Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
A typical human brain consists of roughly 100 billion
neurons, and one key aim of Biological Cybernetics is to simulate
neural systems. A good model of a neuron accurately represents the
behaviour of biological neurons, typically the spiking behaviour. For
cybernetic systems that aim to function in real time with thousands,
millions, or even billions of simulated neurons, it is also important
that the model is computationally efficient. One Fatiguing Leaky Integrate
and Fire neuron is a model that has four free parameters per
neuron. This model has been used in cybernetic agents, but there have
been few links to actual biological behaviour. A model of a rat neocortical
neuron is developed with four specific parameter settings.
This model is tuned to a particular input regime. When compared to
a biological neuron it gets 90% of spikes roughly correct. Further
modifications of the fatigue model enables the FLIF neuron to account
for spontaneous neural firing, a known neural property, that is
not present in the data. These modifications provide other FLIF models
with a similar fit to the biological data. The best of these models
correctly predicts over 94% of the spikes.
