Functional diversity of excitatory commissural interneurons in adult zebrafish
Abstract
Flexibility in the bilateral coordination of muscle contraction underpins variable locomotor movements or gaits. While the locomotor rhythm is generated by ipsilateral excitatory interneurons, less is known about the commissural excitatory interneurons. Here we examined how the activity of the V0v interneurons - an important commissural neuronal class - varies with the locomotor speed in adult zebrafish. Although V0v interneurons are molecularly homogenous, their activity pattern during locomotion is not uniform. They consist of two distinct types dependent on whether they display rhythmicity or not during locomotion. The rhythmic V0v interneurons were further subdivided into three sub-classes engaged sequentially, first at slow then intermediate and finally fast locomotor speeds. Their order of recruitment is defined by scaling their synaptic current with their input resistance. Thus we uncover, in an adult vertebrate, a novel organizational principle for a key class of commissural interneurons and their recruitment pattern as a function of locomotor speed.
Article and author information
Author details
Funding
Karolinska Institutet
- Abdel El Manira
Hjärnfonden
- Abdel El Manira
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All experimental protocols were approved by the local Animal Research Ethical Committee, Stockholm (Stockholms Norra Djurförsöksetiska Nämnd). Protocol number N122/13.
Copyright
© 2016, Björnfors & El Manira
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,031
- views
-
- 444
- downloads
-
- 43
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Neuroscience
What determines when neural representations of memories move together (integrate) or apart (differentiate)? Classic supervised learning models posit that, when two stimuli predict similar outcomes, their representations should integrate. However, these models have recently been challenged by studies showing that pairing two stimuli with a shared associate can sometimes cause differentiation, depending on the parameters of the study and the brain region being examined. Here, we provide a purely unsupervised neural network model that can explain these and other related findings. The model can exhibit integration or differentiation depending on the amount of activity allowed to spread to competitors — inactive memories are not modified, connections to moderately active competitors are weakened (leading to differentiation), and connections to highly active competitors are strengthened (leading to integration). The model also makes several novel predictions — most importantly, that when differentiation occurs as a result of this unsupervised learning mechanism, it will be rapid and asymmetric, and it will give rise to anticorrelated representations in the region of the brain that is the source of the differentiation. Overall, these modeling results provide a computational explanation for a diverse set of seemingly contradictory empirical findings in the memory literature, as well as new insights into the dynamics at play during learning.
-
- Neuroscience
During delayed ballistic reaches, motor areas consistently display movement-specific activity patterns prior to movement onset. It is unclear why these patterns arise: while they have been proposed to seed an initial neural state from which the movement unfolds, recent experiments have uncovered the presence and necessity of ongoing inputs during movement, which may lessen the need for careful initialization. Here, we modeled the motor cortex as an input-driven dynamical system, and we asked what the optimal way to control this system to perform fast delayed reaches is. We find that delay-period inputs consistently arise in an optimally controlled model of M1. By studying a variety of network architectures, we could dissect and predict the situations in which it is beneficial for a network to prepare. Finally, we show that optimal input-driven control of neural dynamics gives rise to multiple phases of preparation during reach sequences, providing a novel explanation for experimentally observed features of monkey M1 activity in double reaching.