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

Comparison of neural activity when anticipation and no anticipation exists. (A,B) Phasic and tonic firing of neuron X under anticipation and no anticipation, respectively. (C,D) ISI histograms summarizing these different behaviors. Parameters: n = 3, J0X = 3.25, CX = 0.7. For the anticipation case kMX = 0.7 and kSX = −1 and for the no anticipation case kMX = −1 and kSX = 0.7. T = 5 × 104.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 3: Comparison of neural activity when anticipation and no anticipation exists. (A,B) Phasic and tonic firing of neuron X under anticipation and no anticipation, respectively. (C,D) ISI histograms summarizing these different behaviors. Parameters: n = 3, J0X = 3.25, CX = 0.7. For the anticipation case kMX = 0.7 and kSX = −1 and for the no anticipation case kMX = −1 and kSX = 0.7. T = 5 × 104.

Mentions: It is possible to analyse how the functional neural regime can be affected by spike anticipation in a complementary way, by taking advantage from the synchronization between master and slave neurons. Let us consider again the scenario provided by the previous neural network with n = 3, analysing the activity of neuron X. In these conditions we consider the state as “anticipation” since synchronization makes the spiking behavior of master and slave neurons equivalent but with the later advanced in time. In consequence we simulate a “no anticipation” state in the same conditions by interchanging the values of the couplings, which will be equivalent to introduce master and slave's signals in the neuron X with no anticipation, i.e., with the slave signal following the evolution of the master one. The results are shown in Figure 3, where Figures 3A,B depict the neuron X activity in anticipation and no anticipation states, respectively, and where Figures 3C,D summarize by ISI histograms. These results reveal how spike anticipation can change dramatically the spiking behavior from tonic spiking to phasic behavior, with the appearance of periodic spike bursts.


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)

Comparison of neural activity when anticipation and no anticipation exists. (A,B) Phasic and tonic firing of neuron X under anticipation and no anticipation, respectively. (C,D) ISI histograms summarizing these different behaviors. Parameters: n = 3, J0X = 3.25, CX = 0.7. For the anticipation case kMX = 0.7 and kSX = −1 and for the no anticipation case kMX = −1 and kSX = 0.7. T = 5 × 104.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 3: Comparison of neural activity when anticipation and no anticipation exists. (A,B) Phasic and tonic firing of neuron X under anticipation and no anticipation, respectively. (C,D) ISI histograms summarizing these different behaviors. Parameters: n = 3, J0X = 3.25, CX = 0.7. For the anticipation case kMX = 0.7 and kSX = −1 and for the no anticipation case kMX = −1 and kSX = 0.7. T = 5 × 104.
Mentions: It is possible to analyse how the functional neural regime can be affected by spike anticipation in a complementary way, by taking advantage from the synchronization between master and slave neurons. Let us consider again the scenario provided by the previous neural network with n = 3, analysing the activity of neuron X. In these conditions we consider the state as “anticipation” since synchronization makes the spiking behavior of master and slave neurons equivalent but with the later advanced in time. In consequence we simulate a “no anticipation” state in the same conditions by interchanging the values of the couplings, which will be equivalent to introduce master and slave's signals in the neuron X with no anticipation, i.e., with the slave signal following the evolution of the master one. The results are shown in Figure 3, where Figures 3A,B depict the neuron X activity in anticipation and no anticipation states, respectively, and where Figures 3C,D summarize by ISI histograms. These results reveal how spike anticipation can change dramatically the spiking behavior from tonic spiking to phasic behavior, with the appearance of periodic spike bursts.

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