7,298 research outputs found
Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent
Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. In addition, cognitive systems cannot be properly appreciated without taking into account brain–body– environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly, it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour
A Model of an Oscillatory Neural Network with Multilevel Neurons for Pattern Recognition and Computing
The current study uses a novel method of multilevel neurons and high order
synchronization effects described by a family of special metrics, for pattern
recognition in an oscillatory neural network (ONN). The output oscillator
(neuron) of the network has multilevel variations in its synchronization value
with the reference oscillator, and allows classification of an input pattern
into a set of classes. The ONN model is implemented on thermally-coupled
vanadium dioxide oscillators. The ONN is trained by the simulated annealing
algorithm for selection of the network parameters. The results demonstrate that
ONN is capable of classifying 512 visual patterns (as a cell array 3 * 3,
distributed by symmetry into 102 classes) into a set of classes with a maximum
number of elements up to fourteen. The classification capability of the network
depends on the interior noise level and synchronization effectiveness
parameter. The model allows for designing multilevel output cascades of neural
networks with high net data throughput. The presented method can be applied in
ONNs with various coupling mechanisms and oscillator topology.Comment: 26 pages, 24 figure
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