How can we model neural plasticity?

A conjecture based on findings of plasticity in ion channel expression is that the level of expression of various ion channels reflects a memory of the cell. This means that we will have networks of neurons with slightly varying dendritic ion channel populations, which influence not only their general intrinsic excitability, but very specifically influence synaptic transmission through their position at synapses. Some channels aid with transmission, others block or reduce transmission, a phenomenon which has been studied as short-term facilitation or depression. Furthermore, ion channels at the synapse have an influence on synaptic plasticity, again, supporting or blocking plastic changes at the synapse.

This makes a model of neural plasticity more complex than synaptic input-dependent LTP/LTD. A single neuron would need variables at synaptic positions for AMPA and NMDA but also for the main potassium and calcium channels (K-A, Kir, Sk, HCN, L-Ca). In addition, a single set of intrinsic variables could capture the density of ion channels in dendritic shaft position. These variables would allow to express the neural diversity which is a result of memorization or learning.

Hypothesis: Neural plasticity is not primarily input-dependent, instead it is guided by a neural internal state which reflects network processes of knowledge building.

How would the variables that define a neural network be learned? The intrinsic variables would be set by neuromodulatory activation and the internal state of the neuron. The synaptic variables would be set from synaptic activation, from neuromodulatory activation, and also from an internal state‘. (In addition, the significant spill-over from synaptic activation which reaches other synapses via the dendrite should also be modeled.)

Let us assume that synaptic stimulation and neuromodulatory activation are well understood. What is the internal state‘ of a neuron?

It has been shown that epigenetic modifications are an important factor in memory. These involve methylation changes in DNA and alterations in histones. Their activation is mediated by protein signaling pathways, encompassing kinases like PKA, PKC, CaMKII, MAPK, and other important protein signaling hubs. Long-term neural plasticity and behavioral memory only happen, when the internal conditions are favorable, many reductions or disruptions of internal processes prevent plasticity and behavioral memory. We do not have the data yet to model these processes in detail. But we may use variables for a neuron‘s internal state which is facilitating or inhibiting plasticity. It is an important theoretical question then to understand the impact of internally-guided plasticity on a neural network, one hypothesis is that it helps with conceptual abstraction and knowledge building.

Hypothesis: Only a few neurons in a target area undergo the permanent, lasting changes underlying long-term memory.

Learning events in rodents lead to epigenetic changes in a targeted area, such as amygdala or hippocampus, but it seems as if there are only a few cells, neurons and non-neurons, involved at a time.These changes begin to appear 30 min after a high-frequency stimulation as in dentate gyrus and last 2-5 hours at least. Some have measured the effects 2 weeks after a learning event. Changes are not widespread, as would be expected in distributed memory, instead they are focal, as if only a few cells suffice to store the memory. It is also possible that the changes are strong only in a focal group of neurons, and present, but much weaker in a more distributed group. Focal learning may be seen as a strategy to build more effective knowledge representations, similar to dropout learning techniques which improve feature representation.

What is the basis for neural plasticity?

The current view of LTP/LTD and specifically AMPA-dependent plasticity, which underlies all theoretical work on neural networks, seems exceedingly narrow. It leaves out levels of plasticity that are well known, like epigenetic modifications, or internal protein signaling, in order to come up with a simple model of use-dependent plasticity, the Hebbian principle, or ‘neurons that fire together, wire together’.

It is interesting and important to tackle the complex task of reducing the large number of individual findings on behavioral memory and neural plasticity into a small set of principles that can be used for models of biologically realistic memorization. Such models should offer capabilities beyond machine learning, namely conceptual abstraction, information filtering, building knowledge.

Here is one such observation: Both AMPA receptor placement and dendritic ion channel expression are regulated by similar, overlapping internal protein pathways. These protein pathways are activated by NM receptors and by NMDA and L-type-calcium channel-based calcium influx. We may conjecture that NM receptors and NMDA-based calcium activation together orchestrate neural plasticity via e.g. the calcium/CaMKII route, and the cAMP/PKA/ERK route, and that these pathways are acting in synergy at the synaptic AMPA sites as well as on the dendritic/synaptic ion channel expression sites.

So what this means is that various forms of intrinsic and synaptic plasticity are guided by the same protein pathways and therefore can be expected to be activated together. Here are specific instances of this synergy:

For instance, strengthening of AMPA could be accompanied by insertion of Sk-channels and reduction of L-type calcium channels, which blocks the synapse from further strengthening (‚overwrite protection‘). Such a mechanism has recently been identified as necessary for the stability and the lasting memorization capabilities of a network. Activation of the cAMP pathway activates Ih (HCN channels) which decreases the intrinsic excitability of the neuron, and allows less synaptic input to be processed. In a way, this kind of activation could be used as a temporal lock to prevent high dendritic excitability after the NM system has become engaged and plasticity in the neuron has started. Vice versa, in the absence of cAMP, dendritic excitability is high and many synaptic inputs are processed by an increase of membrane resistance through a reduction of HCN. Reduction of synaptic activity also reduces dendritic HCN channels. HCN channels may therefore indicate the level of synaptic activation, where more channels limit the parallel processing of synaptic input.