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Recent work characterized shifts in preparatory activity of the motor cortex during motor learning. The specific shift geometry during learning, washout, and relearning blocks was hypothesized to implement the acquisition, retention, and retrieval of motor memories. We sought to train recurrent neural network (RNN) models that could be used to study these motor learning phenomena. We built an environment for a curl field (CF) motor learning task and trained RNNs with reinforcement learning (RL) with novel regularization terms to perform behaviorally realistic reaching trajectories over the course of learning. Our choice of RL over supervised learning was motivated by the idea that motor adaptation, in the absence of demonstrations, is a process of reoptimization. We find these models, despite lack of supervision, reproduce many behavioral findings from monkey CF adaptation experiments. These models also captured key neurophysiological findings.We found that the model’s preparatory activity existed in a force-predictive subspace that remained stable across learning, washout, and relearning. Additionally, preparatory activity shifted uniformly, independently of the distance to the CF trained target. Finally, we found that the washout shift became more orthogonal to the learning shift, and hence more brain-like, when the RNNs were pretrained to have prior experience with CF dynamics. We argue the increased fit to neurophysiological recordings is driven by more generalizable and structured dynamical motifs in the model with more prior experience. This suggests that prior experience could organize preparatory neural activity underlying motor memory to have more orthogonal characteristics, by forming structured dynamical motifs in the motor cortex circuitry. 

We propose a notion of common information that allows one to quantify and separate the information that is shared between two random variables from the information that is unique to each. Our notion of common information is defined by an optimization problem over a family of functions and recovers the Gács-Körner common information as a special case. Importantly, our notion can be approximated empirically using samples from the underlying data distribution. We then provide a method to partition and quantify the common and unique information using a simple modification of a traditional variational auto-encoder. Empirically, we demonstrate that our formulation allows us to learn semantically meaningful common and unique factors of variation even on high-dimensional data such as images and videos. Moreover, on datasets where ground-truth latent factors are known, we show that we can accurately quantify the common information between the random variables. 

We introduce the Redundant Information Neural Estimator (RINE), a method that allows efficient estimation for the component of information about a target variable that is common to a set of sources, known as the “redundant information”. We show that existing definitions of the redundant information can be recast in terms of an optimization over a family of functions. In contrast to previous information decompositions, which can only be evaluated for discrete variables over small alphabets, we show that optimizing over functions enables the approximation of the redundant information for high-dimensional and continuous predictors. We demonstrate this on high-dimensional image classification and motor-neuroscience tasks. 

We introduce a notion of usable information contained in the representation learned by a deep network, and use it to study how optimal representations for the task emerge during training. We show that the implicit regularization coming from training with Stochastic Gradient Descent with a high learning-rate and small batch size plays an important role in learning minimal sufficient representations for the task. In the process of arriving at a minimal sufficient representation, we find that the content of the representation changes dynamically during training. In particular, we find that semantically meaningful but ultimately irrelevant information is encoded in the early transient dynamics of training, before being later discarded. In addition, we evaluate how perturbing the initial part of training impacts the learning dynamics and the resulting representations. We show these effects on both perceptual decision-making tasks inspired by neuroscience literature, as well as on standard image classification tasks.