title: A Quantitative Neural Coding Model of Sensory Memory  
creator: Liu, PHD Peilei
creator: Wang, Professor Ting
subject: Cognitive Psychology
subject: Computational Neuroscience
subject: Dynamical Systems
subject: Machine Learning
subject: Neural Nets
subject: Statistical Models
subject: Neural Modelling
subject: Logic
subject: Philosophy of Mind
description: The coding mechanism of sensory memory on the neuron scale is one of the most  important questions in neuroscience. We have put forward a quantitative neural network model,  which is self-organized, self-similar, and self-adaptive, just like an ecosystem following  Darwin's theory. According to this model, neural coding is a “mult-to-one”mapping from  objects to neurons. And the whole cerebrum is a real-time statistical Turing Machine, with  powerful representing and learning ability. This model can reconcile some important disputations,  such as: temporal coding versus rate-based coding, grandmother cell versus population coding,  and decay theory versus interference theory. And it has also provided explanations for some key  questions such as memory consolidation, episodic memory, consciousness, and sentiment.  Philosophical significance is indicated at last.  
date: 2014-06-30
type: Preprint
type: NonPeerReviewed
format: application/pdf
identifier: http://cogprints.org/9753/1/liutextfigs.pdf
identifier:   Liu, PHD Peilei and Wang, Professor Ting  (2014) A Quantitative Neural Coding Model of Sensory Memory.  [Preprint]     
relation: http://cogprints.org/9753/