Deterministic dynamics emerging from a cortical functional architecture

Siegel, Ralph M.; Read, Heather L.

doi:10.1016/s0893-6080(01)00045-4

Cited by 11 publications

(11 citation statements)

References 88 publications

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“…For example, computational models suggest that pooling of synaptic inputs and synaptic network dynamics could generate synaptic input variability that in turn contributes to spike-timing jitter in recipient neurons (Butts and Goldman 2006;Siegel and Read 2001). Accordingly, for the rat, it is plausible that differences in sound-evoked jitter in auditory cortical neurons stem from differences in their corresponding thalamocortical (Storace et al , 2011(Storace et al , 2012 or corticocortical inputs (Mascagni et al 1993;Romanski and LeDoux 1993;Shi and Cassell 1997).…”

Section: Candidate Biological Mechanisms Contributing To Spiketiming mentioning

confidence: 99%

Neural spike-timing patterns vary with sound shape and periodicity in three auditory cortical fields

Lee

Osman

Volgushev

et al. 2016

Journal of Neurophysiology

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Lee CM, Osman AF, Volgushev M, Escabí MA, Read HL. Neural spike-timing patterns vary with sound shape and periodicity in three auditory cortical fields. J Neurophysiol 115: 1886 -1904, 2016. First published February 3, 2016 doi:10.1152/jn.00784.2015.-Mammals perceive a wide range of temporal cues in natural sounds, and the auditory cortex is essential for their detection and discrimination. The rat primary (A1), ventral (VAF), and caudal suprarhinal (cSRAF) auditory cortical fields have separate thalamocortical pathways that may support unique temporal cue sensitivities. To explore this, we record responses of single neurons in the three fields to variations in envelope shape and modulation frequency of periodic noise sequences. Spike rate, relative synchrony, and first-spike latency metrics have previously been used to quantify neural sensitivities to temporal sound cues; however, such metrics do not measure absolute spike timing of sustained responses to sound shape. To address this, in this study we quantify two forms of spike-timing precision, jitter, and reliability. In all three fields, we find that jitter decreases logarithmically with increase in the basis spline (B-spline) cutoff frequency used to shape the sound envelope. In contrast, reliability decreases logarithmically with increase in sound envelope modulation frequency. In A1, jitter and reliability vary independently, whereas in ventral cortical fields, jitter and reliability covary. Jitter time scales increase (A1 Ͻ VAF Ͻ cSRAF) and modulation frequency upper cutoffs decrease (A1 Ͼ VAF Ͼ cSRAF) with ventral progression from A1. These results suggest a transition from independent encoding of shape and periodicity sound cues on short time scales in A1 to a joint encoding of these same cues on longer time scales in ventral nonprimary cortices.

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Section: Candidate Biological Mechanisms Contributing To Spiketiming mentioning

confidence: 99%

Neural spike-timing patterns vary with sound shape and periodicity in three auditory cortical fields

Lee

Osman

Volgushev

et al. 2016

Journal of Neurophysiology

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“…There is also growing appreciation of the fact that cortical circuit function is expressed in real time by the dynamics of these interactions (Lopes da Silva et al, 1994;Friston & Price 2001;Siegel & Read 2001;Breakspear & Jirsa 2007). Neural context for the functional dynamics of a local cortical circuit is provided by modulations impinging on the circuit elements from other connected circuits.…”

Section: Anatomical and Physiological Foundations Of Neural Contextmentioning

confidence: 99%

The Role of Neural Context in Large-Scale Neurocognitive Network Operations

Bressler

McIntosh

2007

Understanding Complex Systems

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Brain connectivity has been a central factor in the development of theories about the mind-brain link. In its simplest form, brain connectivity analysis has revealed serial processing systems, wherein specific neural elements (neurons, neuronal assemblies, neuronal populations) cooperate to express a circumscribed function that is realized as information passes through the system in a feedforward manner. Consideration of parallel architectures provides a more complex view of system processing by revealing that each brain region may impact many other regions through direct and indirect routes, including areas from which it receives its input. Regardless of the specific architecture, the notion that cognition results from the operations of large-scale neural networks has been present in various forms throughout the history of neuroscience (Finger, 1994; Bressler 1995Bressler , 2002. For a large part of that history, it was difficult to verify this notion because most available methods only allowed investigation of small parts of the nervous system in isolation. Ideally, simultaneous measures from many parts of the brain must be analyzed to understand the operations of large-scale networks that underlie cognition. In the past few decades, advances in functional neuroimaging, including Positron Emission Tomography (PET) functional Magnetic Resonance Imaging (fMRI), and EEG/MEG-based source localization, have allowed simultaneous distributed measures of brain function to be related to cognition.This chapter examines the role of a critical aspect of brain function, which we call neural context, in the selective functioning of interacting neural systems in cognition. We define neural context as the local processing environment of a given neural element that is created by modulatory influences from other neural elements. Neural context allows the response properties of one element in a network to be profoundly affected by the status of other neural elements in that network. As a result of neural context, the relevance of a given neural element for cognitive function typically depends on the status of other interacting elements (McIntosh 1999;Bressler 2003a). By this definition, the processing performed by a given brain area may be modulated by a potentially

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“…These networks (Chang et al 2001;Segev et al 2002) provide a bridge that can link the realistic experimental system to numerous theoretical modeling approaches, which often deal effectively with one dimension (Abeles 1991; Diesmann et al 1999;Golomb and Amitai 1997;Litvak et al 2003;Osan and Ermentrout 2002;Siegal and Read 2001). Viewed as simplified models of cortical structure, such theoretical networks were used to address questions concerning the possible cortical coding schemes (Abeles 1991;Diesmann et al 1999;Litvak et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Signal Propagation Along Unidimensional Neuronal Networks

Feinerman

Segal²,

Moses³

2005

Journal of Neurophysiology

150

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Feinerman, Ofer, Menahem Segal, and Elisha Moses. Signal propagation along unidimensional neuronal networks. J Neurophysiol 94: [3406][3407][3408][3409][3410][3411][3412][3413][3414][3415][3416] 2005. First published July 27, 2005; doi:10.1152/jn.00264.2005. Dissociated neurons were cultured on lines of various lengths covered with adhesive material to obtain an experimental model system of linear signal transmission. The neuronal connectivity in the linear culture is characterized, and it is demonstrated that local spiking activity is relayed by synaptic transmission along the line of neurons to develop into a large-scale population burst. Formally, this can be treated as a onedimensional information channel. Directional propagation of both spontaneous and stimulated bursts along the line, imaged with the calcium indicator Fluo-4, revealed the existence of two different propagation velocities. Initially, a small number of neighboring neurons fire, leading to a slow, small and presumably asynchronous wave of activity. The signal then spontaneously develops to encompass much larger and further populations, and is characterized by fast propagation of high-amplitude activity, which is presumed to be synchronous. These results are well described by an existing theoretical framework for propagation based on an integrate-and-fire model.The advance of activity through a neural network reveals much about its underlying properties. In particular, the velocity in which a signal propagates may differentiate synchronous from asynchronous activity and can identify the limiting factors in transmission, whether they are synaptic or axonal. The dependency of velocities on the strength of the ␣-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) synapses has been experimentally demonstrated in bursts that propagate in slices (Golomb and Amitai 1997).In this report such questions are addressed using a novel experimental system in which dissociated hippocampal neurons are grown along one-dimensional (1-D) patterned lines. These networks (Chang et al. 2001;Segev et al. 2002) provide a bridge that can link the realistic experimental system to numerous theoretical modeling approaches, which often deal effectively with one dimension (Abeles 1991;Diesmann et al. 1999;Golomb and Amitai 1997;Litvak et al. 2003;Osan and Ermentrout 2002;Siegal and Read 2001). Viewed as simplified models of cortical structure, such theoretical networks were used to address questions concerning the possible cortical coding schemes (Abeles 1991;Diesmann et al. 1999;Litvak et al. 2003).The major advantage of using one-dimensional cultures to study propagation is that a signal has only a limited pathway. Two-dimensional (2-D) networks and slices exhibit similar activity, but may have a large number of possible paths connecting any two given points ( The first goal of the study reported herein is to characterize the culture and to demonstrate that it is indeed layered and directed along one dimension. This is done by applying pharmacological treatments an...

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Deterministic dynamics emerging from a cortical functional architecture

Cited by 11 publications

References 88 publications

Neural spike-timing patterns vary with sound shape and periodicity in three auditory cortical fields

Neural spike-timing patterns vary with sound shape and periodicity in three auditory cortical fields

The Role of Neural Context in Large-Scale Neurocognitive Network Operations

Signal Propagation Along Unidimensional Neuronal Networks

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