2 edition of investigation of functional interaction between neurones by cross-correlation of spike trains found in the catalog.
investigation of functional interaction between neurones by cross-correlation of spike trains
Bruce Raymond Vachon
|Statement||Bruce R. Vachon.|
|Contributions||Toronto, Ont. University.|
|The Physical Object|
|Pagination||288 leaves in various foliations :|
|Number of Pages||288|
Representation and selection of time-varying signals by single cortical neurons Jonathan Vincent Toups A dissertation submitted to the faculty of the University of North Carolina at Chapel. During individuated finger movements, a high proportion of synchrony effects was found in spike-triggered averages (SpikeTAs) of rectified electromyographic activity aligned on Cited by:
Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey. Neural binocular interaction and functional architecture in the cat's visual cortex. J. Physiol. (Lond Multimicroelectrode investigation of monkey striate cortex: spike train correlations in the infragranular layers. J. Neurophysiol. Cross-correlation between cerebral blood flow (CBF) and background EEG activity can indicate the integrity of CBF control under changing metabolic demand. The difficulty of obtaining long, continuous recordings of good quality for both EEG and CBF signals in a clinical setting is overcome, in the present work, by an algorithm that allows the cross-correlation function Cited by:
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Pairwise spike correlations are correlations between the spike trains of two neurons, quantified by the spike (cross) correlation function. Higher order correlations include three-neuron, four-neuron, and the general N -neuron correlations, where the joint occurrence of N − 1 spiking events influence the N th spiking by: 9.
Form of auto- and cross-correlation histograms of impulse trains of two neurones with a common monosynaptic input from the third one were studied by methods of modelling of neuronal interaction--biomathematical (computer controlled experiment on molluscs neurones) and mathematical--in wide physiological ranges of values of parameters Author: Zosimovskiĭ Va.
The spike trains emitted by the ith cell in the kth trial were represented by a binary time series, y i k (t), which equalled 1 if there was a spike at time t and zero otherwise.
The number of spikes which must be recorded in order to detect significant correlation between spike trains of two synaptically connected neurons was estimated by a mathematical model. Dependence of this number of spikes on importance of interneuronal connection (measured as the amplitude of the EPSP evoked by a single spike of the input Author: K.
Baev, A. Degtyarenko. Cross-correlation is a measure of the similarity of two signals as a function of the time lag or lead applied to one of the signals. In case the two signals are simultaneously recorded spike trains, the cross-correlation becomes a count of the number of coincidences of firing for the two spike trains as a function of the time delay between them.
ﬁrings. Some well known examples are cross-correlation, joint peri-stimulus time histogram (JPSTH), unitary events, partial directed coherence (PDC), among others.
The cross-correlation function  is probably the most widely used technique to measure the interaction between spike trains. Cross-correlation as a statistical measure was. To analyze functional connections between these neurons, cross-correlation analysis has been most commonly applied to the hundreds to thousands of pairs of these neurons.
However, conventional cross-correlation data needs statistical tests for significance especially when the sample size of recorded spike trains is by: The post-cross-interval (post-CI) between the compared and reference spike trains relative to the nth reference spike in spike train A is defined as (5) τ′ n, m+1 =t m+1 ′−t n such that t m ′≤t n ≤t m+1 ′.
The probability of firing the next spike is given by the probability density function (pdf).Cited by: 3. To explore the functional significance of horizontal neural connections in the extent of a 'hypercolumn' of the cat visual cortex, we carried out cross-correlation analysis of spike trains.
Generation of Spike T rains with Controlled Auto- and Cross-Correlation Functions Figure 3: Controlled cross-correlation structure between two nonstationary random spike trains.
The spike threshold can reduce spike count correlations. The rela-tionship between. and firing rate depends largely on the proportion of subthreshold events that are masked by the spiking threshold.
Correlations in spiking responses arise because of co-fluctuations in synaptic input, which give rise to correlated membrane potential. Measuring spike train correlation We record spike times as those times t i k at which crosses 2 37, Because Z 2 =0 and 0 for all the models we consider, always continues through 2 and begins the next period of interspike dynamics.
We consider the output spike trains y i t = i t−tk, where tk is the time of the kth spike of the ith neuron. the spike trains to interval trains, shown in Fig. 4 D for the spike trains in C. This emphasizes the presence of long ISIs and removes some of the information regarding the precise occurrence times of action potentials.
The interval cross correlation (ICC) between each pair of interval trains is computed and averagedFile Size: 1MB. The incidence of spike-LFP coherence was greater than that of cells showing significant power in the frequency analysis of spike trains alone.
This was strongest between spike trains and both layer V LFPs (around 29 and 69 Hz) and layer II LFPs (around 28 and 69 Hz), despite the mean firing rates being only Hz (around which there was little.
Interaction between theta-locked interneurons and theta waves in the AE state. (A) Auto-correlation of the theta-driving neuron’s firing activity (blue) and the corresponding theta wave (red).(B Cited by: 3.
An investigation into the use of Cross Correlation Velocimetry A Masters Thesis Submitted to the Faculty of the Chapter 1 is a paper titled Frequency and Spatial Dependence of Cross Correlation Velocimetry and was presented at the Central States Section of the Combustion Institute, University of Alabama, April FT_SPIKE_XCORR computes the cross-correlation histogram and shift predictor.
Use as [stat] = ft_spike_xcorr(cfg, data) The input SPIKE should be organised as the spike or the raw datatype, obtained from FT_SPIKE_MAKETRIALS or FT_PREPROCESSING (in that case, conversion is done within the function).
An approach to study the mechanism of information processing in a neural circuit is to identify synaptic connectivity between neurons. As recent advances in experimental techniques facilitate simultaneous recording of a large population of neurons, estimation of synaptic connectivity from multiple neural spike train data has become a major goal in computational Author: Ryota Kobayashi, Katsunori Kitano.
Under optimal conditions, just 3–6 ms of visual stimulation suffices for humans to see motion. Motion perception on this timescale implies that the visual system under these conditions reliably encodes, transmits, and processes neural signals with near-millisecond precision.
Motivated by in vitro evidence for high temporal precision of motion signals in the primate Cited by: 1. Spike train transfer was quantified using cross-correlation analysis (Derjean et al.
; Le Masson et al. ; Levine ). This method allowed us to measure the relation between two spike trains, g and h, in a defined sliding time window τ according to the following equationCited by:. Spike groups are vertically shifted from each other by mV for visual clarity. D, The firing rate of five simultaneously recorded neurons from a 24 h experiment, under continuous 20 Hz stimulation.
Rates computed using bins of 1 h. E, Cross-correlation between the firing rates from D, computed for different timescales (see Materials and Cited by: N2 - Cross-correlation between surface electromyogram (EMG) signals is commonly used as a means of quantifying EMG cross talk during voluntary activation.
To examine the reliability of this method, the relationship between cross talk and the cross-correlation between surface EMG signals was examined by using model by: Program Summary Thursday, 25 February p Registration opens p Welcome reception p Opening remarks p Session 1: Engineering neural circuits Invited speakers: Xiao-Jing Wang, Blaise Agüera y Arcas p Poster Session I Friday, 26 February a Breakfast a Session 2: Memory and temporal integration Invited speaker: Mark Goldman; 3 accepted talksFile Size: 2MB.