Speaker
Description
How the brain controls complex behaviour is still an open question in neuroscience. Foremost, the ability to flexibly adapt movements to new conditions or goals is puzzling. Recent experimental evidence supports the idea of a fixed set of neural covariation patterns, called neural modes, which is flexibly used to create different kinds of movements [1]. The space these neural modes span is called neural manifold. Another set of studies suggest that fast motor adaptation is happening through changes within the original neural manifold, but new covariation patterns can be acquired over longer timescales [2,3,4].
By using computational modelling, we explore the underlying constraints for within- and outside-manifold learning from a network perspective. Firstly, we test whether a generic optimization algorithm which acts on the recurrent weights is enough to explain the experimental discrepancy between within- and outside-manifold learning. Interestingly, we find that there is no intrinsic limitation preferring within-manifold learning. We don’t find evidence that the change in recurrent connections is bigger for outside-manifold learning than for within-manifold learning. In a next step, we dismiss the assumption of a perfect teacher signal which is biologically implausible. Instead, we train a feedback model which infers the error signal on the single neuron level. This error signal is used by the generic algorithm to adapt the recurrent weights accordingly. We find that the feedback model of the within-manifold perturbation can be learned to some extent, whereas it is not possible to infer any meaningful error information on the single neuron level for the outside-manifold perturbation. By using the learned, imperfect teacher signals, our results are consistent with the experimental findings of Sadler et al. [2], where monkeys can learn to rearrange their neural activity to within-manifold perturbations, but not to outside-manifold ones.
Our results suggest that the limitation for within- and outside-manifold learning is maybe not the relearning of the recurrent dynamics itself, but the learning of the error feedback model. Though, one of the main assumptions of our work is that the neural manifold is mainly constrained by the recurrent connectivity. It remains to be investigated whether the same holds true if the manifold is predominantly shaped by external drive.
References
1. Gallego, J. A., Perich, M. G., Naufel, S. N., Ethier, C., Solla, S. A., & Miller, L. E. (2018). Cortical population activity within a preserved neural manifold underlies multiple motor behaviors. Nature communications, 9(1), 4233., 10.1038/s41467-018-06560-z
2. Sadtler, P. T., Quick, K. M., Golub, M. D., Chase, S. M., Ryu, S. I., Tyler-Kabara, E. C., Yu, B. M. & Batista, A. P. (2014). Neural constraints on learning. Nature, 512(7515), 423., 10.1038/nature13665
3. Golub, M. D., Sadtler, P. T., Oby, E. R., Quick, K. M., Ryu, S. I., Tyler-Kabara, E. C., Batista, A., Chase, S. M. & Yu, B. M. (2018). Learning by neural reassociation. Nature neuroscience, 21(4), 607-616., 10.1038/s41593-018-0095-3
4. Oby, E. R., Golub, M. D., Hennig, J. A., Degenhart, A. D., Tyler-Kabara, E. C., Yu, B. M., Chase, S. M. & Batista, A. P. (2019). New neural activity patterns emerge with long-term learning. Proceedings of the National Academy of Sciences, 201820296., 10.1073/pnas.1820296116
