Internal Memory: An Example

A protein Er81 which is present in about 60% of parvalbumin interneurons (parvalbumin is a calcium buffer which is fast, in contrast to calbindin) in layer II/III in the cortex of mice has been found to have an effect on the latency of spiking in these interneurons. (Er81 is also found in layer V of pyramidal cells, there are also publications about that). This is mediated by the expression of the Kv1.1 potassium channel. Neurons with low Er81 expression have less Kv1.1, and these neurons, fast spiking basket cells, respond without latency. These neurons receive both E and I input. Neurons with high Er81 expression have more Kv1.1 channels, and these neurons (primarily basket cells again) have noticeable latencies. In slices it was found that cells of this kind have mostly E input and much less I input.
It then was shown that these ‘kinds’ of neurons actually undergo adult plasticity. A simple experiment – stimulation with kainate, and inhibition with nifedipine, a L-type calcium blocker – showed that Er81 expression was regulated inversely proportional to total network activity, and that this was observable after approximately two hours. So this is a kind of internal plasticity on the same time scale as LTP/LTD.
Additional experiments showed that Er81 plasticity was mediated by calcium entry into the cell (as so many other forms of plasticity), so we have evidence for a cell-specific regulation of Er81.
More precisely, the internal memory is the level of Er81. This can be a long-term storage element and remain constant over long time periods. The plasticity is intrinsic, i.e. in the expression of ion channels. The internal memory sets a parameter on the membrane (mu KAs). When the internal memory changes – a new value emerges, the old value is overwritten – then there is a read-out at the membrane in terms of the mu KAs parameter. So in this particular case, it seems as if the internal value is superfluous, and the mu KAs is identical to epsilon Er81. But this is a mistake, in reality, mu KAs is set by a number of factors, and epsilon ER81 very probably has other effects in the system as well.
It is not clear from this work, why the innervation by E and I neurons is different,  and also how and whether this changes, on the same time scale, or at all.
A surprising observation from this paper is also that high activity causes latencies of interneurons to appear, but low activity abolishes them. One might think that with less latency, there is more inhibition in the network, and high activity abolishes latencies to upregulate inhibition. That is not the case.
Without a simulation, I’d guess that inhibitory latencies reduce excitatory pressure; where activation is stored in the membrane potential of I neurons without letting them spike. There is then reduced spiking of I neurons, but still a reduction of overall excitation in the network, since the capacity of the I neuron to buffer synaptic input is enhanced. These neurons receive mostly E input, because they have this buffer capacity, no-latency neurons in contrast participate in disinhibition – they respond to the level of inhibition as well and adjust their activity. There is more electrical activity stored in the network with longer latencies but less spiking. This is just a guess on the behavior of the network.
Summarizing: a cytoplasmic protein Er81 regulates the density of Kv1.1 channels, which is a form of intrinsic plasticity that is set by a cell-internal signal. The density of Kv1.1 channels influences spike latency and overall spike frequency. Synaptic plasticity is not even used in this scenario.
Tuning of fast-spiking interneuron properties by an activity-dependent transcriptional switch
Nathalie Dehorter etal.
Science  11 Sep 2015: Vol. 349, Issue 6253, pp. 1216-1220
DOI: 10.1126/science.aab3415

Balanced Inhibition-Excitation

Another idea that I consider ill-conceived is the notion that neural networks need to have balanced inhibition-excitation. This means that with every rise (or fall) of overall excitation of the network, inhibition has to closely match it.

On the one hand, this looks like a truism: excitation activates inhibitory neurons and therefore larger excitation means larger inhibition, which reduces excitation. However, the idea in the present form stems from neural modeling: conventional neural networks with their uniform neurons and dispersed connectivity easily either stop spiking because of a lack of activity, or spike at very high rates and ‘fill up’ the whole network to capacity. It is difficult to tune them to the space in-between, and difficult to keep them in this space. Therefore it was postulated that biological neural networks face the same problem and that here also excitation and inhibition need to be closely matched.

First of all inhibition is not simple. Inhibitory-inhibitory interactions make the simplistic explanation unrealistic, and the many different types of inhibitory neurons that have evolved again make it difficult to implement the balanced inhibition-excitation concept.

Secondly, more evolved and realistic neural networks do not face the tuning problem, they are resilient even with larger and smaller differences between inhibition and excitation.

Finally, there are a number of experimental findings showing that it is possible to tune inhibition in the absence of tuning excitation. In a coupled negative feedback model this simply means that the equilibrium values change. But some excitatory neurons may evolve strong activity without directly increasing their own inhibition. Inhibition needs not to be uniformly coupled to excitation, if a network can tolerate fairly large fluctuations in excitation.

Ubiquitous interneurons may still be responsible for guarding lower and upper levels of excitation (‘range-control’). This range may still be variably positioned.

In the next post I want to discuss an interesting form of regulation of inhibitory neurons, which also does not fit well  with the concept of balanced inhibition-excitation.