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Suppose that the neuron is a system {A1, A2 ,.... An excitatory receptors} and {B1, B2 Bm} ,..... inhibitory receptors, this a total of m + n inputs. The input information is processed and the result is sent to the output Z and X, where Z is the output excitatory and inhibitory output X. A neuron actually has multiple outputs, which outputs would be obtained forking Z and X as many times as you want or need, we'll leave it to avoid complicating the notation. If we use the binary logic, these outputs are Z and X to 0 initially and not signaling. A 1 in Z means that sending a driving signal (signal E from now on) and a 1 in X means that you are sending an inhibitory signal (signal I from now on) when the neuron is firing both X and Z bring to 1. The neuron can not excite any, in this case simply the output X is found off to other neurons.
It was said that neurons need to receive more signals E I to fire, it is actually necessary integration since the set of stimuli it receives its influence is large and inaccurate. In this approach we assume that for the neuron to fire impulses are needed to receive more E K I.
- So what makes our model neuron is to sum all the pulses E and add all the drives I then compare the sizes of E and R & K. Being able to play 2 scenarios:
- E = I + K increased the neuron fires and {X, Z} = {1.1}
E minor R & K the neuron does not fire and {X, Z} = {0, 0}
The simplified diagram of the neuron result from follows:
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Transmission between neurons
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memory
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