT cells are the net result of complex interactions involving excitation and inhibition. A summary of these findings is presented as a model for the minimum circuitry necessary to account for the observed behavior.

Messages of Oscillatory Correlograms: A Spike Train Model

2008 ◽  
Vol 20 (5) ◽  
pp. 1211-1238 ◽  
Author(s):  
Gaby Schneider
Keyword(s):  
Spike Train ◽  
Null Model ◽  
Simulated Data ◽  
Joint Analysis ◽  
Data Set ◽  
Proposed Model ◽  
Peak Asymmetry ◽  
Millisecond Range ◽  
Temporal Interactions ◽  
Underlying Processes

Oscillatory correlograms are widely used to study neuronal activity that shows a joint periodic rhythm. In most cases, the statistical analysis of cross-correlation histograms (CCH) features is based on the null model of independent processes, and the resulting conclusions about the underlying processes remain qualitative. Therefore, we propose a spike train model for synchronous oscillatory firing activity that directly links characteristics of the CCH to parameters of the underlying processes. The model focuses particularly on asymmetric central peaks, which differ in slope and width on the two sides. Asymmetric peaks can be associated with phase offsets in the (sub-) millisecond range. These spatiotemporal firing patterns can be highly consistent across units yet invisible in the underlying processes. The proposed model includes a single temporal parameter that accounts for this peak asymmetry. The model provides approaches for the analysis of oscillatory correlograms, taking into account dependencies and nonstationarities in the underlying processes. In particular, the auto- and the cross-correlogram can be investigated in a joint analysis because they depend on the same spike train parameters. Particular temporal interactions such as the degree to which different units synchronize in a common oscillatory rhythm can also be investigated. The analysis is demonstrated by application to a simulated data set.

Impact of substance P on the correlation of spike train evoked by electro acupuncture

2016 ◽  
Vol 87 ◽  
pp. 249-254
Author(s):  
Chen Jin ◽  
Xuan Zhang ◽  
Jiang Wang ◽  
Yi Guo ◽  
Xue Zhao ◽  
...  
Keyword(s):  
Substance P ◽  
Spike Train

scholarly journals A Biologically Plausible Computational Model for Auditory Object Recognition

2009 ◽  
Vol 101 (1) ◽  
pp. 323-331 ◽  
Author(s):  
Eric Larson ◽  
Cyrus P. Billimoria ◽  
Kamal Sen
Keyword(s):  
Object Recognition ◽  
Spike Train ◽  
Nearest Neighbor ◽  
Primary Auditory Cortex ◽  
Spike Trains ◽  
Coincidence Detection ◽  
Model Based ◽  
Plausible Model ◽  
Field L ◽  
Song Discrimination

Object recognition is a task of fundamental importance for sensory systems. Although this problem has been intensively investigated in the visual system, relatively little is known about the recognition of complex auditory objects. Recent work has shown that spike trains from individual sensory neurons can be used to discriminate between and recognize stimuli. Multiple groups have developed spike similarity or dissimilarity metrics to quantify the differences between spike trains. Using a nearest-neighbor approach the spike similarity metrics can be used to classify the stimuli into groups used to evoke the spike trains. The nearest prototype spike train to the tested spike train can then be used to identify the stimulus. However, how biological circuits might perform such computations remains unclear. Elucidating this question would facilitate the experimental search for such circuits in biological systems, as well as the design of artificial circuits that can perform such computations. Here we present a biologically plausible model for discrimination inspired by a spike distance metric using a network of integrate-and-fire model neurons coupled to a decision network. We then apply this model to the birdsong system in the context of song discrimination and recognition. We show that the model circuit is effective at recognizing individual songs, based on experimental input data from field L, the avian primary auditory cortex analog. We also compare the performance and robustness of this model to two alternative models of song discrimination: a model based on coincidence detection and a model based on firing rate.

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