Collective Dynamics in Sparse Networks

Luccioli, Stefano; Olmi, Simona; Politi, Antonio; Torcini, Alessandro

doi:10.1103/physrevlett.109.138103

Cited by 52 publications

(63 citation statements)

References 28 publications

(39 reference statements)

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“…Under strong coupling strengths, neuron networks of equal neurons support the appearance of complete synchronisation (CS)17181920, as shown in Fig. 1.…”

mentioning

confidence: 87%

Weak connections form an infinite number of patterns in the brain

Ren

Bai

Baptista

et al. 2017

Sci Rep

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Recently, much attention has been paid to interpreting the mechanisms for memory formation in terms of brain connectivity and dynamics. Within the plethora of collective states a complex network can exhibit, we show that the phenomenon of Collective Almost Synchronisation (CAS), which describes a state with an infinite number of patterns emerging in complex networks for weak coupling strengths, deserves special attention. We show that a simulated neuron network with neurons weakly connected does produce CAS patterns, and additionally produces an output that optimally model experimental electroencephalograph (EEG) signals. This work provides strong evidence that the brain operates locally in a CAS regime, allowing it to have an unlimited number of dynamical patterns, a state that could explain the enormous memory capacity of the brain, and that would give support to the idea that local clusters of neurons are sufficiently decorrelated to independently process information locally.

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“…Under strong coupling strengths, neuron networks of equal neurons support the appearance of complete synchronisation (CS)17181920, as shown in Fig. 1.…”

mentioning

confidence: 87%

Weak connections form an infinite number of patterns in the brain

Ren

Bai

Baptista

et al. 2017

Sci Rep

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“…This means synaptic currents nearly cancel on average, but feature strong fluctuations, giving rise to sustained irregular spiking [4]. Well-established results show that such strongly recurrent networks operating in a balanced regime can produce chaotic dynamics in a range of settings, from abstract firing rate models with random connectivity [5] to networks of spiking units with excitatory and inhibitory cell classes [2, 6–8]. Chaos implies that the network dynamics depend very sensitively on network states, so that tiny perturbations to initial conditions may lead to large effects over time.…”

Section: Introductionmentioning

confidence: 99%

Encoding in Balanced Networks: Revisiting Spike Patterns and Chaos in Stimulus-Driven Systems

et al. 2016

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Highly connected recurrent neural networks often produce chaotic dynamics, meaning their precise activity is sensitive to small perturbations. What are the consequences of chaos for how such networks encode streams of temporal stimuli? On the one hand, chaos is a strong source of randomness, suggesting that small changes in stimuli will be obscured by intrinsically generated variability. On the other hand, recent work shows that the type of chaos that occurs in spiking networks can have a surprisingly low-dimensional structure, suggesting that there may be room for fine stimulus features to be precisely resolved. Here we show that strongly chaotic networks produce patterned spikes that reliably encode time-dependent stimuli: using a decoder sensitive to spike times on timescales of 10’s of ms, one can easily distinguish responses to very similar inputs. Moreover, recurrence serves to distribute signals throughout chaotic networks so that small groups of cells can encode substantial information about signals arriving elsewhere. A conclusion is that the presence of strong chaos in recurrent networks need not exclude precise encoding of temporal stimuli via spike patterns.

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“…Several features of neural networks have been shown to influence synchronization of neural populations, e.g., neural excitability [10], the presence of inhibitory component [11] and the structure of connections [12]. In recent years, a crucial role of inhibitory component in neural ensembles has been proposed to reproduce specific patterns observed in cortical regions of the brain.…”

Section: Introductionmentioning

confidence: 99%

Synchronization and long-time memory in neural networks with inhibitory hubs and synaptic plasticity

et al. 2017

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We investigate the dynamical role of inhibitory and highly connected nodes (hub) in synchronization and input processing of leaky-integrate-and-fire neural networks with short term synaptic plasticity. We take advantage of a heterogeneous mean-field approximation to encode the role of network structure and we tune the fraction of inhibitory neurons f I and their connectivity level to investigate the cooperation between hub features and inhibition. We show that, depending on f I , highly connected inhibitory nodes strongly drive the synchronization properties of the overall network through dynamical transitions from synchronous to asynchronous regimes. Furthermore, a metastable regime with long memory of external inputs emerges for a specific fraction of hub inhibitory neurons, underlining the role of inhibition and connectivity also for input processing in neural networks.

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Collective Dynamics in Sparse Networks

Cited by 52 publications

References 28 publications

Weak connections form an infinite number of patterns in the brain

Weak connections form an infinite number of patterns in the brain

Encoding in Balanced Networks: Revisiting Spike Patterns and Chaos in Stimulus-Driven Systems

Synchronization and long-time memory in neural networks with inhibitory hubs and synaptic plasticity

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