大脑信息处理和路由的确定性多值逻辑方案
Deterministic multivalued logic scheme for information processing and routing in the brain
摘要：
通过与量子信息学和基于噪声的逻辑的状态向量的类比，我们提出了一个用于处理和处理大脑中信息的神经电路的一般方案和元素。具体来说，我们考虑随机（例如，Poissonian）的有限持续时间尖峰序列，并使用兴奋性和抑制性突触的理想化概念，提供了一个从N个部分重叠的序列（“神经位”）中生成2^N-1个正交向量的过程。然后，我们表明，这些向量可以用来构建2^（2^N-1）-1个不同的叠加，表示相同数量的逻辑值时，携带或路由信息。在量子信息学中，上述数字是相同的，然而，本逻辑方案是更有利的，因为它在尖峰训练中矢量的存在由适当的符合电路检测的意义上是确定性的。由于这个原因，它不需要在标准互相关分析或量子计算中使用的那种时间平均或重复测量。
英文摘要：
Driven by analogies with state vectors of quantum informatics and noise-based logic, we propose a general scheme and elements of neural circuitry for processing and addressing information in the brain. Specifically, we consider random (e.g., Poissonian) trains of finite-duration spikes, and, using the idealized concepts of excitatory and inhibitory synapses, offer a procedure for generating 2^N-1 orthogonal vectors out of N partially overlapping trains ("neuro-bits"). We then show that these vectors can be used to construct 2^(2^N-1)-1 different superpositions which represent the same number of logic values when carrying or routing information. In quantum informatics the above numbers are the same, however, the present logic scheme is more advantageous because it is deterministic in the sense that the presence of a vector in the spike train is detected by an appropriate coincidence circuit. For this reason it does not require time averaging or repeated measurements of the kind used in standard cross-correlation analysis or in quantum computing.
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