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7. Signal Transmission and Neuronal Coding
In the preceding chapters, a theoretical description of neurons and neuronal
populations has been developed. We are now ready to apply the theoretical
framework to one of the fundamental problems of Neuroscience - the problem of
neuronal coding and signal transmission. We will address the problem as three
different questions, viz.,
- (i)
- How does a population of neurons react to a fast change in the
input? This question, which is particularly interesting in the context of
reaction time experiments, is the topic of Section 7.2.
- (ii)
- What is the response of an asynchronously firing population to an
arbitrary time-dependent input current? This question points to the signal
transfer properties as a function of stimulation frequency and noise. In
Section 7.3 we calculate the signal transfer
function for a large population as well as the signal-to-noise ratio in a
finite population of, say, a few hundred neurons.
- (iii)
- What is the `meaning' of a single spike? If a neuronal
population receives one extra input spike, how does this affect the population
activity? On the other hand, if a neuron emits a spike, what do we learn
about the input? These questions, which are intimately related to the problem
of neural coding, are discussed in Section 7.4.
The population integral equation of Chapter 6.3 allows
us to discuss these questions from a unified point of view. We focus in this
chapter on a system of identical and independent neurons, i.e., a homogeneous
network without lateral coupling. In this case, the behavior of the
population as a whole is identical to the averaged behavior of a single
neuron. Thus the signal transfer function discussed in
Section 7.3 or the coding characteristics discussed
in Section 7.4 can also be interpreted as single-neuron
properties. Before we dive into the main arguments we derive in
Section 7.1 the linearized population equation that
will be used throughout this chapter.
Subsections
Next: 7.1 Linearized Population Equation
Up: II. Population Models
Previous: 6.7 Summary
Gerstner and Kistler
Spiking Neuron Models. Single Neurons, Populations, Plasticity
Cambridge University Press, 2002
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