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Foraging behavior in a 3-D virtual sea snail having a spiking neural network brain

机译:具有尖刺神经网络大脑的3-D虚拟海蜗牛中的觅食行为

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This paper reports on a simulation study of foraging behavior in a 3-D virtual sea snail. The responsible circuit is composed of 8 spiking neurons which is part of a larger 37 neuron brain. The 3-D virtual environment has full soft body physics enabled and is completely defined in software. When no odor targets are available this brain implements a semi-random path foraging behavior and when targets are available this brain switches to a directed approach behavior. The core spiking neuron simulation equation is the Erlang function which is simulated as a cascade of leaky exponential functions. The use of this equation is justified by the new Soft State Automata Theory which describes causation in non-clocked mathematically discontinuous systems like the brain in which finite states cannot be defined by the system itself. The use of the Erlang function to propagate both the normal signal and the threshold response signal results in 9 neural control parameters, 7 of which may be changed adaptively.
机译:本文报道了在3D虚拟海蜗牛中觅食行为的仿真研究。负责的电路由8个尖刺神经元组成,它们是较大的37个神经元大脑的一部分。 3-D虚拟环境启用了完整的软体物理,并在软件中完全定义。当没有气味目标可用时,此大脑将执行半随机的觅食行为,而当目标可用时,此大脑将切换为定向接近行为。峰值神经元仿真方程的核心是Erlang函数,该函数被模拟为级联的泄漏指数函数。新的软状态自动机理论证明了该方程式的合理性,该理论描述了非计时的数学不连续系统(例如大脑)中的因果关系,在该系统中,系统本身无法定义有限状态。使用Erlang函数传播正常信号和阈值响应信号均会导致9个神经控制参数,其中7个可以自适应更改。

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