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 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.Comment: 9 pages, 3 figure

A Quantitative Neural Coding Model of Sensory Memory\ud

The coding mechanism of sensory memory on the neuron scale is one of the most\ud important questions in neuroscience. We have put forward a quantitative neural network model,\ud which is self-organized, self-similar, and self-adaptive, just like an ecosystem following\ud Darwin's theory. According to this model, neural coding is a “mult-to-one”mapping from\ud objects to neurons. And the whole cerebrum is a real-time statistical Turing Machine, with\ud powerful representing and learning ability. This model can reconcile some important disputations,\ud such as: temporal coding versus rate-based coding, grandmother cell versus population coding,\ud and decay theory versus interference theory. And it has also provided explanations for some key\ud questions such as memory consolidation, episodic memory, consciousness, and sentiment.\ud Philosophical significance is indicated at last.\u

Neural Mechanism of Language

This paper is based on our previous work on neural coding. It is a self-organized model supported by existing evidences. Firstly, we briefly introduce this model in this paper, and then we explain the neural mechanism of language and reasoning with it. Moreover, we find that the position of an area determines its importance. Specifically, language relevant areas are in the capital position of the cortical kingdom. Therefore they are closely related with autonomous consciousness and working memories. In essence, language is a miniature of the real world. Briefly, this paper would like to bridge the gap between molecule mechanism of neurons and advanced functions such as language and reasoning.Comment: 6 pages, 3 figure

Motor Learning Mechanism on the Neuron Scale

Based on existing data, we wish to put forward a biological model of motor system on the neuron scale. Then we indicate its implications in statistics and learning. Specifically, neuron firing frequency and synaptic strength are probability estimates in essence. And the lateral inhibition also has statistical implications. From the standpoint of learning, dendritic competition through retrograde messengers is the foundation of conditional reflex and grandmother cell coding. And they are the kernel mechanisms of motor learning and sensory motor integration respectively. Finally, we compare motor system with sensory system. In short, we would like to bridge the gap between molecule evidences and computational models.Comment: 8 pages, 4 figure