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Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks.

de Santos-Sierra D, Sanchez-Jimenez A, Garcia-Vellisca MA, Navas A, Villacorta-Atienza JA - Front Comput Neurosci (2015)

Bottom Line: We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties.In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship.This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

View Article: PubMed Central - PubMed

Affiliation: Group of Biometrics, Biosignals and Security, Research Centre for Smart Buildings and Energy Efficiency (CeDInt), Technical University of Madrid Madrid, Spain ; Laboratory of Computational System Biology, Center for Biomedical Technology, Technical University of Madrid Madrid, Spain.

ABSTRACT
Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

No MeSH data available.


Related in: MedlinePlus

Anticipating spike synchronization in Hindmarsh–Rose neurons. (A) Basic neural network that will be used in the paper. The master (M) and slave (S) neurons are connected through n intermediary neurons (ij with j = 1, …, n). All of them are modeled as Hindmarsh–Rose neurons with a specific arrangement of their oscillation frequencies (see Section Materials and Methods for details). (B) Chaotic dynamical state (bursting) of the neurons (blue for the master neuron activity). (C) With no intermediaries, i.e., direct master-slave coupling, spike anticipation appears (blue line for master and red line for slave), keeping the phase synchronization (inset). (D) When three intermediary neurons exist with a proper distribution of their firing frequencies (see text) spike anticipation is up to fourfold enhanced (measured as the difference between the maxima of the closest master and slave action potentials).
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Figure 1: Anticipating spike synchronization in Hindmarsh–Rose neurons. (A) Basic neural network that will be used in the paper. The master (M) and slave (S) neurons are connected through n intermediary neurons (ij with j = 1, …, n). All of them are modeled as Hindmarsh–Rose neurons with a specific arrangement of their oscillation frequencies (see Section Materials and Methods for details). (B) Chaotic dynamical state (bursting) of the neurons (blue for the master neuron activity). (C) With no intermediaries, i.e., direct master-slave coupling, spike anticipation appears (blue line for master and red line for slave), keeping the phase synchronization (inset). (D) When three intermediary neurons exist with a proper distribution of their firing frequencies (see text) spike anticipation is up to fourfold enhanced (measured as the difference between the maxima of the closest master and slave action potentials).

Mentions: As pointed previously, the basic network studied in this work is mathematically described by Equation 1 and depicted in Figure 1A. The master neuron displays a chaotic dynamics structured in bursts (sequences of spikes separated by silent intervals), shown in Figure 1B. The unidirectional coupling of the neurons in the network forces the synchronization of their dynamics, where spike anticipation appears according to the number of intermediary neurons. Figure 1C illustrates this anticipation by showing superimposed two synchronized spikes of the master (blue curve) and the slave (red curve) neurons when no intermediaries are present (n = 0); the inset shows the synchronized state by correlating the master and slave interspike intervals or ISI. On the other hand, the introduction of three intermediary neurons (n = 3) enhances four-fold the spike anticipation, as shown in Figure 1D, keeping the synchronization of the system (inset).


Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks.

de Santos-Sierra D, Sanchez-Jimenez A, Garcia-Vellisca MA, Navas A, Villacorta-Atienza JA - Front Comput Neurosci (2015)

Anticipating spike synchronization in Hindmarsh–Rose neurons. (A) Basic neural network that will be used in the paper. The master (M) and slave (S) neurons are connected through n intermediary neurons (ij with j = 1, …, n). All of them are modeled as Hindmarsh–Rose neurons with a specific arrangement of their oscillation frequencies (see Section Materials and Methods for details). (B) Chaotic dynamical state (bursting) of the neurons (blue for the master neuron activity). (C) With no intermediaries, i.e., direct master-slave coupling, spike anticipation appears (blue line for master and red line for slave), keeping the phase synchronization (inset). (D) When three intermediary neurons exist with a proper distribution of their firing frequencies (see text) spike anticipation is up to fourfold enhanced (measured as the difference between the maxima of the closest master and slave action potentials).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4663270&req=5

Figure 1: Anticipating spike synchronization in Hindmarsh–Rose neurons. (A) Basic neural network that will be used in the paper. The master (M) and slave (S) neurons are connected through n intermediary neurons (ij with j = 1, …, n). All of them are modeled as Hindmarsh–Rose neurons with a specific arrangement of their oscillation frequencies (see Section Materials and Methods for details). (B) Chaotic dynamical state (bursting) of the neurons (blue for the master neuron activity). (C) With no intermediaries, i.e., direct master-slave coupling, spike anticipation appears (blue line for master and red line for slave), keeping the phase synchronization (inset). (D) When three intermediary neurons exist with a proper distribution of their firing frequencies (see text) spike anticipation is up to fourfold enhanced (measured as the difference between the maxima of the closest master and slave action potentials).
Mentions: As pointed previously, the basic network studied in this work is mathematically described by Equation 1 and depicted in Figure 1A. The master neuron displays a chaotic dynamics structured in bursts (sequences of spikes separated by silent intervals), shown in Figure 1B. The unidirectional coupling of the neurons in the network forces the synchronization of their dynamics, where spike anticipation appears according to the number of intermediary neurons. Figure 1C illustrates this anticipation by showing superimposed two synchronized spikes of the master (blue curve) and the slave (red curve) neurons when no intermediaries are present (n = 0); the inset shows the synchronized state by correlating the master and slave interspike intervals or ISI. On the other hand, the introduction of three intermediary neurons (n = 3) enhances four-fold the spike anticipation, as shown in Figure 1D, keeping the synchronization of the system (inset).

Bottom Line: We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties.In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship.This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

View Article: PubMed Central - PubMed

Affiliation: Group of Biometrics, Biosignals and Security, Research Centre for Smart Buildings and Energy Efficiency (CeDInt), Technical University of Madrid Madrid, Spain ; Laboratory of Computational System Biology, Center for Biomedical Technology, Technical University of Madrid Madrid, Spain.

ABSTRACT
Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

No MeSH data available.


Related in: MedlinePlus