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Saturday, May 16, 2020 | History

2 edition of Neuronal spike trains and stochastic point processes found in the catalog.

Neuronal spike trains and stochastic point processes

Donald H. Perkel

Neuronal spike trains and stochastic point processes

by Donald H. Perkel

  • 129 Want to read
  • 39 Currently reading

Published by Rand Corp. in Santa Monica, Calif .
Written in English

    Subjects:
  • Electrophysiology -- Mathematical models.,
  • Neurons.

  • Edition Notes

    Statement[by] Donald H. Perkel, George L. Gerstein, and George P. Moore.
    SeriesRand Corporation. Memorandum RM-4816-PR
    ContributionsGerstein, George L., joint author., Moore, George P., 1932- joint author.
    Classifications
    LC ClassificationsQ180.A1 R36 no. 4816
    The Physical Object
    Paginationxi, 136 p.
    Number of Pages136
    ID Numbers
    Open LibraryOL5597104M
    LC Control Number68002847

    Neural Spike Train Analysis 1: Introduction to Point Processes physical systems that produce a stochastic set of localized events in time or space. Any point process describing neural spike train data can be modeled using a conditional intensity function, and. Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating idea is that neurons in the SNN do not fire at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather fire only when a membrane.

    The paper “Spike sorting by joint probabilistic modeling of neural spike trains and waveforms” by B. A. Matthews and M. A. Clements develops a novel probabilistic method for automatic neural spike sorting which uses stochastic point process models of neural spike trains and parameterized action potential waveforms. A novel likelihood model Author: Wei Wu, Asohan Amarasingham, Zhe (Sage) Chen, Sung-Phil Kim.   Stable propagation of synchronous spiking in cortical neural networks Gerstein,G. L. & Moore,G. P. Neuronal spike trains and stochastic point processes. A. Stable propagation of Cited by:

    Information through a Spiking Neuron Charles F. Stevens and Anthony Zador Salk Institute MNL/S La J olIa, CA [email protected] Abstract While it is generally agreed that neurons transmit information about their synaptic inputs through spike trains, the code by which this information is transmitted is not well understood. An upperCited by: Home Browse by Title Periodicals Neural Computation Vol. 24, No. 8 Strictly positive-definite spike train kernels for point-process divergences article Strictly positive-definite spike train kernels for point-process divergencesCited by: 1.


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Neuronal spike trains and stochastic point processes by Donald H. Perkel Download PDF EPUB FB2

The statistical analysis of two simultaneously observed trains of neuronal spikes is described, using as a conceptual framework the theory of stochastic point processes. The first statistical question that arises is whether the observed trains are independent; statistical techniques for testing independence are developed around the notion that, under the null hypothesis, the times of spike occurrence in one train Cited by: 4 The dendrites 13 13 1.

2 Types of neurons 2 Signals in the nervous system 14 2. 1 Action potentials as point events - point processes in the nervous system 15 18 2. 2 Spontaneous activi~ in neurons 3 Stochastic modelling of single neuron spike trains 19 3.

1 Characteristics of a neuron spike train 19 3. 2 The mathematical neuron 23 4 Superposition models 26 4. 1 superposition of renewal processes Author: W.

Ray. dependent.4 Neuronal spike trains rarely satisfy this requirement completely; even thosespiketrains that canadequatelybedescribed as stationary oftenexhibit serial dependenceamonginterspike intervals. Many results first established in renewal theory have subsequently been gen-eralized to nonrenewal stationary point processes (McFadden, ), andin some.

COVID Resources. Reliable information about the coronavirus (COVID) is available from the World Health Organization (current situation, international travel).Numerous and frequently-updated resource results are available from this ’s WebJunction has pulled together information and resources to assist library staff as they consider how to handle coronavirus.

According to the null hypothesis, spike trains Aand B are independent in the mathematical sense. This means that spikes in train Aoccur at momentstaken at randomwith respect to train B. In relating the two spike trains wemaytherefore use somemathematical results about single point processes observed fromrandom momentsin time.

Train A l Train B I] i,VA--' Backward, Iv Forward. One important class ofstationary point processes, known as renewalprocesses, has the property that the lengths of intervals between events are statistically in- dependent.4 Neuronal spike trains rarely satisfy this requirement completely; even thosespiketrainsthatcanadequatelybedescribedasstationary oftenexhibitserial by: The statistical techniques available for the analysis of single spike trains are described and related to the underlying mathematical theory, that of stochastic point processes, i.e., of stochastic processes whose realizations may be described as series of point events occurring in Cited by: simultaneous spike train stochastic point process neuronal spike train observed dependency two-train comparison statistical sign train represent random instant first statistical question statistical analysis oftwo acompanion paper observed train monitored neuron spike occurrence conceptual framework various class possible mode trainsofneuronal.

The statistical techniques available for the analysis of single spike trains are described and related to the underlying mathematical theory, that of stochastic point processes, i.e., of stochastic processes whose realizations may be described as series of point events occurring in.

Neural spike trains are described by temporal point processes because the spike events are localized in time. It is also possible to use point process theory to model data that is localized at a discrete set of locations in space or in both space and time. The mathematical theory of stochastic point processes in its probabilistic and statistical aspects is applied to nerve-impulse sequences.

Mathematical results are extended and illustrated through the application of statistical techniques to the results of computer experiments on simulated nerve by: 1 Some basic neurophysiology 4 The neuron 1.

1 4 1. 1 The axon 7 1. 2 The synapse 9 12 1. 3 The soma 1. 4 The dendrites 13 13 1. 2 Types of neurons 2 Signals in the nervous system 14 2. 1 Action potentials as point events - point processes in the nervous system 15 18 2.

2 Spontaneous activi~ in neurons 3 Stochastic modelling of single neuron spike trains 19 3. 1 Characteristics of. Abstract. The statistical analysis of two simultaneously observed trains of neuronal spikes is described, using as a conceptual framework the theory of stochastic point first statistical question that arises is whether the observed trains are independent; statistical techniques for testing independence are developed around the notion that, under the null hypothesis, the times of.

The easiest way to simulate such spike trains is a point process simulation based on a Poisson process model [15]. This model is based on the assumption that the probability of a neuron firing in Author: Carl Van Vreeswijk.

Neuronal Spike Trains and Stochastic Point ProcessesII. Simultaneous Spike TrainsCited by: His research is focused on the relations between structure, dynamics, and function in spiking networks of the brain.

He combines neuronal network modeling and spike train analysis, often using stochastic point processes as a conceptual link. A review of noise in the nervous system with a focus on internal noise sources can be found in ().Analysis of spike trains in terms of stochastic point processes has a long tradition (; ) and often involves concepts from renewal theory ().Some principles of spike-train analysis with an emphasis on modern results have been reviewed by Gabbiani and Koch and Rieke et al.

A typical neuron, e.g., a pyramidal cell in the vertebrate cortex, receives input spikes from thousands of other neurons, which in turn receive input from their presynaptic neurons and so forth; see Fig.

While it is not impossible to incorporate millions of integrate-and-fire neurons into a huge network model, it is often reasonable to focus the modeling efforts on a specific subset of.

Decoding of neuronal ensemble spike trains is implemented via stochastic state-space models with point process observations.

The framework is illustrated with examples of neural decoding of hand Author: Wilson Truccolo. Stochastic models for spike trains of single neurons. point processes in the nervous system.- Spontaneous activity in neurons.- 3 Stochastic modelling of single neuron spike trains.- Characteristics of a neuron spike train.- The mathematical neuron.- 4 Superposition models.- Superposition of renewal processes.

Statistics of spike trains In this section, we introduce some important concepts for the statistical description of neuronal spike trains. A central notion will be the interspike interval distribution which is discussed in the framework of a generalized input-dependent renewal theory.

Renewal processes are a class of stochastic point.Spikes: Exploring the Neural Code is a pleasure. It deals with a fundamental issue in neuroscience―how information about the world is represented in sensory spike trains―how information about the world is represented in sensory spike trains―and does so with clarity for the neuroscientist and rigor for the computational by: We de ne spike trains mathematically as point processes and describe the Poisson and Gamma models.

We then introduce a variety of regularity measures normally used to describe neural spike trains. 2 Theoretical framework Elements of probability theory Here we describe spike trains and their properties rigorously by appropriate mathematicalFile Size: KB.