Real-time computing without stable states:: A new framework for neural computation based on perturbations
Real-time computing without stable states:: A new framework for neural computation based on perturbations
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DOI：
10.1162/089976602760407955
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发表时间：
2002-11-01
影响因子：
2.9
通讯作者：
Markram, H
中科院分区：
文献类型：
--
作者：
Maass, W;Natschläger, T;Markram, H
神经建模的一个关键挑战是解释如何在真实的时间内通过集成和激发神经元的常规递归电路处理来自快速变化环境的连续多模态输入流。我们提出了一个新的计算模型，实时计算时变输入，提供了一种替代的范例的基础上图灵机或吸引子神经网络。它不需要依赖于任务的神经回路结构。相反，它是基于高维动力系统的原理与统计学习理论相结合，可以在通用的进化或发现的循环电路上实现。结果表明，由一个足够大的和异构的神经电路形成的高维动力系统的固有瞬态动力学可以作为通用的模拟衰落记忆。读出神经元可以学习以真实的时间从这种循环神经电路的当前状态中提取关于不同任务可能需要的当前和过去输入的信息。稳定的内部状态不需要给出稳定的输出，因为瞬态内部状态可以由读出神经元转换为稳定的目标输出，这是由于动力系统的高维性。我们的方法是基于一个严格的计算模型，液体状态机，不像图灵机，不需要定义良好的离散内部状态之间的顺序转换。与图灵机一样，它得到了严格的数学结果的支持，这些结果预测了理想条件下的通用计算能力，但对于实时处理时变输入的生物学更现实的情况。我们的方法为神经编码的解释，神经生理学中的实验设计和数据分析，以及机器人和神经技术中问题的解决提供了新的视角。
A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.