36 Information Storage and Neural Control 



some similarities are obvious, as are some points of dissimilarity. 

 A legitimate question yet remains: How does the existence of a 

 potentially remarkable device of this sort aid us in our present 

 work? There are at least two answers to this question. 



The first answer is exemplified by the work of those who have 

 studied the brain as a computing machine. Turing (1) proved 

 that a very simple device is capable of computing any number 

 which a reasonable man might wish to call computable. In a 

 classic paper, McCulloch and Pitts (2) argued that, since mathe- 

 matical logic has been stated in a form where deductions become 

 a form of computation, a device of no greater complexity than a 

 Turing machine should be capable of performing any logical 

 computation, no matter how complex. They, in fact, proved that 

 elements no more complex than neurons were sufficient for this 

 purpose. That is, they demonstrated that to every logical proposi- 

 tion there corresponds a nerve net which can be constructed from 

 idealized neurons, and that the converse is also true. The brain, 

 thus, is not just in some vague sense like a computing machine; 

 the brain is a computing machine. The important activity of the 

 brain is its inputting, processing, and outputting of information. 

 Although we have had these computing machines — brains — around 

 for a long time, only recently have we had any others of comparable 

 complexity. To biology, the presence of the digital computer has 

 provided, in addition to a new source of interested human talent, 

 a manipulatable device which can be studied in vivo and whose 

 descriptors, as they are discovered, might profitably be applied 

 to the human machine. Studies of electronic systems, and of sys- 

 tems in general, have provided insights into some important 

 biological questions. To mention just one such question which 

 has received a lot of attention: How is it possible to construct a 

 reliable system out of billions of variable, unreliable parts? This 

 question has been attacked profitably by McCulloch (3, 4) and 

 von Neumann (5), among others. 



The second answer is the one on which I wish to dwell more 

 extensively. It is frequently the case that, although we know the 

 properties of all components of a system, we are unable to predict 

 the behavior of the system if it is composed of many components. 

 It is true that not all of the relevant properties of neurons are 



