β Scribed by Vipin Srivastava; S.F. Edwards
No coin nor oath required. For personal study only.
The brain's memory system can store without any apparent constraint, it recalls stored information e ciently and it is robust against lesion. Existing models of memory do not fully account for all these features. The model due to HopΓΏeld (Proc. Natl. Acad. Sci. USA 79 (1982) 2554) based on Hebbian learning (The Organization of Behaviour, Wiley, New York, 1949) shows an early saturation of memory with the retrieval from memory becoming slow and unreliable before collapsing at this limit. Our hypothesis (Physica A 276 (2000) 352) that the brain might store orthogonalized information improved the situation in many ways but was still constrained in that the information to be stored had to be linearly independent, i.e., signals that could be expressed as linear combinations of others had to be excluded. Here we present a model that attempts to address the problem quite comprehensively in the background of the above attributes of the brain. We demonstrate that if the brain devolves incoming signals in analogy with Fourier analysis, the noise created by interference of stored signals diminishes systematically (which yields prompt retrieval) and most importantly it can withstand partial damages to the brain.
π SIMILAR VOLUMES
## Abstract In previous work we developed and validated a mathematical model that predicted force output from skeletal muscles subjected to sixβpulse stimulation trains under isometric condition. The current study investigated the model's ability to predict force responses to longer stimulation tra