Scientists gain insights into properties that enable neural circuit stability after specific perturbations.
A neural circuit comprises a population of nerve cells (neurons) interconnected by synapses. Once activated, these units carry out specific functions that result in different behaviors. Neural circuits of animals within the same species can display huge differences in the properties of their components: the neurons and the synapses.
However, these circuits still reliably fulfill the same function with nearly identical output, for instance generate a certain behavior. Researchers at the Max Planck Institute for Brain Research in Frankfurt have now used a single property, the phase difference of a rhythmic model circuit, to show that perturbations can reveal the underlying differences between different implementations of seemingly identical circuits.
Using computational models, the scientists studied neurons with biologically measured ion channel conductances, enabling them to expose relationships between specific conductances that play an essential role in circuit stability after perturbation.
Neural circuits among different animal species are made up of the same types of neurons and synapses though they may differ in arrangement and strength. Yet, they enable each individual to perform a given behavior equally well – a property that has been called "degeneracy".
This is especially the case for innate (rather than learned) behaviors that are critical for the survival of the animal. Examples of these include rhythmic behaviors like breathing and locomotion in humans or the digestion of food by the nervous system in the stomach of crabs and lobsters.
In a new computational study, Max Planck Research Group Leader Dr. Julijana Gjorgjieva and Ph.D. student Sebastian Onasch use one of the simplest circuits, called a half-center oscillator, that consists of two neurons mutually coupled with inhibition to determine what can be said about the properties of the circuits only by examining the output of the circuit.
This circuit is known to generate alternating activity, where only one neuron is active at a time, rhythmically switching to the other one. The rhythmic output of the circuit can be well described by the phase difference, a characteristic that determines the regularity of the activation of the two constituent neurons.
“In order to find the rules that underlie stable circuits we asked ourselves ‘what can we do with a single measurement that is fairly easy to obtain experimentally, such as the phase difference between two neurons’? We were surprised by the result”, explains Onasch.
The scientists first generated a large population of such model circuits consisting of neurons with biologically determined parameters. Specifically, they used a Hodgkin-Huxley neuron model with multiple intrinsic conductances measured in the nervous system of the stomach of crabs and lobsters, which can give rise to very different firing patterns of the individual neurons and circuits.
However, they found that many of these circuits actually had the same firing pattern even though the intrinsic conductances were different. The scientists then induced perturbations either from the environment or intrinsic challenges and found that these circuits responded differently – and they could quantify exactly how they differed by using statistical and machine learning techniques.
“We found that stable circuit output results from correlations between specific neuronal conductances generated by particular channel types but not others”, explains Gjorgjieva.
“Rather than studying the stability of circuits with prescribed conductance relationships, we approached the problem in reverse: starting from the stability to a given perturbation, we revealed conductance relationships of the circuit’s constituent neurons that similarly affect circuit stability when perturbed. This revealed surprising conductance combinations that could even predict the response to specific perturbations.”
“Our work identifies the conditions for the emergence of stable and robust circuit output from variable circuit elements. This has important implications for drug design and reliable neuromodulatory control”, concludes Gjorgjieva.
This work was funded by the Max Planck Society.
Dr. Julijana Gjorgjieva, Computation in Neural Circuits Group, Max Planck Institute for Brain Research, firstname.lastname@example.org, +49 69 850033 3600
Sebastian Onasch and Julijana Gjorgjieva. Circuit stability to perturbations reveals hidden variability in the balance of intrinsic and synaptic conductances. Journal of Neuroscience 16 March 2020, JN-RM-0985-19; DOI: https://doi.org/10.1523/JNEUROSCI.0985-19.2020
Dr. Irina Epstein | Max-Planck-Institut für Hirnforschung
Rising water temperatures could endanger the mating of many fish species
03.07.2020 | Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung
Moss protein corrects genetic defects of other plants
03.07.2020 | Rheinische Friedrich-Wilhelms-Universität Bonn
Solar cells based on perovskite compounds could soon make electricity generation from sunlight even more efficient and cheaper. The laboratory efficiency of these perovskite solar cells already exceeds that of the well-known silicon solar cells. An international team led by Stefan Weber from the Max Planck Institute for Polymer Research (MPI-P) in Mainz has found microscopic structures in perovskite crystals that can guide the charge transport in the solar cell. Clever alignment of these "electron highways" could make perovskite solar cells even more powerful.
Solar cells convert sunlight into electricity. During this process, the electrons of the material inside the cell absorb the energy of the light....
Empa researchers have succeeded in applying aerogels to microelectronics: Aerogels based on cellulose nanofibers can effectively shield electromagnetic radiation over a wide frequency range – and they are unrivalled in terms of weight.
Electric motors and electronic devices generate electromagnetic fields that sometimes have to be shielded in order not to affect neighboring electronic...
A promising operating mode for the plasma of a future power plant has been developed at the ASDEX Upgrade fusion device at Max Planck Institute for Plasma...
Live event – July 1, 2020 - 11:00 to 11:45 (CET)
"Automation in Aerospace Industry @ Fraunhofer IFAM"
The Fraunhofer Institute for Manufacturing Technology and Advanced Materials IFAM l Stade is presenting its forward-looking R&D portfolio for the first time at...
With an X-ray experiment at the European Synchrotron ESRF in Grenoble (France), Empa researchers were able to demonstrate how well their real-time acoustic monitoring of laser weld seams works. With almost 90 percent reliability, they detected the formation of unwanted pores that impair the quality of weld seams. Thanks to a special evaluation method based on artificial intelligence (AI), the detection process is completed in just 70 milliseconds.
Laser welding is a process suitable for joining metals and thermoplastics. It has become particularly well established in highly automated production, for...
02.07.2020 | Event News
19.05.2020 | Event News
07.04.2020 | Event News
03.07.2020 | Life Sciences
03.07.2020 | Studies and Analyses
03.07.2020 | Power and Electrical Engineering