In this paper a model for auditory perception is introduced. This model is based on a network of integrate-and-fire and resonate-and-fire neurons and is aimed to control the phonotaxis
behavior of a roving robot. The starting point is the model of
phonotaxis in Gryllus Bimaculatus: the model consists of four integrate-and-fire neurons and is able of discriminating the calling song of male cricket and orienting the robot towards the sound source. This paper aims to extend the model to include an
amplitude-frequency clustering. The proposed spiking network shows
different behaviors associated with different characteristics of
the input signals (amplitude and frequency). The behavior implemented on the robot is similar to the cricket behavior, where some frequencies are associated with the calling song of male crickets, while other ones indicate the presence of predators.
Therefore, the whole model for auditory perception is devoted to
control different responses (attractive or repulsive) depending on
the input characteristics. The performance of the control system
has been evaluated with several experiments carried out on a
In this paper a biologically-inspired network of spiking neurons is used for robot navigation control. The implemented scheme is able to process information coming from the robot contact sensors in order to avoid obstacles and on the basis of these actions to learn how to respond to stimuli coming from range finder sensors.
The implemented network is therefore able of reinforcement learning through a mechanism based on operant conditioning. This learning takes place according to a plasticity law in the synapses, based on spike timing. Simulation results discussed in the paper show the suitability of the approach and interesting adaptive properties of the network.
The study of spatio-temporal patterns generation and processing in systems with high parallelism like biological neuronal networks gives birth to a new technology able to realize architectures with robust performance even in noisy environments. The behavioural properties of neural assemblies warrant an effective exchange and use of information in presence of high-level neuronal noise.
Neuron population processing and self-organization have been reproduced by connecting several neuron through synaptic connections, which can be either electrical or chemical, in artificial information processing architectures based on Field Programmable Gate Arrays (FPGA). The adopted neuron model is based on Izhikevich’s description of cortical neuron dynamics .
The development of biological neuronal network models has been focused on architecture features like changes over time of topologies, uniformity of the connections, node diversity, etc. The hardware reproduction of neuron dynamical behaviour, by giving high computation performance, allows the development of innovative computational methods and models based on self-organizing nonlinear architectures.
The concept of stochastic resonance introduced the idea that the presence of noise in nonlinear systems may have benefic effects. In this paper different regular topologies of populations of FitzHugh-Nagumo neurons have been investigated with respect to the presence of noise in the network. Each neuron is subjected to an independent source of noise. In these conditions the behavior of the population depend on the connection among the elements. In population of uncoupled neurons the so-called stochastic resonance without tuning was observed. Moreover, we show that globally coupled neurons have increasing response-to-stimulus coherence for increasing values of the coupling strength. In locally coupled neurons the performance depend on the neighborhood radius and in general are higher than in the case of uncoupled neurons.
Nanotechnology finds in flagellar bacteria an uncomparable example of a very efficient and miniaturized motor. This and the complex behavior of the bacteria colonies growth in a self-organized way make the study of flagellar bacteria very important and appealing for possible applications. For these reasons in this work single bacterium
motion and colonies growth have been studied by applying nonlinear methods. The characterization of the single bacterium motion leads to the conclusion that determinism (due to chemotaxis) is predominant with respect to random terms. This result is confirmed by the possibility of modelling the case study of colonies growth through
an activation/inhibition dynamics.