Transfer learning

…by using state-of-the-art deep learning methods. We implemented four models to best learn the invariant representations and overcome the predictable noise sources from elec

…to explain planning, reinforcement learning algorithms to explain exploration, and Bayesian algorithms to explain categorization. The standard approach is to search for parts in the brain corresponding to the steps of the algorithm. But we haven’t been able to find many such parts. This has led some to deny that algorithms are useful to systems neuroscience, and to interpret neural systems usi

Transcranial magnetic stimulation (TMS) is a promising tool for neuromodulatory interventions in research and clinical settings, yet its effects are highly variable. Real-time EEG-TMS seeks to mitigate this variability by analyzing electroencephalography (EEG) signals to determine the optimal timing for stimulation. However, existing EEG-informed TMS methodologies are predominantly open-loop, relying on predefined brain states and disregarding immediate and long-term stimulation effects [1, 2, 3]. Here, we propose an adaptive closed-loop TMS approach that allows dynamically adjusting stimula

…We use transfer learning to use an object recognition convolutional neural network (VGG-16) to predict firing rates and gam

Normative neural network models provide a valuable tool for answering principled ‘why’ questions about neural structure, function, and organisation from an optimization perspective. However, typical approaches often compare models without controlling for differences in architecture, training dataset or model objective. As a result, it is often difficult to make strong conclusions relating variation in model performance to a given characteristic. Here we ask what factors tend to make models predict neural responses most effectively?To investigate this, we explored how hyperparameter choice impa

Learning occurs in the context of individual prior experience. Variability in learning is, therefore, due to both differences in learning capacity as well as differences in those prior experiences. To describe the effect of prior experience on learning and to identify how the brain transfers this prior experience for current learning, we measured the behavior of many rats across two tasks on a six-arm track. In the first, reward was delivered after any transition between arms. In the second, reward was only delivered at three arms (2-3-4). To receive reward, it was necessary to alternate betwe

…marker-less pose estimation based on transfer learning with deep neural networks, promises to greatly enhance our ability to track animal pose with high-accuracy and high-efficiency, because it can match human labelling accuracy with minimal training data. Here, we implement Deeplabcut routines and develop a pipeline to accurately track the pose of mice undergoing defensive behaviors. Defensive behaviors are elicited

Brain-computer interface (BCI) applications enable innovative communication with and control of external devices for people with motor deficits by providing a bridge between brain signals and motor actions to replace and restore beneficial function. To measure neural response, Electrocorticography (ECoG) has been utilized as an effective modality for brain-computer interfaces (BCIs). Many studies have shown that both motor imagery and movement execution are located in the frontoparietal region of the brain (Hardwick et. al., 2018). In this study, we aim to demonstrate a shared representation o

Flexible computation is a hallmark of intelligent behavior. Yet, little is known about how neural networks contextually reconfigure for different computations. Humans are able to perform a new task without extensive training, presumably through the composition of elementary processes that were previously learned. Cognitive scientists have long hypothesized the possibility of a compositional neural code, where complex neural computations are made up of constituent components; however, the neural substrate underlying this structure remains elusive in biological and artificial neural networks. He

My group pioneered the development of a novel intracortical brain computer interface (iBCI) that decodes muscle activity (EMG) from signals recorded in the motor cortex of animals. We use these synthetic EMG signals to control Functional Electrical Stimulation (FES), which causes the muscles to contract and thereby restores rudimentary voluntary control of the paralyzed limb. In the past few years, there has been much interest in the fact that information from the millions of neurons active during movement can be reduced to a small number of “latent” signals in a low-dimensional manifold compu

…Generalization and transfer learning in spatial domains is evident in mirror-invariant neural scene representations, and in the reuse of reference frames and representations across similar environments. Shared reference frames appear in other mammals as well, as, for example, in rodents reusing grid-cell maps for different but perceptually similar environments. However, the fie

Flexible computation is a hallmark of intelligent behavior. Yet, little is known about how recurrently connected circuits contextually reconfigure for different computations. Neuronal activity is primarily studied during the performance of a single task due to difficulty of animal training. To investigate neural network flexibility for multiple computations, we trained recurrent neural networks (RNNs) to perform a set of sensori-motor and cognitive tasks. We examined networks through the lens of dynamical systems, where the evolution of population activity is dependent on activity in the previ

Animals naturally display a wide range of behaviours in varying contexts. Not only do their actions depend on their internal states, but also how their early sensory cortex processes information. Here, we propose that contextual top-down afferents adapt the receptive fields of sensory neurons to accommodate varying task demands. Although this view is widely supported by connectomic and electrophysiological evidence, a mechanistic and, in particular, a normative understanding is still lacking. We investigate how task-dependent top-down signals can reshape the functional mapping of sensory proce