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Augmenting Supervised Learning by Meta-learning Unsupervised Local Rules
The brain performs unsupervised learning and (perhaps) simultaneous supervised learning.
A Philosophical Understanding of Representation for Neuroscience
The reasons to call some neural activity a representation and the assumptions that come with this term are not generally made clear from its common uses in neuroscience.
Towards intervention-centric causal reasoning in learning agents
Here we show how a meta-learning approach can be used to perform causal learning in this challenging setting, where the action-space is not a set of interventions and the observation space is a high-dimensional space with a latent causal structure.
Rarely-switching linear bandits: optimization of causal effects for the real world
Using this idea, and the theory of linear contextual bandits, we present a conservative policy updating procedure which updates a deterministic policy only when justified.
Reconfiguring motor circuits for a joint manual and BCI task
Compared to activity observed during naturalistic motor control, we found that both units used to drive the BCI directly (control units) and units that did not directly control the BCI (non-control units) significantly changed their tuning to wrist torque.
