Temporal pattern discrimination of impulse sequences in the computer-simulated nerve cells

This paper deals with some properties of temporal pattern discrimination performed by single digital-computer simulated synaptic cells. To clarify these properties, the Shannon's entropy method which is a basic notion in the information theory and a fundamental approach for the design of pattern classification system was applied to input-output relations of the digital computer simulated synaptic cells. We used the average mutual information per symbol as a measure for the temporal pattern sensitivity of the nerve cells, and the average response entropy per symbol as a measure for the frequency transfer characteristics. To use these measures, the probability of a post-synaptic spike as a function of the recent history of pre-synaptic intervals was examined in detail. As the results of such application, it was found that the EPSP size is closely related to the pattern of impulse sequences of the input, and the average mutual information per symbol for EPSP size is given by a bimodal curve with two maximum values. One is a small EPSP size and the other is a large EPSP size. In two maximum points, the structure of the temporal pattern discrimination reverses each other. In addition, the relation between the mean frequency, or the form of impulse sequences of the input, and the average mutual information per symbol has been examined. The EPSP size at one maximum point of average mutual information is in inverse proportion to the magnitude of input mean frequency which relates to the convergence number of input terminal, while that at the other maximum point is proportional to that of the mean frequency. Moreover, the temporal pattern discrimination is affected remarkably by whether successive interspike intervals of the input are independent or not in the statistical sense. Computer experiments were performed by the semi-Markov processes with three typical types of transition matrixes and these shuffling processes. The average mutual informations in the cases of these semi-Markov processes are in contrast to those of the shuffling processes which provide a control case. The temporal structure of successive interspike intervals of the input is thus a significant factor in pattern discrimination at synaptic level.

[1]  R. Adams.  I. The Synthesis of D- and L-Threo- and D- and L-Erythro-α-Amino-β-hydroxy-n-caproic Acids. II. Experiments on the Preparation of α-Aminoalkanesulfonamides. III. The Influence of Nerve Impulse Sequence on the Contractions of Different Crustacean Muscles , 1950 .

[2]  George L. Gerstein,et al.  Mathematical models for the all-or-none activity of some neurons , 1962, IRE Trans. Inf. Theory.

[3]  G. P. Moore,et al.  SENSITIVITY OF NEURONES IN APLYSIA TO TEMPORAL PATTERN OF ARRIVING IMPULSES. , 1963, The Journal of experimental biology.

[4]  D. M. Wilson,et al.  RELATIVE REFRACTORINESS AND PATTERNED DISCHARGE OF LOCUST FLIGHT MOTOR NEURONS. , 1964, The Journal of experimental biology.

[5]  V. Mountcastle,et al.  NEURAL ACTIVITY IN MECHANORECEPTIVE CUTANEOUS AFFERENTS: STIMULUS-RESPONSE RELATIONS, WEBER FUNCTIONS, AND INFORMATION TRANSMISSION. , 1965, Journal of neurophysiology.

[6]  D. R. Smith,et al.  A STATISTICAL ANALYSIS OF THE CONTINUAL ACTIVITY OF SINGLE CORTICAL NEURONES IN THE CAT UNANAESTHETIZED ISOLATED FOREBRAIN. , 1965, Biophysical journal.

[7]  Thomas Herbert Naylor.  Computer Simulation Techniques , 1966 .

[8]  M ten Hoopen.  Multimodal interval distributions. , 1966, Kybernetik.

[9]  R. Stein,et al.  The information capacity of nerve cells using a frequency code. , 1967, Biophysical journal.

[10]  R. E. Olsen,et al.  Dynamic statistics of crayfish caudal photoreceptors. , 1967, Biophysical journal.

[11]  G. P. Moore,et al.  Neuronal spike trains and stochastic point processes. I. The single spike train. , 1967, Biophysical journal.

[12]  小池 将貴,et al.  Computer Simulation Techniques：Thomas H. Naylor, Joseph L. Balintfy, Donald S. Burdick and Kong Chu 著，John Wiley & Sons, Inc., New York , 1968 .

[13]  M Tsukada,et al.  [Statistical analysis and functional interpretation of input-output relation in computer-simulated nerve cells]. , 1972, Iyo denshi to seitai kogaku. Japanese journal of medical electronics and biological engineering.

[14]  G. P. Moore,et al.  Input-output relations in computer-simulated nerve cells , 1968, Kybernetik.

[15]  José Pedro Segundo,et al.  Spike probability in neurones: influence of temporal structure in the train of synaptic events , 1966, Kybernetik.