http://cogprints.org/9753/
A Quantitative Neural Coding Model of Sensory Memory
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.
Liu, PHD Peilei
Wang, Professor Ting
Cognitive Psychology
Computational Neuroscience
Dynamical Systems
Machine Learning
Neural Nets
Statistical Models
Neural Modelling
Logic
Philosophy of Mind
Peilei
Liu
Ting
Wang