Balanced Inhibition/Excitation (2) – The I/E ratio

Some time ago, I suggested that the theoretical view on balanced inhibition/excitation (in cortex and cortical models) is probably flawed. I suggested that we have a loose regulation instead, where inhibition and excitation can fluctuate independently.

The I/E balance stems from the idea that the single pyramidal neuron should receive approximately equal strength of inhibition and excitation, in spite of the fact that only 10-20% of neurons in cortex are inhibitory (Destexhe2003, more on that below). Experimental measurements have shown that this conjecture is approximately correct, i.e. inhibitory neurons make stronger contacts, or their influence is stronger relative to excitatory inputs.

The E-I balance in terms of synaptic drive onto a single pyramidal neuron is an instance of antagonistic regulation which allows gear-shifting of inputs, and in this case, allows very strong inputs to be downshifted by inhibition to a weaker effect on the membrane potential. What is the advantage of such a scheme? Strong signals are less prone to noise and uncertainty than weak signals. Weak signals are filtered out by the inhibitory drive. Strong signals allow unequivocal signal transmission, whether excitatory synaptic input, (or phasic increases of dopamine levels, in other contexts), which are then gear-shifted down by antagonistic reception. There may also be a temporal sequence: a strong signal is followed by a negative signal to restrict its time course and reduce impact. In the case of somatic inhibition following dendritic excitation the fine temporal structure could work together with the antagonistic gear-shifting exactly for this goal. Okun and Lampl, 2008 have actually shown that inhibition follows excitation by several milliseconds.

But what are the implications for an E/I network, such as cortex?

Here is an experimental result:

During both task and delay, mediodorsal thalamic (MD) neurons have 30-50% raised firing rates, fast-spiking (FS) inhibitory cortical neurons have likewise 40-60% raised firing rates, but excitatory (regular-spiking, RS) cortical neurons are unaltered. Thus there is an intervention possible, by external input from MD, probably directly to FS neurons, which does not affect RS neuron rate at all (fig. a and c, SchmittLIetal2017)

Untitled 1

Mediodorsal thalamic stimulation raises inhibition, but leaves excitation unchanged.

At the same time, in this experiment, the E-E connectivity is raised (probably by some form of short-term synaptic potentiation), such that E neurons receive more input, which is counteracted by more inhibition. (cf. also Hamilton, L2013). The balance on the level of the single neuron would be kept, but the network exhibits only loose regulation of the I/E ratio: unilateral increase of inhibition.

There are several studies which show that it is possible to raise inhibition and thus enhance cognition, for instance in the mPFC of CNTNAP2 (neurexin, a cell adhesion protein) deficient mice, which have abnormally raised excitation, and altered social behavior (SelimbeyogluAetal2017, cf. Foss-FeigJ2017 for an overview). Also, inhibition is necessary to allow critical period learning – which is hypothesized to be due to a switch from internally generated spontaneous activity to external sensory perception (ToyoizumiT2013) – in line with our suggestion that the gear-shifting effect of locally balanced I/E allows only strong signals to drive excitation and spiking and filters weak, internally generated signals.


Dendritic computation

A new paper  Universal features of dendrites through centripetal branch ordering published: July 3, 2017) shows more or less the opposite of what it cites as common wisdom: „neuronal computation is known to depend on the morphology of dendrites”

Namely, since all dendrites follow general topological principles, it is probably not the dendritic morphology that matters in a functional sense. To make a dendrite functional, i.e. let it participate in adaptive information processing, we have to refer to the ion channels and GPCRs that populate the spines and shafts and shape the generation of action potentials.

Dendritic integration: 60 years of progress. (Stuart GJ, Spruston N.) Nat Neurosci. 2015 Dec;18(12):1713-21. doi: 10.1038/nn.4157. Epub 2015 Nov 25. Review. PMID:26605882.

Plasticity of dendritic function. Magee JC, Johnston D. Curr Opin Neurobiol. 2005 Jun;15(3):334-42. Review. PMID:15922583

Gabriele Scheler BMC Neurosci. 2013; 14(Suppl 1): P344. Published online 2013 Jul 8. doi: 10.1186/1471-2202-14-S1-P344. PMCID: PMC3704850

Neuromodulation of circuits with variable parameters: single neurons and small circuits reveal principles of state-dependent and robust neuromodulation. Marder E1, O’Leary T, Shruti S. Annu Rev Neurosci. 2014;37:329-46. doi: 10.1146/annurev-neuro-071013-013958.

Bad Ideas in Neuoscience

balanced excitation inhibition

dopamine=reward learning

hidden layers

explaining attention by top-down and bottom-up processes

I should collect some more. Why are they bad? Because they are half-truths. There is “something” right about these ideas, but as scientific concepts, the way they are currently defined, I think they are wrong. Need to be replaced.

Neural Coding

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.