Spike trains
The Computational Structure of Spike Trains
... from spike trains, our approach finds their causal state models (CSMs), the minimal hidden Markov models or stochastic automata capable of generating statistically identical time ...the spike train ...
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Statistics of spike trains in conductance-based neural networks: Rigorous results
... spiking activity. Those conclusions where obtained using the maximal entropy prin- ciple [ 14 ]. Assume that the average value of observables quantities (e.g., firing rate or spike correlations) has been ...
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Spike trains statistics in integrate and fire models: exact results.
... 7 Raster plots statistics As discussed in the introduction, the neuroscience commu- nity is confronted to the delicate problem of characterizing statistical properties of raster plots from finite time spike ...
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Can We Hear the Shape of a Maximum Entropy Potential From Spike Trains?
... Spike Trains Acknowledgements We consider a spike-generating stationary Markov process whose transition probabilities are known. We show that there is a canonical potential whose Gibbs distribution, ...
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Analyzing large-scale spike trains data with spatio-temporal constraints
... 1 Context Recent experimental advances have made it possible to record several hundred neurons simultaneously in the retina as well as in the cortex. Analyzing such a huge amount of data requires to elaborate ...
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Parameters estimation for spatio-temporal maximum entropy distributions: application to neural spike trains.
... that spike trains generation involves causal interac- tions between neurons and memory ...a spike pattern ω(n) at time n, given the past history of spikes reads P ω(n) ...
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Reconstructing the functional connectivity of multiple spike trains sing Hawkes models
... In counting processes, parameter estimation is generally performed using maximum likelihood method since, under some regularity conditions, maximum likelihood estimators produce the small- est asymptotic variance [31]. ...
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Detection of synchronized firings in multivariate neural spike trains during motor tasks
... and n =10 in the case of FGDM, the mean number of false min positives detection in randomized data was lower than 10. V. D ISCUSSION In this paper, we presented two standard methods ([3] and [4]) able to detect groups of ...
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Reconstructing the functional connectivity of multiple spike trains using Hawkes models
... observed spike trains ([29, ...predict spike occurrences in a given neuron as a function of its earlier spikes, of the preceding activities of the other recorded neurons, and possibly of some other ...
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Dynamics and spike trains statistics in conductance-based Integrate-and-Fire neural networks with chemical and electric synapses
... 3. Ostojic S, Brunel N, Hakim V. Synchronization properties of networks of electrically coupled neurons in the presence of noise and heterogeneities. J. Comp. Neurosci., 26(3):369–392, 2009. 4. Vasquez J-C, Palacios A, ...
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On the mathematical consequences of binning spike trains
... for spike trains, then the binned chain, though of unbounded memory with variable length, will automatically present all good statistical features needed to study its longtime ...
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Neuronal networks, spike trains statistics and Gibbs distributions
... of spike train statistics is that techniques and ideas from statistical mechanics and thermodynamics will help us understand the collective “macroscopic” behavior of big populations spiking neurons with rela- ...
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Dynamics and spike trains statistics in conductance-based Integrate-and-Fire neural networks with chemical and electric synapses
... for spike trains statistics analysis such as maxi- mum entropy models or Generalized Linear Models ...retinal spike train analysis [3]. Especially, it involves spatio-temporal spike patterns ...
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Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains.
... (2008) Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike ...temporal spike patterns with millisecond precision, both in vitro and in vivo, lasting from a few ...
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An integrate-and-fire model to generate spike trains with long-range dependence
... 5 Discussion In this paper, we have studied two approaches to model the long-range temporal correlations observed in the spike trains of certain neurons. In a first approach, the introduction of a weakly ...
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Dynamics and spike trains statistics in conductance-based Integrate-and-Fire neural networks with chemical and electric synapses
... 2 Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland * tsanovm@tcd.ie Neuronal ensemble activity in the hippocampus is the basis of the cognitive spatial map; the firing properties of ...
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Estimating maximum entropy distributions from periodic orbits in spike trains
... Binning is currently used in spike train data. It has the eect of removing time- correlations and it clearly simpli es the shape of the potential. The eect of binning will be studied in a separate paper. ...
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Space-time correlations in spike trains and the neural code
... The receptive eld of a sensory neuron is a region of space in which the presence of a stimulus will alter the ring of that neuron.. Receptive eld[r] ...
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Philosophy of the Spike: Rate-Based vs. Spike-Based Theories of the Brain
... notably by Nicolas Brunel and colleagues, using methods from statistical mechanics ( Brunel, 2000 ). It is possible to derive equations that describe the transformation between the input rates of independent ...
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Spike train statistics and Gibbs distributions
... 4 Conclusion In this paper we have argued that Gibbs distribution considered in a fairly general sense could constitute generic statistical models to fit spike trains data. The example of the Integrate and ...
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