The dimensionality of neural representations is at the center of an intense debate. Theoretically, a low-dimensional representation buys you robustness and generalizability. On the other hand, high-dimensional representations are more flexible, because they are more easily decoded by downstream areas. This theoretical tension is reflected in paradoxical findings in neuroscience. With reconciliatory potential, some have argued that the widespread low-dimensional representations in neurophysiological recordings could simply reflect the dimensionality of the task the animals are engaged with.
This workshop will bring together evidence from both views, with the hope of clarifying their interaction in particular in three broad fields:
1. Decision Making
2. Learning in Artificial Neural Networks
3. Motor Learning
With the help of the audience and the speakers, we would like to address the following general questions:
1. Does the brain use both high and low dimensional representations, and if so, for what purposes?
2. Can the paradoxical empirical findings be reconciled? For example, seeing neural representations as low-dimensional with respect to the neural space (~10^10) but high dimensional with respect to the task variables (<10)?
3. How does dimensionality change during learning, both in biological and artificial networks?