Topic: transfer learning

The development of algorithms for high-accuracy animal tracking is fundamental to unravel the neural basis of behavior. The recent growth of Deeplabcut, an open source toolbox for 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

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 propose a novel method to predict contextual modulation of local field potential (LFP) gamma synchronization and multi-unit activity (MUA) in a single framework. Convolutional neural networks are state-of-the-art models of the primary visual cortex (V1). Sensory processing in V1 is determined by contextual modulation via feedback and recurrent connections. However, the computational goal and the neural implementation that shape V1 activity are not yet fully understood. We use transfer learning to use an object recognition convolutional neural network (VGG-16) to predict firing rates and gam