This issue features 3 papers on electrophysiology (1-3) and one on motor control (4). The first one describes electrical communication between bacteria, based on (indirectly) voltage-dependent K+ channels. This shows the universality of electrical communication based on ionic channels, something that is not specific of neurons. The second one shows how to build a simple low-cost dynamic clamp system (where the injected current depends in real time on the measured voltage), using a low-cost microcontroller (not Arduino, but similar). The third one is a (primarily) modeling study on the extracellular field produced by an axon bundle, showing how its terminal can produce strong fields. Finally, I discuss a theoretical paper which shows how spiking neurons can control a simple physical system (an inverted pendulum).
1. Prindle A, Liu J, Asally M, Garcia-Ojalvo J and GM Süel (2015). Ion channels enable electrical communication in bacterial communities. (Comment on PubPeer)
This paper describes oscillations of membrane potential and extracellular potassium in a bacterial population (shown indirectly with an optical sensor), which show radial synchronization (ie same Vm for cells at the same radius). The proposed mechanism is as follows. A wave of depolarization is initiated by some metabolic factor which makes a K+ channel open, hyperpolarizing the cell. This releases K+ in the extracellular environment. The extracellular increase in K+ reduces the Nernst potential for K+, so all neighboring cells are depolarized. The K+ channel is voltage-dependent (indirectly in the model), it opens when the cell is depolarized. So there is a hyperpolarization that releases K+ extracellularly. With appropriate nonlinearities, the result is a propagating wave of K+ and Vm, which is faster than diffusion. There is a simple Hodgkin-Huxley type model in the supplementary methods. Some of it might be a little questionable (eg K+ reversal potential increases linearly rather than logarithmically with concentration; but that might be ok for small ion fluxes and probably doesn’t change the results qualitatively), but generally sensible. It is a chain of cells coupled through the extracellular environment. It would be interesting to extend the model to a disk and see whether one can account for radial synchrony.
This is interesting for at least two reasons. One is that there is electrical communication based on ionic channels not just in neurons but also in bacteria; so probably in all living cells. Another is the mode of communication is neither gap junctions (direct electrical coupling) nor synapses (through neurotransmitters), but through changes in ionic composition of the extracellular environment. These changes should occur also in the nervous system, so could it be that neurons also communicate in this way?
2. Desai NS, Gray R, Johnston D (2017). A Dynamic Clamp on Every Rig. (Comment on PubPeer)
This paper presents a low-cost dynamic clamp system implemented with a Teensy microcontroller, which works independently of the recording PC. It makes using the dynamic clamp much simpler, when one would otherwise need an operating system with a real time kernel. The associated website is unusually good! with detailed part list and construction methods, code, advice, etc.
3. McColgan T, Liu J, Kuokkanen PT, Carr CE, Wagner H, Kempter R (2017). Dipolar extracellular potentials generated by axonal projections. (Comment on PubPeer)
The authors show that the terminal zone of an axon bundle can generate a strong dipolar extracellular field. This is particularly the case in the auditory brainstem of barn owls (and most likely of mammals), where there is a strong extracellular potential (several mV) locked to the sound, called the neurophonic. The idea is quite simple. In the terminal zone, the axons bifurcate then terminate., so that the number of axons increases, then decreases. If the wavelength of the propagating wave is right, then current is drawn into the region where axons bifurcate and exits where they terminate. This is shown numerically and theoretically, and compared to data in barn owl nucleus laminaris. One point I am wondering about is the role of axon diameters in the phenomenon; indeed at an axon bifurcation, diameters of daughter branches tend to be smaller than that of the primary branch, so one might wonder whether that might not counterbalance the increase in axon number.
4. Kang TS, Banerjee A (2017). Learning Deterministic Spiking Neuron Feedback Controllers. (Comment on PubPeer)
The authors study how spiking neurons can control an inverted pendulum. Each spike produces a force acting on the pendulum (like a muscle twitch), and the observed variables (angle and its derivative) are inputs to the neurons (it’s a single layer). The question is how to set the parameters (input gains) so that the system is stable. This is an interesting problem, which is not straightforward, despite the simplicity of the architecture. The authors simply define an error function and derive a gradient descent on parameters, which seems to work. It seems however that the gradient depends on detailed aspects of the system, so it’s not so clear that is a good solution. Nevertheless, it is interesting because it addresses a problem of learning that is not representational but directly related to behavior, in contrast with most modeling studies on synaptic plasticity and learning.