We consider a network of networks consisting of small input neural network and four small-world subnetworks Hodgkin-Huxley neurons. Input network receives an external signal and transfers it to subnetworks via excitatory couplings while the subnets interact with each other via inhibitory couplings. We show that the subnets are divided into 2 clusters under the influence of an inhibitory couplings between them. The synchronization indexes of subnetworks periodically change in time. We found that SIs can oscillate either in-phase or anti-phase depending on the couplings between subnetworks.
We propose to use the chimera-like state for stimulus classification in a spiking neural network of bistable HH neurons. As a stimulus, we use an external pulsed current applied to the network. Additive noise makes the neurons nonidentical so that the external pulse switches only a part of the neurons from the resting to the oscillatory state depending on the pulse amplitude. For classification, we use the neural network and two output neurons. The network is trained on two external pulses with different amplitudes to adjust coupling strengths between neurons in the main network and output neurons. We investigate influence of inhibitory coupling between output neurons on classification of input signal with different amplitudes.
We consider two small-world networks of Hodgkin-Huxley neurons interacting via inhibitory coupling. We found that synchronization indices (SI) in both networks oscillate periodically in time, so that time intervals of high SI alternate with time intervals of low SI. Depending on the coupling strength, the two coupled networks can be in the regime of either in-phase or anti-phase synchronization. We suppose that the inherent mechanism behind such a behavior lies in the cognitive resource redistribution between neuronal ensembles of the brain.
We investigate the dynamics of individual Hodgkin-Huxley neuron in a multistable area where both stable fixed point and stable limit cycle coexist. We demonstrate a possibility of controlling neuron dynamics by a short pulse of the constant external current. Depending on the pulse time, duration and amplitude it can switch the neuron state from resting to oscillatory one and vice versa. We investigate the possibility of controlling the dynamics of a network of 100 bistable Hodgkin-Huxley neurons by a short external current pulse. We show that for certain values of the pulse parameters, such as amplitude, time length, and applying time, the pulse can force some neurons to change their dynamics.
We investigate the dynamics of the networks of 100 identical bistable Hodgkin-Huxley neurons with scale-free, small-world and random topologies. For all of them, we discover a phenomenon when one part of the neurons are in the resting state, while the other one is in the oscillatory regime in a certain area of coupling strength and external current amplitude. We investigate this phenomenon and explain it by neuron interaction similar to the short pulse of external current which is able to switch the neuron regime from resting to oscillatory one and vice versa. We find the differences on this phenomenon for different topologies and investigate the evolution of it with increasing of external current.
In this paper we numerically simulate a two-layer network of coupled Hodgkin-Huxley neurons for modulating a processing visual perception by the human brain. We investigate the influence of the external stimulus amplitude on the dynamics of second layer neurons. We discover coherent resonance phenomenon in the system: there is an area of external stimulus amplitude when both SNR and characteristic correlation time are maximal. We also analyze the influence of internal noise amplitude on the system dynamics.
In this paper, we study the complex multi-scale network of nonlocally coupled oscillators for the appearance of chimera states. Chimera is a special state in which, in addition to the asynchronous cluster, there are also completely synchronous parts in the system. We show that the increase of nodes in subgroups leads to the destruction of the synchronous interaction within the common ring and to the narrowing of the chimera region.
In this paper we study the spiking behaviour of a neuronal network consisting of Rulkov elements. We find that the regularity of this behaviour maximizes at a certain level of environment noise. This effect referred to as coherence resonance is demonstrated in a random complex network of Rulkov neurons. An external stimulus added to some of neurons excites them, and then activates other neurons in the network. The network coherence is also maximized at the certain stimulus amplitude.
In the present paper, the possibility of classification by artificial neural networks of a certain architecture of ambiguous images is investigated using the example of the Necker cube from the experimentally obtained EEG recording data of several operators. The possibilities of artificial neural network classification of ambiguous images are investigated in the different frequency ranges of EEG recording signals.
In this paper we study the spiking behaviour of a neuronal network consisting of 100 Rulkov elements coupled to each other with randomly chosen coupling strength. We find periodical grouping forming in the signal from all neurons in the network. We discovered the phenomenon of coherent resonance when signal-to-noise ration takes the maximum value at certain values of such parameters as number of neurons in the system, number of stimulated neurons, amplitude of external stimulus and amplitude of internal noise.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.