In a classical neural network, where storage relies only on synapses, all memory is always present. Synaptic connections have specific weights, and any processing event uses them with all of the memory involved.
It could be of course that in a processing event only small regions of the brain network are being used, such that the rest of the connections form a hidden structure, a reservoir, a store or repository of unused information. Such models exist.
There is a real issue with stable pattern storage of a large number of patterns with a realistic amount of interference in a synaptic memory model. Classification, such as recognition of a word shape can be done very well, but storing 10,000 words and using them appropriately seems difficult. Yet that is still a small vocabulary for a human memory.
Another solution is conditional memory, i.e. storage that is only accessed when activated, which otherwise remains silent. Neurons offer many possibilities for storing memory other than at the strength of a synapse, and it should be worthwhile investigating if any of this may be exploited in a theoretical model.
To understand neural coding, we have to regard the relationship of synchronized membrane potentials (local field potentials) and the firing of the individual neuron. We have two different processes here, because the firing of the single neuron is not determined simply by the membrane potential exceeding a fixed threshold. Rather, the membrane potential’s fluctuation does not predict the individual neuron’s firing, because the neuron has a dynamic, flexible firing threshold that is determined by its own internal parameters. Also, the membrane potential is subject to synchronization by direct contact between membranes, it is not necessarily or primarily driven by synaptic input or neuronal spiking. Similarly, HahnG2014(Kumar) have noted that membrane synchronization cannot be explained from a spiking neural network.
The determination of an individual neuron’s firing threshold is a highly dynamic process, i.e. the neuron constantly changes its conditions for firing without necessarily disrupting its participation in ongoing membrane synchronization processes. In other words, membrane potential fluctuations are determined by synaptic input as well as local synchronization processes, and spikes depend on membrane potentials filtered by a dynamic, individually adjustable firing threshold.
The model for a neural coding device contains the following:
A neuronal membrane that is driven by synaptic input and synchronized by local interaction (both excitatory and inhibitory)
A spiking threshold with internal dynamics, possibly within an individual range, which determines spiking from membrane potential fluctuations.
In this model the neural code is determined by at least three factors: synaptic input, local synchronization, and firing threshold value. We may assume that local synchronization acts as a filter for signal loss, i.e. it unifies and diminishes differences in synaptic input. Firing thresholds act towards individualization, adding information from stored memory. The whole set-up acts to filter the synaptic input pattern towards a more predictable output pattern.