A Quantitative Neural Coding Model of Sensory Memory

TL;DR

This paper introduces a comprehensive neural network model of sensory memory that explains various coding mechanisms and cognitive phenomena, emphasizing its adaptive, self-organizing properties and philosophical implications.

Contribution

It presents a novel quantitative neural coding model that unifies multiple coding theories and explains key aspects of sensory memory and consciousness.

Findings

Reconciles temporal and rate coding debates

Explains memory consolidation and episodic memory

Models the cerebrum as a real-time statistical Turing Machine

Abstract

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 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.