Complementary action of chemical and electrical synapses to perception

Borges, Fernando S.; Lameu, Ewandson L.; Batista, Antônio M.; Iarosz, Kelly C.; Baptista, Murilo S.; Viana, Ricardo L.

doi:10.1016/j.physa.2015.02.098

Cited by 10 publications

(7 citation statements)

References 18 publications

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“…To elucidate the fundamental dynamical mechanisms behind observable brain behaviour, there has been a lot of research based on simulations of neuron networks and their behaviour as a function of the intensity of the coupling strengths 13 14 15 16 17 18 19 20 and the connecting topology 21 22 23 24 . Under strong coupling strengths, neuron networks of equal neurons support the appearance of complete synchronisation (CS) 17 18 19 20 , as shown in Fig.…”

mentioning

confidence: 99%

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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confidence: 99%

Weak connections form an infinite number of patterns in the brain

Ren

Bai

Baptista

et al. 2017

Sci Rep

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“…The increase of the DR value is also associated with the increase of the number of excitatory chemical synapses [12,13]. Borges et al [14] reported the complementary effect of chemical and electrical synapses on the enhance of the DR. Protachevicz et al shown that chemical synapses can enhance DR of the neural network submitted to external stimuli [15].…”

Section: Introductionmentioning

confidence: 98%

“…In this work, we investigate the criticality and dynamic range of a cellular automaton modelling a neuronal network in which the neurons are connected by means of excitatory and inhibitory chemical synapses [12,14]. In order to understand the relationship between maximisation of the DR and the critical self-sustainable activity, we consider a refractory period in the model like the one proposed by Larremore et al [32].…”

Section: Introductionmentioning

confidence: 99%

Influence of inhibitory synapses on the criticality of excitable neuronal networks

Borges¹,

Protachevicz²,

Santos³

et al. 2020

Preprint

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In this work, we study the dynamic range of a neuronal network of excitable neurons with excitatory and inhibitory synapses. We obtain an analytical expression for the critical point as a function of the excitatory and inhibitory synaptic intensities. We also determine an analytical expression that gives the critical point value in which the maximal dynamic range occurs. Depending on the mean connection degree and coupling weights, the critical points can exhibit ceasing or ceaseless dynamics. However, the dynamic range is equal in both cases. We observe that the external stimulus mask some effects of self-sustained activity (ceaseless dynamic) in the region where the dynamic range is calculated. In these regions, the firing rate is the same for ceaseless dynamics and ceasing activity. Furthermore, we verify that excitatory and inhibitory inputs are approximately equal for a network with a large number of connections, showing excitatory-inhibitory balance as reported experimentally.

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“…In particular, neural network models have been used extensively in neuroscience applications, such as in studies of neural dynamics 5 , dynamic range [6][7][8][9] , neural synchronization [10][11][12][13][14] , flow of information [15][16][17] and brain plasticity 18,19 to name a few.…”

Section: Introductionmentioning

confidence: 99%