Predicting brain evoked response to external stimuli from temporal correlations of spontaneous activity

Sarracino, Alessandro; Arviv, Oshrit; Shriki, Oren; Arcangelis, L. de

doi:10.1103/physrevresearch.2.033355

“…While regions with shorter ACW, i.e., unimodal regions, exhibit higher amplitude during different tasks. These results support the idea that the resting state’s INT strongly shapes task-related activity and associated input processing 2 , 60 , 61 . The mechanisms of this, however, remain unclear.…”

Section: Part I: Intrinsic Neural Timescales In Rest and Task Statessupporting

confidence: 84%

“…Hence, comparison of rest ACW and task temporal receptive windows shows analogous hierarchical topographical organization. This suggests a close relationship between rest and task, i.e., rest–task modulation or interaction 56 – 60 (see below for the discussion of task-specific changes in INT).…”

Section: Part I: Intrinsic Neural Timescales In Rest and Task Statesmentioning

confidence: 99%

The brain and its time: intrinsic neural timescales are key for input processing

Golesorkhi

¹

,

Gómez‐Pilar

²

,

Zilio

³

et al. 2021

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We process and integrate multiple timescales into one meaningful whole. Recent evidence suggests that the brain displays a complex multiscale temporal organization. Different regions exhibit different timescales as described by the concept of intrinsic neural timescales (INT); however, their function and neural mechanisms remains unclear. We review recent literature on INT and propose that they are key for input processing. Specifically, they are shared across different species, i.e., input sharing. This suggests a role of INT in encoding inputs through matching the inputs’ stochastics with the ongoing temporal statistics of the brain’s neural activity, i.e., input encoding. Following simulation and empirical data, we point out input integration versus segregation and input sampling as key temporal mechanisms of input processing. This deeply grounds the brain within its environmental and evolutionary context. It carries major implications in understanding mental features and psychiatric disorders, as well as going beyond the brain in integrating timescales into artificial intelligence.

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“…The times τ 1 and τ 2 represent the correlation times in the linear approximation of the dynamical equations. Indeed, in such approximation the temporal correlation functions C xy ( t ) = ⟨ x ( t ) y (0)⟩ − ⟨ x ⟩ ⟨ y ⟩, where x and y are two observables and where the symbol ⟨···⟩ represents an average over noise in the stationary state (see Methods), can be written as the linear combinations of two exponential decays [22] (see Methods for the explicit expressions) …”

Section: Resultsmentioning

confidence: 99%

Critical behaviour of the stochastic Wilson-Cowan model

Candia

¹

,

Sarracino

²

,

Apicella

³

et al. 2021

Preprint

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Spontaneous brain activity is characterized by bursts and avalanche-like dynamics, with scale-free features typical of critical behaviour. The stochastic version of the celebrated Wilson-Cowan model has been widely studied as a system of spiking neurons reproducing non-trivial features of the neural activity, from avalanche dynamics to oscillatory behaviours. However, to what extent such phenomena are related to the presence of a genuine critical point remains elusive. Here we address this central issue, providing analytical results in the linear approximation and extensive numerical analysis. In particular, we present results supporting the existence of a bona fide critical point, where a second-order-like phase transition occurs, characterized by scale-free avalanche dynamics, scaling with the system size and a diverging relaxation time-scale. Moreover, our study shows that the observed critical behaviour falls within the universality class of the mean-field branching process, where the exponents of the avalanche size and duration distributions are, respectively, −3/2 and −2. We also provide an accurate analysis of the system behaviour as a function of the total number of neurons, focusing on the time correlation functions of the firing rate in a wide range of the parameter space.Author summaryNetworks of spiking neurons are introduced to describe some features of the brain activity, which are characterized by burst events (avalanches) with power-law distributions of size and duration. The observation of this kind of noisy behaviour in a wide variety of real systems led to the hypothesis that neuronal networks work in the proximity of a critical point. This hypothesis is at the core of an intense debate. At variance with previous claims, here we show that a stochastic version of the Wilson-Cowan model presents a phenomenology in agreement with the existence of a bona fide critical point for a particular choice of the relative synaptic weight between excitatory and inhibitory neurons. The system behaviour at this point shows all features typical of criticality, such as diverging timescales, scaling with the system size and scale-free distributions of avalanche sizes and durations, with exponents corresponding to the mean-field branching process. Our analysis unveils the critical nature of the observed behaviours.

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“…The times τ 1 and τ 2 represent the correlation times in the linear approximation of the dynamical equations. Indeed, in such approximation the temporal correlation functions C xy (t) = hx(t)y(0)i − hxihyi, where x and y are two observables and where the symbol h� � �i represents an average over noise in the stationary state (see Methods), can be written as the linear combinations of two exponential decays [35] (see Methods for the explicit expressions)…”

Section: The Stochastic Wilson-cowan Modelmentioning

confidence: 99%

Critical behaviour of the stochastic Wilson-Cowan model

Candia

¹

,

Sarracino

²

,

Apicella

³

et al. 2021

Self Cite

View full text Add to dashboard Cite

Spontaneous brain activity is characterized by bursts and avalanche-like dynamics, with scale-free features typical of critical behaviour. The stochastic version of the celebrated Wilson-Cowan model has been widely studied as a system of spiking neurons reproducing non-trivial features of the neural activity, from avalanche dynamics to oscillatory behaviours. However, to what extent such phenomena are related to the presence of a genuine critical point remains elusive. Here we address this central issue, providing analytical results in the linear approximation and extensive numerical analysis. In particular, we present results supporting the existence of a bona fide critical point, where a second-order-like phase transition occurs, characterized by scale-free avalanche dynamics, scaling with the system size and a diverging relaxation time-scale. Moreover, our study shows that the observed critical behaviour falls within the universality class of the mean-field branching process, where the exponents of the avalanche size and duration distributions are, respectively, 3/2 and 2. We also provide an accurate analysis of the system behaviour as a function of the total number of neurons, focusing on the time correlation functions of the firing rate in a wide range of the parameter space.

show abstract

Predicting brain evoked response to external stimuli from temporal correlations of spontaneous activity

Cited by 26 publications

References 35 publications

The brain and its time: intrinsic neural timescales are key for input processing

The brain and its time: intrinsic neural timescales are key for input processing

Critical behaviour of the stochastic Wilson-Cowan model

Critical behaviour of the stochastic Wilson-Cowan model

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