Stable propagation of synchronous spiking in cortical neural networks

Diesmann, Markus; Gewaltig, Marc‐Oliver; Aertsen, Ad

doi:10.1038/990101

Cited by 908 publications

(901 citation statements)

References 25 publications

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“…Precise spike times can lead to synchronous spike volleys in pools of neurons that can propagate from pool to pool in model 74 and in vitro 8 feedforward networks with a precision that depends on a number of physiological parameters. The sequential activation of multiple pools is referred to as a synfire chain (or as 'cortical songs' 75 ).…”

Section: Synfire Chains In Cortical Networkmentioning

confidence: 99%

Regulation of spike timing in visual cortical circuits

2008

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A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role.Reliability and precision are two different quantities. When you make an appointment with your friend, she can either keep the appointment or not show up at all. If she does show up, she might or might not be on time. The former uncertainty is related to reliability, whereas the latter is related to precision. When the same stimulus waveform is repeatedly injected at the soma of a neuron in vitro (FIG. 1a), a similar spike train is obtained on each trial 1,2 (FIG. 1b). When approximately the same number of spikes occur on each trial the neuron is said to be reliable, whereas when the spikes occur almost at the same time across trials it is said to be precise (FIG. 1c). For a single neuron, the potential information content of precise and reliable spike times is many times larger than that which is contained in the firing rate, which is averaged across a typical interval of a hundred milliseconds [3][4][5][6] . The information contained in spike timing is available immediately, rather than after an averaging period. Furthermore, the timing of patterns of spikes can potentially transmit even more information than the timing of the individual constituent spikes 3,7 . The potential relevance of spike patterns becomes apparent when we consider neurons at the population level: when a group of similar neurons (a 'pool') produces precise and reliable spike trains, the neurons they project to receive volleys of synchronous spikes 8,9 . This opens up the possibility of communicating between different cortical areas through synchronous spike volleys.In contrast to the in vitro situation described above, in the intact cortex most excitatory synaptic inputs arrive at the dendrites rather than at the soma (FIG. 1d), and synaptic transmission is typically unreliable [10][11][12][13] . Furthermore, most of these dendritic inputs are not directly related to ongoing sensory stimulation; rather, they reflect spatiotemporally structured internal activity. Therefore, when the same stimulus is presented repeatedly, the resulting spike trains are usually neither precise nor reliable when they are aligned to the stimulus onset 6 . Instead, HHMI Author ManuscriptHHMI Author Manuscript HHMI Author Manuscript neural activity in vivo might be dominated by internally generated complex reverberations or rhythmic oscillations, and precise and reliable sp...

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Section: Synfire Chains In Cortical Networkmentioning

confidence: 99%

Regulation of spike timing in visual cortical circuits

2008

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“…On the other hand, the duration of the cluster reveals the nature of propagation. For instance, nLFPs linked through a long series of time bins spanning dozens of milliseconds might reflect local synchronized activity, which propagates either in the form of waves (Prechtl, Cohen et al, 1997;Ermentrout and Kleinfeld, 2001) or synfire chains (Abeles, 1992;Diesmann, Gewaltig et al, 1999;Vogels and Abbott, 2005). In contrast, short-lived, but spatially extended clusters might represent tightly synchronized population spikes that can arise from reciprocally coupled neuronal populations (Traub, Whittington et al, 1996).…”

Section: Nlfp Clusters Reflect Precise Spatiotemporal Patterns Of Synmentioning

confidence: 99%

“…The distribution mechanism would be reflected in the ability of a local, synchronized group to trigger synchronization at distant sites in the network according to e.g. a synfire chain mechanism (Abeles, 1992;Diesmann, Gewaltig et al, 1999). Accordingly, an nLFP cluster would represent a cascade of topplings on the grid.…”

Section: Self-organized Criticality As a Homeostatic Principle Duringmentioning

confidence: 99%

Homeostasis of neuronal avalanches during postnatal cortex development in vitro

Stewart

Plenz

2008

Journal of Neuroscience Methods

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Cortical networks in vivo and in vitro are spontaneously active in the absence of inputs, generating highly variable bursts of neuronal activity separated by up to seconds of quiescence. Previous measurements in adult rat cortex revealed an intriguing underlying organization of these dynamics, termed neuronal avalanches, which is indicative of a critical network state. Here we demonstrate that neuronal avalanches persist throughout development in cortical slice cultures from newborn rats. More specifically, we find that in spite of large variations of average rate in activity, spontaneous bursts occur with power-law distributed sizes (exponent -1.5) and a critical branching parameter close to 1. Our findings suggest that cortical networks homeostatically regulate a critical state during postnatal maturation.

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“…A simple model for this type of processing is a feedforward network, in which each neuron in a given layer receives multiple synaptic inputs from neurons in the previous layer (Hebb, 1949;Abeles, 1991). The issue of propagation of spiking activity (firing rates or synchrony) in isolated feedforward networks (FFNs) has been addressed in several theoretical studies (Diesmann et al, 1999;Gerstner, 2000;Câteau and Fukai, 2001;Gewaltig et al, 2001;van Rossum et al, 2002;Litvak et al, 2003) and in experiments (Reyes, 2003). Numerical simulations of FFNs showed that excitation of the first group of neurons with a volley of near synchronous spikes may result in increased synchrony of the spike volley, which remains stable in subsequent layers (Diesmann et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

“…The issue of propagation of spiking activity (firing rates or synchrony) in isolated feedforward networks (FFNs) has been addressed in several theoretical studies (Diesmann et al, 1999;Gerstner, 2000;Câteau and Fukai, 2001;Gewaltig et al, 2001;van Rossum et al, 2002;Litvak et al, 2003) and in experiments (Reyes, 2003). Numerical simulations of FFNs showed that excitation of the first group of neurons with a volley of near synchronous spikes may result in increased synchrony of the spike volley, which remains stable in subsequent layers (Diesmann et al, 1999). Thus, propagating synchrony in the FFN has been proposed as a model to explain the occurrence of task-related precise spike patterns observed in behaving primates (Abeles et al, 1993;Nicolelis et al, 1995;Riehle et al, 1997;Prut et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Conditions for Propagating Synchronous Spiking and Asynchronous Firing Rates in a Cortical Network Model

Kumar

Rotter²,

Aertsen³

2008

J. Neurosci.

185

246

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. Here, we show that by modeling synapses as conductance transients, rather than current sources, it is possible to embed and propagate transient synchrony in the FFN, without destabilizing the background network dynamics. However, the network activity has a strong impact on the type of activity that can be propagated in the embedded FFN. Global synchrony and high firing rates in the embedding network prohibit the propagation of both, synchronous and asynchronous spiking activity. In contrast, asynchronous low-rate network states support the propagation of both, synchronous spiking and asynchronous, but only low firing rates. In either case, spiking activity tends to synchronize as it propagates, challenging the feasibility to transmit information in asynchronous firing rates. Finally, asynchronous network activity allows to embed more than one FFN, with the amount of cross talk depending on the degree of overlap in the FFNs. This opens the possibility of computational mechanisms using transient synchrony among the activities in multiple FFNs.

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Stable propagation of synchronous spiking in cortical neural networks

Cited by 908 publications

References 25 publications

Regulation of spike timing in visual cortical circuits

Regulation of spike timing in visual cortical circuits

Homeostasis of neuronal avalanches during postnatal cortex development in vitro

Conditions for Propagating Synchronous Spiking and Asynchronous Firing Rates in a Cortical Network Model

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