The main purpose of thinking is to forecast phenomena that take place in the environment. To this end, humans and animals must refer to a complicated knowledge base which is somewhat vaguely called memory. One has to realize the two main problem areas in a discussion of memory: (1) The internal representations of sensory information in the brain networks. (2) The memory mechanism itself. Most of the experimental and theoretical work has concentrated on the latter. Although it has been extremely difficult to detect memory traces experimentally, the storage mechanism is theoretically still the easier part of the problem. Contrary to this, it has been almost a mystery how a physical system can automatically extract various kinds of abstractions from the vast number of vague sensory signals. This article contains some views and results about the formation of such internal representations in idealized neural networks, and their memorization. It seems that both of the above functions, viz. formation of the internal representations and their storage, can be implemented simultaneously by an adaptive, self-organizing neural structure which consists of a large number of neural units arranged into a two-dimensional network. All units of this network receive sensory signals through many input channels and they are further provided with abundant mutual feedback in the lateral direction through adaptive neural fibers. If the spatial distribution of the feedback connections is distance dependent in a particular way, and if the strengths of the connectivities change in proportion to the signals according to certain simple nonlinear laws, different neurons will be sensitized to different features of the input signals in an orderly fashion, making up various feature maps of the types found in the brain. On the other hand, activity patterns over such feature maps will be memorized by the same network in spatially distributed form and recalled associatively, in relation to an incomplete cue or key pattern.
[1]
C. Gilbert.
Horizontal integration in the neocortex
,
1985,
Trends in Neurosciences.
[2]
O. Creutzfeldt,et al.
An intracellular analysis of visual cortical neurones to moving stimuli: Responses in a co-operative neuronal network
,
2004,
Experimental Brain Research.
[3]
T. Kohonen,et al.
A principle of neural associative memory
,
1977,
Neuroscience.
[4]
Roman Bek,et al.
Discourse on one way in which a quantum-mechanics language on the classical logical base can be built up
,
1978,
Kybernetika.
[5]
Shun-ichi Amari,et al.
Competitive and Cooperative Aspects in Dynamics of Neural Excitation and Self-Organization
,
1982
.
[6]
M. Huttunen,et al.
General Model for the Molecular Events in Synapses During Learning
,
2015,
Perspectives in biology and medicine.
[7]
C. Malsburg.
Self-organization of orientation sensitive cells in the striate cortex
,
2004,
Kybernetik.
[8]
W. Singer,et al.
The effects of early visual experience on the cat's visual cortex and their possible explanation by Hebb synapses.
,
1981,
The Journal of physiology.
[9]
G. Stent.
A physiological mechanism for Hebb's postulate of learning.
,
1973,
Proceedings of the National Academy of Sciences of the United States of America.
[10]
Teuvo Kohonen,et al.
Correlation Matrix Memories
,
1972,
IEEE Transactions on Computers.