Models of Signal Generation in Neural Elements

  • Ronald J. MacGregor
  • Edwin R. Lewis


In this chapter, we shall discuss together three topics that are often treated separately, namely, chemical synapses, sensory receptors, and spontaneous activity in neurons. The common feature of these three topics is the generation of signals in the nervous system. Since the nervous system generally is viewed as a processor of signals, it seems natural to present models concerning the generation of those signals early in our discussion. It is quite conceivable that certain signals in the nervous system arise through the actions of many neurons or other cells in concert. On the other hand, it is clear that many signals do in fact arise within individual cells, and it is these signals that are discussed in the present chapter. Discussion of the generation of signals through the concerted actions of many cells is reserved for later sections, covering neural networks. Strictly speaking, we might limit our discussion here to models of sensory receptors and models of spontaneous signal generation, considering synaptic action to involve the transmission of preexisting signals rather than the generation of new signals. Indeed, we shall consider direct electrical communication between neurons in just this way, since it generally involves the more or less direct transfer of preexisting signals. However, synapses apparently involve mediation by a chemical transmitter and a marked change in the nature of the signal as it is transferred to the receiving cell. Moreover, the interaction between the chemical mediator and the receiving cell membrane apparently is very similar to (perhaps identical with) the interactions between chemical stimuli and chemoreceptor membranes (e.g., the action at olfactory or gustatory receptors).


Spike Train Reversal Potential Neural Modeling Neural Element Synaptic Response 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Plenum Press, New York 1977

Authors and Affiliations

  • Ronald J. MacGregor
    • 1
  • Edwin R. Lewis
    • 2
  1. 1.University of ColoradoBoulderUSA
  2. 2.University of CaliforniaBerkeleyUSA

Personalised recommendations