Showing papers by "Byron M. Yu published in 2018"
TL;DR: It is shown that short-term learning relies on a fixed neural repertoire, which limits behavioral improvement, and indicates that the activity patterns that a neural population can generate are even more constrained than previously thought.
Abstract: Behavior is driven by coordinated activity across a population of neurons. Learning requires the brain to change the neural population activity produced to achieve a given behavioral goal. How does population activity reorganize during learning? We studied intracortical population activity in the primary motor cortex of rhesus macaques during short-term learning in a brain-computer interface (BCI) task. In a BCI, the mapping between neural activity and behavior is exactly known, enabling us to rigorously define hypotheses about neural reorganization during learning. We found that changes in population activity followed a suboptimal neural strategy of reassociation: animals relied on a fixed repertoire of activity patterns and associated those patterns with different movements after learning. These results indicate that the activity patterns that a neural population can generate are even more constrained than previously thought and might explain why it is often difficult to quickly learn to a high level of proficiency.
173 citations
TL;DR: Surprisingly, the distributions of redundant neural activity and task-relevant activity were coupled, which enabled accurate predictions of the distributions, suggesting limits on the extent to which redundancy may be exploited by the brain for computation.
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.
56 citations
Carnegie Mellon University1, RIKEN Brain Science Institute2, Boston University3, Harvard University4, Massachusetts Institute of Technology5, New Jersey Institute of Technology6, RWTH Aachen University7, University of Pittsburgh8, University of Washington9, Brown University10, Osaka University11, Case Western Reserve University12, Michigan State University13, University of Virginia14, Honda15, Kyoto University16
TL;DR: For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.
Abstract: Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.
55 citations
TL;DR: It is shown that attention is coded diversely in a population and is distinct between unstimulated and stimulated contexts, providing a contrast to normalized gain models of attention.
Abstract: Visual neurons respond more vigorously to an attended stimulus than an unattended one. How the brain prepares for response gain in anticipation of that stimulus is not well understood. One prominent proposal is that anticipation is characterized by gain-like modulations of spontaneous activity similar to gains in stimulus responses. Here we test an alternative idea: anticipation is characterized by a mixture of both increases and decreases of spontaneous firing rates. Such a strategy would be adaptive as it supports a simple linear scheme for disentangling internal, modulatory signals from external, sensory inputs. We recorded populations of V4 neurons in monkeys performing an attention task, and found that attention states are signaled by different mixtures of neurons across the population in the presence or absence of a stimulus. Our findings support a move from a stimulation-invariant account of anticipation towards a richer view of attentional modulation in a diverse neuronal population.
30 citations