Constraints on neural redundancy
Abstract
Millions of neurons drive the activity of hundreds of muscles, meaning many different neural population activity patterns could generate the same movement. Studies have suggested that these redundant (i.e., behaviorally equivalent) activity patterns may be beneficial for neural computation. However, it is unknown what constraints may limit the selection of different redundant activity patterns. We leveraged a brain-computer interface, allowing us to define precisely which neural activity patterns were redundant. Rhesus monkeys made cursor movements by modulating neural activity in primary motor cortex. We attempted to predict the observed distribution of redundant neural activity. Principles inspired by work on muscular redundancy did not accurately predict these distributions. Surprisingly, the distributions of redundant neural activity and task-relevant activity were coupled, which enabled accurate predictions of the distributions of redundant activity. This suggests limits on the extent to which redundancy may be exploited by the brain for computation.
Data availability
Source data files have been provided for Figures 2-6. Code for analysis has been made available at https://github.com/mobeets/neural-redundancy-elife2018, with an MIT open source license (copy archived at https://github.com/elifesciences-publications/neural-redundancy-elife2018).
Article and author information
Author details
Funding
National Science Foundation (NCS BCS1533672)
- Aaron P Batista
- Byron M Yu
- Steven M Chase
National Institutes of Health (R01 HD071686)
- Aaron P Batista
- Byron M Yu
- Steven M Chase
National Science Foundation (Career award IOS1553252)
- Steven M Chase
National Institutes of Health (CRCNS R01 NS105318)
- Aaron P Batista
- Byron M Yu
Craig H. Neilsen Foundation (280028)
- Aaron P Batista
- Byron M Yu
- Steven M Chase
Simons Foundation (364994)
- Byron M Yu
Pennsylvania Department of Health (Research Formula Grant SAP 4100077048)
- Byron M Yu
- Steven M Chase
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 animal handling procedures were approved by the University of Pittsburgh Institutional Animal Care and Use Committee (protocol #15096685) in accordance with NIH guidelines. All surgery was performed under general anesthesia and strictly sterile conditions, and every effort was made to minimize suffering.
Copyright
© 2018, Hennig et al.
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
-
- 5,745
- views
-
- 661
- downloads
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
Synaptic inhibition is the mechanistic backbone of a suite of cortical functions, not the least of which are maintaining network stability and modulating neuronal gain. In cortical models with a single inhibitory neuron class, network stabilization and gain control work in opposition to one another – meaning high gain coincides with low stability and vice versa. It is now clear that cortical inhibition is diverse, with molecularly distinguished cell classes having distinct positions within the cortical circuit. We analyze circuit models with pyramidal neurons (E) as well as parvalbumin (PV) and somatostatin (SOM) expressing interneurons. We show how, in E – PV – SOM recurrently connected networks, SOM-mediated modulation can lead to simultaneous increases in neuronal gain and network stability. Our work exposes how the impact of a modulation mediated by SOM neurons depends critically on circuit connectivity and the network state.
-
- Neuroscience
The hippocampus is believed to encode episodic memory by binding information about the content of experience within a spatiotemporal framework encoding the location and temporal context of that experience. Previous work implies a distinction between positional inputs to the hippocampus from upstream brain regions that provide information about an animal’s location and nonpositional inputs which provide information about the content of experience, both sensory and navigational. Here, we leverage the phenomenon of ‘place field repetition’ to better understand the functional dissociation between positional and nonpositional information encoded in CA1. Rats navigated freely on a novel maze consisting of linear segments arranged in a rectilinear, city-block configuration, which combined elements of open-field foraging and linear-track tasks. Unlike typical results in open-field foraging, place fields were directionally tuned on the maze, even though the animal’s behavior was not constrained to extended, one-dimensional (1D) trajectories. Repeating fields from the same cell tended to have the same directional preference when the fields were aligned along a linear corridor of the maze, but they showed uncorrelated directional preferences when they were unaligned across different corridors. Lastly, individual fields displayed complex time dynamics which resulted in the population activity changing gradually over the course of minutes. These temporal dynamics were evident across repeating fields of the same cell. These results demonstrate that the positional inputs that drive a cell to fire in similar locations across the maze can be behaviorally and temporally dissociated from the nonpositional inputs that alter the firing rates of the cell within its place fields, offering a potential mechanism to increase the flexibility of the system to encode episodic variables within a spatiotemporal framework provided by place cells.