Experimental and Theoretical Analyses of Synchrony in Feedforward Networks

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

There is a wealth of electrophysiological evidence suggesting that synchronous firing of neurons is present in the normal functioning brain. Synchrony has been postulated to be the substrate for encoding precise timing of action potentials that is necessary for pattern formation or for "binding" activities of neurons that encode distinct features of a stimulus. A hallmark of neural activity during a seizure is the appearance of synchronous events throughout the brain. Synchrony, however, is not exclusively found during a seizure but seems to be ubiquitous under normal conditions and, indeed, may be crucial for information processing. This chapter focuses on recent experimental and theoretical studies of synchrony in feedforward neural networks. This network is the backbone for information transfer in the brain given that signals must be propagated from neuron to neuron, from one nucleus to another, and from one brain region to another. Epileptiform activity may represent an aberrant manifestation of functional synchrony. If so, then understanding the mechanism by which synchrony propagates normally through a neural network might provide clues as to how the synchrony becomes pathological and may suggest preventive measures. As in feedforward networks, epileptiform activity originates from a focal point and then spreads rapidly in a stereotypic manner through a sequence of cortical areas. The simplicity of the feedforward network means that both experimental and rigorous theoretical approaches can be used to derive very general principles, which may then be applied to the study of epilepsy.

Original languageEnglish (US)
Title of host publicationComputational Neuroscience in Epilepsy
PublisherElsevier Inc.
Pages304-316
Number of pages13
ISBN (Print)9780123736499
DOIs
StatePublished - 2008

Fingerprint

Neurons
Brain
Seizures
Automatic Data Processing
Action Potentials
Epilepsy
Theoretical Models

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Experimental and Theoretical Analyses of Synchrony in Feedforward Networks. / Reyes, Alexander.

Computational Neuroscience in Epilepsy. Elsevier Inc., 2008. p. 304-316.

Research output: Chapter in Book/Report/Conference proceedingChapter

Reyes, Alexander. / Experimental and Theoretical Analyses of Synchrony in Feedforward Networks. Computational Neuroscience in Epilepsy. Elsevier Inc., 2008. pp. 304-316
@inbook{6539d55d0c224078b4d311b34ac94baf,
title = "Experimental and Theoretical Analyses of Synchrony in Feedforward Networks",
abstract = "There is a wealth of electrophysiological evidence suggesting that synchronous firing of neurons is present in the normal functioning brain. Synchrony has been postulated to be the substrate for encoding precise timing of action potentials that is necessary for pattern formation or for {"}binding{"} activities of neurons that encode distinct features of a stimulus. A hallmark of neural activity during a seizure is the appearance of synchronous events throughout the brain. Synchrony, however, is not exclusively found during a seizure but seems to be ubiquitous under normal conditions and, indeed, may be crucial for information processing. This chapter focuses on recent experimental and theoretical studies of synchrony in feedforward neural networks. This network is the backbone for information transfer in the brain given that signals must be propagated from neuron to neuron, from one nucleus to another, and from one brain region to another. Epileptiform activity may represent an aberrant manifestation of functional synchrony. If so, then understanding the mechanism by which synchrony propagates normally through a neural network might provide clues as to how the synchrony becomes pathological and may suggest preventive measures. As in feedforward networks, epileptiform activity originates from a focal point and then spreads rapidly in a stereotypic manner through a sequence of cortical areas. The simplicity of the feedforward network means that both experimental and rigorous theoretical approaches can be used to derive very general principles, which may then be applied to the study of epilepsy.",
author = "Alexander Reyes",
year = "2008",
doi = "10.1016/B978-012373649-9.50023-5",
language = "English (US)",
isbn = "9780123736499",
pages = "304--316",
booktitle = "Computational Neuroscience in Epilepsy",
publisher = "Elsevier Inc.",

}

TY - CHAP

T1 - Experimental and Theoretical Analyses of Synchrony in Feedforward Networks

AU - Reyes, Alexander

PY - 2008

Y1 - 2008

N2 - There is a wealth of electrophysiological evidence suggesting that synchronous firing of neurons is present in the normal functioning brain. Synchrony has been postulated to be the substrate for encoding precise timing of action potentials that is necessary for pattern formation or for "binding" activities of neurons that encode distinct features of a stimulus. A hallmark of neural activity during a seizure is the appearance of synchronous events throughout the brain. Synchrony, however, is not exclusively found during a seizure but seems to be ubiquitous under normal conditions and, indeed, may be crucial for information processing. This chapter focuses on recent experimental and theoretical studies of synchrony in feedforward neural networks. This network is the backbone for information transfer in the brain given that signals must be propagated from neuron to neuron, from one nucleus to another, and from one brain region to another. Epileptiform activity may represent an aberrant manifestation of functional synchrony. If so, then understanding the mechanism by which synchrony propagates normally through a neural network might provide clues as to how the synchrony becomes pathological and may suggest preventive measures. As in feedforward networks, epileptiform activity originates from a focal point and then spreads rapidly in a stereotypic manner through a sequence of cortical areas. The simplicity of the feedforward network means that both experimental and rigorous theoretical approaches can be used to derive very general principles, which may then be applied to the study of epilepsy.

AB - There is a wealth of electrophysiological evidence suggesting that synchronous firing of neurons is present in the normal functioning brain. Synchrony has been postulated to be the substrate for encoding precise timing of action potentials that is necessary for pattern formation or for "binding" activities of neurons that encode distinct features of a stimulus. A hallmark of neural activity during a seizure is the appearance of synchronous events throughout the brain. Synchrony, however, is not exclusively found during a seizure but seems to be ubiquitous under normal conditions and, indeed, may be crucial for information processing. This chapter focuses on recent experimental and theoretical studies of synchrony in feedforward neural networks. This network is the backbone for information transfer in the brain given that signals must be propagated from neuron to neuron, from one nucleus to another, and from one brain region to another. Epileptiform activity may represent an aberrant manifestation of functional synchrony. If so, then understanding the mechanism by which synchrony propagates normally through a neural network might provide clues as to how the synchrony becomes pathological and may suggest preventive measures. As in feedforward networks, epileptiform activity originates from a focal point and then spreads rapidly in a stereotypic manner through a sequence of cortical areas. The simplicity of the feedforward network means that both experimental and rigorous theoretical approaches can be used to derive very general principles, which may then be applied to the study of epilepsy.

UR - http://www.scopus.com/inward/record.url?scp=84882480408&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84882480408&partnerID=8YFLogxK

U2 - 10.1016/B978-012373649-9.50023-5

DO - 10.1016/B978-012373649-9.50023-5

M3 - Chapter

SN - 9780123736499

SP - 304

EP - 316

BT - Computational Neuroscience in Epilepsy

PB - Elsevier Inc.

ER -