Synapses themselves have interesting structure and contain little vesicles of transmitter chemical and when a spike arrives in the axon it causes these vesicles to migrate to the surface and be released into the synaptic cleft. There are several different kinds of transmitter chemicals. There are transmitter chemicals that implement positive weights and ones that implement negative weights. The transmitter molecules diffuse across the synaptic clef and bind to receptor molecules in the membrane of the post-synaptic neuron, and by binding to these big molecules in the membrane they change their shape, and that creates holes in the membrane. These holes are like specific ions to flow in or out of the post-synaptic neuron and that changes their state of depolarization. In biology, depolarization is a sudden change within a cell, during which the cell undergoes a dramatic electrical change. Synapses adapt, and that is what most of learning is, changing the effectiveness of a synapse. They can adapt by varying the number of vesicles that get released when a spike arrives or by varying the number of receptor molecules that are sensitive to the released transmitter molecules.
Synapses are very slow compared with computer memory nd have a lot of advantages over the random access memory on a computer because they are very small and low power.
The most important property of synapses is that they can adapt. They use locally available signals to change their strengths, and that is how we learn to perform complicated computations. Questions:
- How do they decide (make decisions) and how do synapses change their strength?
- What are the rules for how the synapses should adapt?