1: One - to - one. Activity in the incoming neuron will cause output. If the input is inhibitory, the effect could be to inhibit any spontaneous output from the neuron.
The following examples (2 - 12) could all be tried with different values of inputs & threshold.
2: Spatial summation (OR). Input from either A or B would match the threshold and cause output; this would have the same effect as input from both A and B at the same time.
3: Spatial summation (AND). Now that the threshold is 2, it needs input from both A and B to cause output.
4: Spatial summation (A but not B). Input from A alone will cause output, but if there is input from B at the same time then summation with its inhibitory input will prevent output.
5: Early inhibition. A could provide excitatory input, resulting in output, unless it has been previously inhibited by activity in B.
6: Pre-synaptic inhibition. Similar to 5, except that the inhibitory effect of B is just before the axonal ending of A (rather than via dendrites in 5).
7: Recurrent collateral. Similar to no. 5, except that the inhibitory input is provided as a result of excitatory input from A causing the neuron to fire (and then inhibit A, which stops exciting the central neuron, so the inhibition stops, so A fires and the inhibition occurs again, and so on…)
8: Recurrent collateral. Similar to 7, but with the inhibition occurring as in 6.
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9: Delayed inhibition. Like 5, but delays are introduced by the additional synapses round the circuit.
10: Reverberating circuit (excitatory). You can follow the excitatory signals around; the period of reverberation depends on how many neurons are involved in the circuit (and the delays involved).
11: Reverberating circuit (inhibitory). Similar to 10, but based on inhibition - work it out for yourself!
12: Temporal summation. What happens if A and B fire at the same time? What if B's input just follows A's? What if B's input is a lot later than A's?
13: Learning circuit; based on the reverberating loops and summations in the previous boxes. Neurons P and N represent positive and negative reinforcers for opposing outputs from neurons F (O1) and G (O2).
Lateral inhibition is an important aspect of patterned systems, especially sensory processing. The net effect is generally to enhance the contrast between the information flowing in the adjacent channels. This is often important in sensory systems, such as the visual pathway.
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14: Lateral inhibition (forwards).
15: Lateral inhibition (backwards).
16: Lateral inhibition (array).