Epigenetic modification is a powerful mechanism for the induction, the expression and persistence of long-term memory.
For long-term memory, we need to consider diverse cellular processes. These occur in neurons from different brain regions (in particular hippocampus, cortex, amygdala) during memory consolidation and recall. For instance, long-term changes in kinase expression in the proteome, changes in receptor subunit composition and localization at synaptic/dendritic membranes, epigenetic modifications of chromatin such as DNA methylation and histone methylation in the nucleus, and the posttranslational modifications of histones, including phosphorylation and acetylation, all these play a role. Histone acetylation is of particular interest because a number of known medications exist, which function as histone deacetylase inhibitors (HDACs), i.e. have a potential to increase DNA transcription and memory (more on this in a later post).
Epigenetic changes are important because they define the internal conditions for plasticity for the individual neuron. They underlie for instance, kinase or phosphatase-mediated (de)activations of enzymatic proteins and therefore influence the probability of membrane proteins to become altered by synaptic activation.
Among epigenetic changes, DNA methylation typically acts to alter, often to repress, DNA transcription at cytosine, or CpG islands in vertebrates. DNA methylation is mediated by enzymes such as Tet3, which catalyses an important step in the demethylation of DNA. In dentate gyrus of live rats, it was shown that the expression of Tet3 is greatly increased by LTP – synaptically induced memorization – , suggesting that certain DNA stretches were demethylated , and presumably activated. During induction of LTP by high frequency electrical stimulation, DNA methylation is changed specifically for certain genes known for their role in neural plasticity . The expression of neural plasticity genes is widely correlated with the methylation status of the corresponding DNA .
So there is interesting evidence for filtering the induction of plasticity via the epigenetic landscape and modifiers of gene expression, such as HDACs. Substances which act as histone deacetylase inhibitors (HDACs) increase histone acetylation. An interesting result from research on fear suggests that HDACs increase some DNA transcription, and enhance specifically fear extinction memories , ,.
Current synaptic plasticity models have one decisive property which may not be biologically adequate, and which has important repercussions on the type of memory and learning algorithms in general that can be implemented: Each processing or transmission event is an adaptive learning event.
In contrast, in biology, there are many pathways that may act as filters from the use of a synapse to the adaptation of its strength. In LTP/LTD, typically 20 minutes are necessary to observe the effects. This requires the activation of intracellular pathways, often co-occurence of a GPCR activation, and even nuclear read-out.
Therefore we have suggested a different model, greatly simplified at first to test its algorithmic properties. We include intrinsic excitability in learning (LTP-IE, LTD-IE). The main innovation is that we separate learning or adaptation from processing or transmission. Transmission events leave traces at synapses and neurons that disappear over time (short-term plasticity), unless they add up over time to unusually high (low) neural activations, something that can be determined by threshold parameters. Only if a neuron engages in a high (low) activation-plasticity event we get long-term plasticity at both neurons and synapses, in a localized way. Such a model is in principle capable of operating in a sandpile fashion. We do not know yet what properties the model may exhibit. Certain hypotheses exist, concerning abstraction and compression of a sequence of related inputs, and the development of an individual knowledge.
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.