Journal Article

Constraints on neural redundancy

Oby, Emily R, Lund, Peter J, Quick, Kristin M, Sadtler, Patrick T, Ryu, Stephen I, Chase, Steven M, Hennig, Jay A, Tyler-Kabara, Elizabeth C, Golub, Matthew D, Yu, Byron M, Batista, Aaron P

2018

Overview

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. When you swing a tennis racket, muscles in your arm contract in a specific sequence. For this to happen, millions of neurons in your brain and spinal cord must fire to make those muscles contract. If you swing the racket a second time, the same muscles in your arm will contract again. But the firing pattern of the underlying neurons will probably be different. This phenomenon, in which different patterns of neural activity generate the same outcome, is called neural redundancy. Neural redundancy allows a set of neurons to perform multiple tasks at once. For example, the same neurons may drive an arm movement while simultaneously planning the next activity. But does performing a given task constrain how often different patterns of neural activity can be produced? If so, this would limit whether other tasks could be carried out at the same time. To address this, Hennig et al. trained macaque monkeys to use a brain-computer interface (BCI). This is a device that reads out electrical brain activity and converts it into signals that can be used to control another device. The key advantage of a BCI is that the redundant activity patterns are precisely known. The monkeys learned to use their brain activity, via the BCI, to move a cursor on a computer screen in different directions. The results revealed that monkeys could only produce a limited number of different patterns of brain activity for a given BCI cursor movement. This suggests that the ability of a group of neurons to multitask is restricted. For example, if the same set of neurons is involved in both planning and performing movements, then an animal’s ability to plan a future movement will depend on the one it is currently performing. BCIs can help patients who have suffered stroke or paralysis. They enable patients to use their brain activity to control a computer or even robotic limbs. Understanding how the brain controls BCIs will help us improve their performance and deepen our knowledge of how the brain plans and performs movements. This might include designing BCIs that allow users to multitask more effectively.

• Publisher: eLife Science Publications, Ltd,eLife Sciences Publications Ltd,eLife Sciences Publications, Ltd
• Subject: Activity patterns / Animals / Brain / brain-computer interface / Brain-Computer Interfaces / Computer applications / Computer engineering / Cortex (motor) / Experiments / Hypotheses / Implants / Macaca mulatta / Models, Neurological / motor control / Motor Cortex - physiology / Movement - physiology / Muscle function / Muscles / neural computation / Neural Pathways - physiology / neural redundancy / Neurons / Neurons - physiology / Neuroscience / Paralysis / Population / Psychomotor Performance - physiology / Sports injuries / Tennis / Velocity