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Emergence of perceptual states in nonlinear lattices: A new computational model for perception

机译:非线性晶格中感知状态的出现:一种新的感知计算模型

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Insects show the ability to react to certain stimuli with simple reflexes using direct sensory-motor pathways, which can be considered as basic behaviors, while high brain regions provide secondary pathway allowing the emergence of a cognitive behavior which modulates the basic abilities. Taking inspiration from this evidence, a new general purpose perceptual control architecture is briefly presented and experimentally applied to a rover navigating in a cluttered environment. The core of the architecture is constituted by the representation layer, where different stimuli, triggering competitive reflexes, are fused to form a unique abstract picture of the environment. Each representation induces a learnable modulation of the basic behaviors in order to determine the robot overall behavior. The representation is formalized by means of reaction-diffusion nonlinear partial differential equations, under the paradigm of the cellular neural networks (CNNs), whose dynamics converges to steady-state Turing patterns. A suitable unsupervised learning leads to the shaping of the basins of attraction of the Turing patterns that, at the end of the leaning stage, represent a particular behavior modulation. Both simulations and robot experiments are drawn to demonstrate the potentiality and the effectiveness of the approach.
机译:昆虫表现出使用直接的感觉运动路径对简单刺激做出反应的能力,可以将其视为基本行为,而高大脑区域则提供了辅助路径,从而允许认知行为的出现调节了基本能力。从这一证据中获得启发,简要介绍了一种新的通用感知控制架构,并将其实验性地应用于在混乱环境中行驶的流动站。该体系结构的核心由表示层构成,在该层中,触发竞争反射的不同刺激融合在一起,形成了独特的环境抽象图景。每个表示都诱导对基本行为的可学习的调整,以便确定机器人的总体行为。该表示形式是在细胞神经网络(CNN)的范式下,通过反应扩散非线性偏微分方程形式化的,其动力学收敛到稳态图灵模式。适当的无监督学习会导致图灵模式吸引盆的形成,在倾斜阶段结束时,这表示特定的行为调制。进行了仿真和机器人实验,以证明该方法的潜力和有效性。

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