Limits...
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

Firing regimes of a biologically-inspired neural network under spike anticipation. (A) Neural network where the afferent information is introduced in the master neuron and conveyed to the closed loop where the neuron X is driven by the excitatory (blue flat arrow end) and inhibitory (red dot arrow end) inputs from master and slave neurons. (B) Detail of the closed loop, composed of n intermediary neurons. We denote the coupling between neuron X and master and slave by kMX and kSX, respectively. (C,D) Chaotic dynamics of the neuron X under different spike anticipation, induced by introducing different intermediaries (n = 0 and n = 3, respectively). (E,F) Statistical analysis of the neuron X activity by means of ISI histograms. Neuron X also follows the Hindmarsh–Rose model described in the Section Materials and Methods adopting the same values for the majority of the common parameters, and with J0X = 1.3, CX = 1, kMX = 3 and kSX = −3. T = 5 × 104.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4663270&req=5

Figure 2: Firing regimes of a biologically-inspired neural network under spike anticipation. (A) Neural network where the afferent information is introduced in the master neuron and conveyed to the closed loop where the neuron X is driven by the excitatory (blue flat arrow end) and inhibitory (red dot arrow end) inputs from master and slave neurons. (B) Detail of the closed loop, composed of n intermediary neurons. We denote the coupling between neuron X and master and slave by kMX and kSX, respectively. (C,D) Chaotic dynamics of the neuron X under different spike anticipation, induced by introducing different intermediaries (n = 0 and n = 3, respectively). (E,F) Statistical analysis of the neuron X activity by means of ISI histograms. Neuron X also follows the Hindmarsh–Rose model described in the Section Materials and Methods adopting the same values for the majority of the common parameters, and with J0X = 1.3, CX = 1, kMX = 3 and kSX = −3. T = 5 × 104.

Mentions: Now we explore the effect of spike anticipation in a neural network exhibiting diverse characteristics typical of both biological and artificial neural networks, as the presence of closed loops, excitatory and inhibitory synapses (responsible for generating and modulating the network dynamics), relay or intermediate neurons, convergent and divergent flux of information, etc. This network is depicted in Figure 2A, where master (blue) and slave (red) neurons are coupled to a third neuron X (green), whose dynamics will depend on spike anticipation through the master-slave synchronization. The couplings strength of neuron X with master and slave neurons are denoted by kMX and kSX, respectively, where we will consider positive values for kMX, modeling the excitatory afferent to the neuron X from the master, and negative values for kSX describing the inhibitory input to X from the slave neuron.


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)

Firing regimes of a biologically-inspired neural network under spike anticipation. (A) Neural network where the afferent information is introduced in the master neuron and conveyed to the closed loop where the neuron X is driven by the excitatory (blue flat arrow end) and inhibitory (red dot arrow end) inputs from master and slave neurons. (B) Detail of the closed loop, composed of n intermediary neurons. We denote the coupling between neuron X and master and slave by kMX and kSX, respectively. (C,D) Chaotic dynamics of the neuron X under different spike anticipation, induced by introducing different intermediaries (n = 0 and n = 3, respectively). (E,F) Statistical analysis of the neuron X activity by means of ISI histograms. Neuron X also follows the Hindmarsh–Rose model described in the Section Materials and Methods adopting the same values for the majority of the common parameters, and with J0X = 1.3, CX = 1, kMX = 3 and kSX = −3. T = 5 × 104.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Firing regimes of a biologically-inspired neural network under spike anticipation. (A) Neural network where the afferent information is introduced in the master neuron and conveyed to the closed loop where the neuron X is driven by the excitatory (blue flat arrow end) and inhibitory (red dot arrow end) inputs from master and slave neurons. (B) Detail of the closed loop, composed of n intermediary neurons. We denote the coupling between neuron X and master and slave by kMX and kSX, respectively. (C,D) Chaotic dynamics of the neuron X under different spike anticipation, induced by introducing different intermediaries (n = 0 and n = 3, respectively). (E,F) Statistical analysis of the neuron X activity by means of ISI histograms. Neuron X also follows the Hindmarsh–Rose model described in the Section Materials and Methods adopting the same values for the majority of the common parameters, and with J0X = 1.3, CX = 1, kMX = 3 and kSX = −3. T = 5 × 104.
Mentions: Now we explore the effect of spike anticipation in a neural network exhibiting diverse characteristics typical of both biological and artificial neural networks, as the presence of closed loops, excitatory and inhibitory synapses (responsible for generating and modulating the network dynamics), relay or intermediate neurons, convergent and divergent flux of information, etc. This network is depicted in Figure 2A, where master (blue) and slave (red) neurons are coupled to a third neuron X (green), whose dynamics will depend on spike anticipation through the master-slave synchronization. The couplings strength of neuron X with master and slave neurons are denoted by kMX and kSX, respectively, where we will consider positive values for kMX, modeling the excitatory afferent to the neuron X from the master, and negative values for kSX describing the inhibitory input to X from the slave neuron.

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