Models and Analogues in Biology

By Society for Experimental Biology | Go to book overview

MODELLING OF LARGE-SCALE NERVOUS ACTIVITY

BY D. M. MACKAY Wheatstone Physics Laboratory, King's College, London


INTRODUCTION

The past decade or two have seen a great variety of attempts to simulate the nervous system, usually by models drawn from the field of electronic circuits. At one extreme we have had the digital computor, discrete in its structure and discrete in its symbolic variables (which are often reduced to 'all' or 'none'). For the firing of a single peripheral nerve-cell, with its all-or-none character, the analogy was tempting; and there seems to be little doubt that 'coincidence detection' after the manner of digital circuitry does take place where several synapses converge on single neurons ( Lorente ed Nó, 1939). We owe to McCulloch & Pitts ( 1943, 1947) the demonstration that theoretical 'neurons' on this principle could be combined to imitate any specifiable type of information processing in living organisms; an 'existence theorem' which rapidly encouraged (or provoked) a mounting stream of variants and alternatives.

For afferent and efferent systems in particular, it has become clear that the thought-forms of 'analogue' rather than digital computing are more appropriate. Frequency-modulation, rather than binary coding, seems to be the rule. To use the interval between nerve-impulses as a continuous 'analogue' variable need in fact be no less efficient than to use their presence or absence as a digital variable ( MacKay & McCulloch, 1952, 1953); and 'analogue' servo-models have thrown much light, for example, on the mechanisms of control of skeletal muscle ( Merton, 1953, Eldredet al. 1953).

We thus arrive at a second, more general, class of model--the 'information-flow model'--in which discrete pathways are invested with signals that may be effectively continuous. As I have argued elsewhere ( MacKay 1951a, 1954, 1956a) these pathways should comprise not only conventional (presumably neural) signal-channels, but also channels (which might well be biochemical) through which the probabilities of neural function are modified). For the broad outlines of large-scale organization of the C.N.S. one may hope that this type of model will have permanent value, especially in clinical work (from which, incidentally, it may be expected to gain much of its structure) ( MacKay, 1954).

The object of the present brief paper, however, is to explore a further step. When enough cross-connexions are formed in a network of discrete pathways, the characteristic intuitions of the servo-engineer begin to fail him. The concepts of feedback, and even of input and output, become ambiguous and

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Models and Analogues in Biology
Table of contents

Table of contents

  • Title Page iii
  • Contents v
  • Preface vii
  • Quantum Physics and Biology† 1
  • Models in Genetics 6
  • Kinetic Models of Development And Heredity 13
  • Tissues in Culture and in the Body 28
  • References 40
  • Models of Muscle 41
  • References 66
  • Mechanical Models in Zoology 69
  • Conclusions 82
  • Physical Models in Biology 83
  • Estimation of Values Of Parameters of a Model to Conform With Observations 102
  • Summary 120
  • Applications of Theoretical Models to the Study of Flight- Behaviour in Locusts and Birds 122
  • References 138
  • Electrical Analogues in Biology 140
  • Computers and the Nervous System 152
  • References 168
  • Models in Cybernetics 169
  • References 190
  • Modelling of Large-Scale Nervous Activity 192
  • Conclusions 197
  • Energy Models of Motivation 199
  • Summary 212
  • The Use of Models in the Teaching Of Embryology 214
  • School Biology as An Educational Model 230
  • Conclusion 241
  • The Problem of Communication In Biological Teaching 243
  • Acknowledge Ments 248
  • A Review of the Symposium: Models and Analogues in Biology 250
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