> have detailed how memory is encoded by synaptic weights
Synaptic weights is our computational model of neural networks that seems to work pretty well. But this is a model. No one has discovered the biological mechanism by which synapses are "weighted". The actual computation going on in a cell is totally based on chemistry.
There are a lot of different specialized networks in human and animal brains that don't necessarily translate easily to "weighted synaptic networks" all that well. For example, networks that process audio information have been shown to recognize individual frequencies (in fact, they pretty much solely do this!) Such networks seem to be primarily based on the firing rate across a synapse. For such networks is seems more reasonable to think of neurons as signal processors whose internal computation is more based on the time domain, e.g. some kind of Fourier transform-equivalent.
In short, I think you should put less weight (pun intended) in our computational model of neurons (link weights) and look more at biology.
Synaptic weights is our computational model of neural networks that seems to work pretty well. But this is a model. No one has discovered the biological mechanism by which synapses are "weighted". The actual computation going on in a cell is totally based on chemistry.
There are a lot of different specialized networks in human and animal brains that don't necessarily translate easily to "weighted synaptic networks" all that well. For example, networks that process audio information have been shown to recognize individual frequencies (in fact, they pretty much solely do this!) Such networks seem to be primarily based on the firing rate across a synapse. For such networks is seems more reasonable to think of neurons as signal processors whose internal computation is more based on the time domain, e.g. some kind of Fourier transform-equivalent.
In short, I think you should put less weight (pun intended) in our computational model of neurons (link weights) and look more at biology.