Recent advances in neuroscience data acquisition allow for the simultaneous recording of large populations of neurons across multiple brain areas while subjects perform complex cognitive tasks. Interpreting these data requires us to index how task-relevant information is shared across brain regions, but this is often confounded by the mixing of different task parameters at the single neuron level. Here, inspired by a method developed for a single brain area, we introduce a new technique for demixing variables across multiple brain areas, called demixed shared component analysis (dSCA).

This section details how P AML can be combined with Gaussian processes, as in our experiments. Alternatively, one can use other probabilistic methods, e.g., Bayesian Neural Networks [1]. Secondly, it enables mini-batch training for further improvement in computational efficiency. During the evaluation, we compute the errors with respect to the normalized outputs, since the observed environments' state representations include dimensions of differing We use control signals that alternate back and forth from one end of the range to the other to generate trajectories. This policy resulted in better coverage of the state-space, compared to a random walk.

Recent advances in neuroscience data acquisition allow for the simultaneous recording of large populations of neurons across multiple brain areas while subjects perform complex cognitive tasks.

Data-efficient learning algorithms are essential in many practical applications where data collection is expensive, e.g., in robotics due to the wear and tear.

We highly appreciate the reviewers' time, efforts, and valuable suggestions! R3, R4 asked for further clarification on the differences between existing work and our approach. P AML and ACL can be seen as complimentary approaches, e.g., P AML might be used to R1 also mentions that only one of the environments is learned from pixel data. Lastly, we will add an analysis of the settings fully observed 4.1 and pixel-descriptor 4.4. With space constraints in mind and since our work's goal is to incorporate active ML approach used in this work in Section 2. Control signals.

Meta-reinforcement learning and multi-task reinforcement learning aim to improve the sample efficiency by leveraging the shared structure within a family of tasks.

Updating only these task-specific modules then allows the model to be adapted to low-data tasks for as many steps as necessary without risking overfitting. Unfortunately, existing meta-learning methods either do not scale to long adaptation or else rely on handcrafted task-specific architectures. Here, we propose a meta-learning approach that obviates the need for this often sub-optimal hand-selection.