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Wyniki 1-1 spośród 1 dla zapytania: authorDesc:"Igor Karoń"

» FPGA implementation of the Predator-Prey algorithm with adrenalin boost based on a Spiking Neural Network

Igor Karoń

Karol Gugała

Janusz Pochmara

Andrzej Rybarczyk

Spiking neural networks use the element of time in communicating by sending out individual pulses [1]. Spiking neurons can therefore multiplex information into a single stream of signals, like the frequency and amplitude of sound in the auditory system [2, 3]. There are currently a lot of papers about using Spiking Neural Network in robotic [4] but most of them focuses on using neural networks to identify and avoid terrain obstacles or finding the shortest way to signal source: [5-8]. Due to natural network properties, it is an interesting problem to implement some of the biological patterns occurring both in the world of animals and humans. One of these behavioral patterns is a natural instinct to escape threats that have natural origins (e.g. forest fires, floods) and from the world of animals (e.g. escape from predators). In nature, one can see how animals have developed some behaviors that help them survive. The ones with better strategies live through, and pass on their techniques to children [9]. Accurate understanding and mapping of similar behavior allow both better understanding of brain functions and enable use of these schemes as a base for future research projects. Presented paper focus on prey behavior controlled by Spiking Neural Network and modified version or Predator-Prey algorithm. In order to increase the efficiency of the algorithm some behaviors based on the adrenaline have been implemented. As admission to our future work hardware implementation of Spiking Neural Network in FPGA was presented. Spiking neuron models Neurons are elementary information processing units in brain. Structure of biological neuron and synapses has already been well described in many other publications [5, 10, 11]. The following work uses the Leaky Integrate-and-Fire model. LIF is counted as a simplified version of Hodgkin-Huxley model. This model was chosen because: of both low computational complexity, and ability of direct pr[...] więcej»
w zeszycie ELEKTRONIKA - KONSTRUKCJE, TECHNOLOGIE, ZASTOSOWANIA 2011/12

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