Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links

Teramae, Jun-nosuke; Tsubo, Yasuhiro; Fukai, Tomoki

doi:10.1038/srep00485

Cited by 165 publications

(197 citation statements)

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“…1A), matching experimental reports (Song et al 2005;Perin et al 2011). Heterogeneity in synaptic efficacy has important consequences for information transmission (Teramae et al 2012).…”

Section: Conductance-based Network Model With Heterogeneous Synaptic supporting

confidence: 51%

“…An average synaptic connection causes only small postsynaptic depolarization, particularly when membrane conductance is high during synaptic bombardments (Teramae et al 2012;Chicharro and Panzeri 2014;Kumar et al 2008;Destexhe et al 2003;Troyer and Miller 1997). As a result, it is difficult to predict sequential firing from synaptic weights alone.…”

Section: Discussionmentioning

confidence: 99%

“…Strong cortical connections are a desirable experimental target because of their proposed roles in information processing (Song et al 2005;Litwin-Kumar et al 2011;Teramae et al 2012;Gururangan et al 2014). However, they are rare in the population of all connections.…”

Section: Discussionmentioning

confidence: 99%

“…A large number of presynaptic inputs contribute to the high conductance, depolarized state of postsynaptic neurons during circuit activity (Brunel 2000;Destexhe et al 2003;MacLean et al 2005; Watson et al 2008;Kumar et al 2008;Neske et al 2015), limiting the influence of any one synaptic connection (Teramae et al 2012;Chicharro and Panzeri 2014). The large number of spikes that comprise a multineuronal activity pattern and the impossibility of monitoring all neurons in neocortex mean that correlations in spiking between neurons are not necessarily indicative of a monosynaptic connection (Gerstein and Perkel 1969).…”

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confidence: 99%

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Multineuronal activity patterns identify selective synaptic connections under realistic experimental constraints

Chambers

MacLean

2015

Journal of Neurophysiology

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Chambers B, MacLean JN. Multineuronal activity patterns identify selective synaptic connections under realistic experimental constraints. J Neurophysiol 114: 1837-1849. First published July 22, 2015 doi:10.1152/jn.00429.2015.-Structured multineuronal activity patterns within local neocortical circuitry are strongly linked to sensory input, motor output, and behavioral choice. These reliable patterns of pairwise lagged firing are the consequence of connectivity since they are not present in rate-matched but unconnected Poisson nulls. It is important to relate multineuronal patterns to their synaptic underpinnings, but it is unclear how effectively statistical dependencies in spiking between neurons identify causal synaptic connections. To assess the feasibility of mapping function onto structure we used a network model that showed a diversity of multineuronal activity patterns and replicated experimental constraints on data acquisition. Using an iterative Bayesian inference algorithm, we detected a select subset of monosynaptic connections substantially more precisely than correlation-based inference, a common alternative approach. We found that precise inference of synaptic connections improved with increasing numbers of diverse multineuronal activity patterns in contrast to increased observations of a single pattern. Surprisingly, neuronal spiking was most effective and precise at revealing causal synaptic connectivity when the lags considered by the iterative Bayesian algorithm encompassed the timescale of synaptic conductance and integration (ϳ10 ms), rather than synaptic transmission time (ϳ2 ms), highlighting the importance of synaptic integration in driving postsynaptic spiking. Last, strong synaptic connections were detected preferentially, underscoring their special importance in cortical computation. Even after simulating experimental constraints, top down approaches to cortical connectivity, from function to structure, identify synaptic connections underlying multineuronal activity. These select connections are closely tied to cortical processing. activity maps; reliable timing; two-photon calcium ion imaging; computational neuroscience SYNAPTIC CONNECTIONS ARE FUNDAMENTAL to neocortical computation. They are responsible for instantiating and constraining the multineuronal activity patterns that underlie sensation and behavior (Harvey et al. 2012;O'Connor et al. 2013). If we are to understand information processing, it is crucial to map cortical activity at the level of neurons and their synaptic relationships. For example, paired patch-clamp recordings during quiescence have revealed dense connectivity in local excitatory networks (Song et al. 2005;Neske et al. 2015), yet, circuit spiking activity is sparse and diverse, in ways that are not easily predicted from connection patterns alone (Barth and Poulet 2012). Functional connectivity maps are an important bridge between static connectivity and dynamic information processing.It is challenging to link activity to underlying connectivity. A large numb...

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“…1A), matching experimental reports (Song et al 2005;Perin et al 2011). Heterogeneity in synaptic efficacy has important consequences for information transmission (Teramae et al 2012).…”

Section: Conductance-based Network Model With Heterogeneous Synaptic supporting

confidence: 51%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Multineuronal activity patterns identify selective synaptic connections under realistic experimental constraints

Chambers

MacLean

2015

Journal of Neurophysiology

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“…The PRF may be generalized for pulses of different amplitude, which allows to account for inhomogeneous distribution of synaptic weights [26,27]. Coupling delays can 10 also be easily included [48][49][50][51][52].…”

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confidence: 99%

Phase response function for oscillators with strong forcing or coupling

Klinshov

Yanchuk

Stephan

et al. 2017

EPL

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Phase response curve (PRC) is an extremely useful tool for studying the response of oscillatory systems, e.g. neurons, to sparse or weak stimulation. Here we develop a framework for studying the response to a series of pulses which are frequent or/and strong so that the standard PRC fails. We show that in this case, the phase shift caused by each pulse depends on the history of several previous pulses. We call the corresponding function which measures this shift the phase response function (PRF). As a result of the introduction of the PRF, a variety of oscillatory systems with pulse interaction can be reduced to phase systems. The main assumption of the classical PRC model, i.e. that the effect of the stimulus vanishes before the next one arrives, is no longer a restriction in our approach. However, as a result of the phase reduction, the system acquires memory, which is not just a technical nuisance but an intrinsic property relevant to strong stimulation. We illustrate the PRF approach by its application to various systems, such as Morris-Lecar, Hodgkin-Huxley neuron models, and others. We show that the PRF allows predicting the dynamics of forced and coupled oscillators even when the PRC fails. Thus, the PRF provides an effective tool that may be used for simulation of neural, chemical, optic oscillators, etc.A variety of physical, chemical, biological, and other systems exhibit periodic behaviors.The state of such a system can be naturally determined by its phase [1], that is, the single variable indicating the position of the system within its cycle. The concept of the phase proved to be exceptionally useful for the study of driven and coupled oscillators [1][2][3].In order to describe the response of oscillators to an external force or coupling the so- called phase response curve (PRC) is widely used. The PRC defines the oscillator's response to a single short stimulus (pulse). The PRC can be calculated numerically or measured experimentally for oscillatory systems of different origin [4]. These properties made it a useful tool for the study of forced or coupled oscillators [5][6][7][8][9][10][11], and it is especially effective in neuroscience where the interactions are mediated by pulses. If the pulse arrivals are separated by sufficiently long time intervals, the transient caused by a pulse vanishes before the next one comes. From the theoretical point of view, it means that the system returns to the vicinity of its stable limit cycle before the next pulse arrives, see Fig. 1(a). In this case the effect of each pulse can be described by the classical PRC Z(ϕ), which determines the resulting phase shift given that the pulse arrived at the phase ϕ. Another case when the PRCs are useful is when the forcing is continuous in time but weak ( Fig. 1(b)). In this case the system remains close to the limit cycle, and the phase dynamics can be described by the so-called infinitesimal phase response curve [12].Therefore, the PRC-based approach is applicable for either weak or sparse stimulation.However, in many re...

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Cortical Networks with Lognormal Synaptic Connectivity and Their Implications in Neuronal Avalanches

Fukai

Klinshov

Teramae

2014

Criticality in Neural Systems

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Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links

Cited by 165 publications

References 61 publications

Multineuronal activity patterns identify selective synaptic connections under realistic experimental constraints

Multineuronal activity patterns identify selective synaptic connections under realistic experimental constraints

Phase response function for oscillators with strong forcing or coupling

Cortical Networks with Lognormal Synaptic Connectivity and Their Implications in Neuronal Avalanches

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