The Biophysical Basis of Firing Variability in Cortical Neurons - Computational Neuroscience: A Comprehensive Approach - page 176

old, because strong subthreshold oscillations could keep the rhythm intact until the
stimulus moves above threshold again. For a class 2 neuron, there is more of a ten-
dency for spikes to drop out without a wide dispersion of spike times: this is because
the late stage is more restricted and has a harder onset than for class 1 neurons. This
is illustrated in Figure 6.11 , where we show that changing the class of the Morris-
Lecar model from 1 to 2 can greatly reduce the dispersion of the final spikes - class
2 neurons intrinsically prefer to stay coherent or be silent, while class 1 neurons have
a smooth transition between the two extremes.

6.11 Conclusions

It is clear that, to understand firing variability, it is very important to consider both the
dynamics of spike generation, and the nature and parameters of the noise sources in
cortical neurons. Using more sophisticated and more quantitatively exact dynamical
models, for example taking account of higher-order patterns spiking such as bursting
and slower processes such as dendritic calcium spikes, will uncover a much greater
range of excitable behaviour shaped by noise in the cortex. Which phenomena are
functionally important will be made clear as we find out more about the cortical
network firing patterns of awake, behaving animals. An important principle which
should also be considered is the energetic cost of precise firing, and how this has
been adapted to [4]. There is still a great deal of theoretical and experimental work
to be done in order to arrive at a satisfactory understanding of firing variability in the
cortex.

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