154 citations · 266 across the 20 of their papers we have counts for
21 papers
Expressive probabilistic sampling in recurrent neural networks
Shirui Chen, Linxing Preston Jiang, Rajesh P. N. Rao +1
In sampling-based Bayesian models of brain function, neural activities are assumed to be samples from probability distributions that the brain uses for probabilistic computation. H…
Brain-inspired Computational Intelligence via Predictive Coding
Tommaso Salvatori, Ankur Mali, Christopher L. Buckley +4
Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks t…
Hyper-Universal Policy Approximation: Learning to Generate Actions from a Single Image using Hypernets
Dimitrios C. Gklezakos, Rishi Jha, Rajesh P. N. Rao
Inspired by Gibson's notion of object affordances in human vision, we ask the question: how can an agent learn to predict an entire action policy for a novel object or environment…
Repairing Brain-Computer Interfaces with Fault-Based Data Acquisition
Cailin Winston, Caleb Winston, Chloe N Winston +4
Brain-computer interfaces (BCIs) decode recorded neural signals from the brain and/or stimulate the brain with encoded neural signals. BCIs span both hardware and software and have…
Active Predictive Coding Networks: A Neural Solution to the Problem of Learning Reference Frames and Part-Whole Hierarchies
Dimitrios C. Gklezakos, Rajesh P. N. Rao
We introduce Active Predictive Coding Networks (APCNs), a new class of neural networks that solve a major problem posed by Hinton and others in the fields of artificial intelligenc…
Predictive Coding Theories of Cortical Function
Linxing Preston Jiang, Rajesh P. N. Rao
Predictive coding is a unifying framework for understanding perception, action and neocortical organization. In predictive coding, different areas of the neocortex implement a hier…
Emergent behavior and neural dynamics in artificial agents tracking turbulent plumes
Satpreet Harcharan Singh, Floris van Breugel, Rajesh P. N. Rao +1
Tracking a turbulent plume to locate its source is a complex control problem because it requires multi-sensory integration and must be robust to intermittent odors, changing wind d…
Brain Co-Processors: Using AI to Restore and Augment Brain Function
Rajesh P. N. Rao
Brain-computer interfaces (BCIs) use decoding algorithms to control prosthetic devices based on brain signals for restoration of lost function. Computer-brain interfaces (CBIs), on…
Investigating naturalistic hand movements by behavior mining in long-term video and neural recordings
Satpreet H. Singh, Steven M. Peterson, Rajesh P. N. Rao +1
Recent technological advances in brain recording and artificial intelligence are propelling a new paradigm in neuroscience beyond the traditional controlled experiment. Rather than…
Time-varying Autoregression with Low Rank Tensors
Kameron Decker Harris, Aleksandr Aravkin, Rajesh Rao +1
We present a windowed technique to learn parsimonious time-varying autoregressive models from multivariate timeseries. This unsupervised method uncovers interpretable spatiotempora…
Towards Neural Co-Processors for the Brain: Combining Decoding and Encoding in Brain-Computer Interfaces
Rajesh P. N. Rao
The field of brain-computer interfaces is poised to advance from the traditional goal of controlling prosthetic devices using brain signals to combining neural decoding and encodin…
BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains
Linxing Preston Jiang, Andrea Stocco, Darby M. Losey +3
We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electr…
Transformational Sparse Coding
Dimitrios C. Gklezakos, Rajesh P. N. Rao
A fundamental problem faced by object recognition systems is that objects and their features can appear in different locations, scales and orientations. Current deep learning metho…
Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic Maps
Kaiyu Zheng, Andrzej Pronobis, Rajesh P. N. Rao
We introduce Graph-Structured Sum-Product Networks (GraphSPNs), a probabilistic approach to structured prediction for problems where dependencies between latent variables are expre…
AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video
Nancy Xin Ru Wang, Ali Farhadi, Rajesh Rao +1
Developing useful interfaces between brains and machines is a grand challenge of neuroengineering. An effective interface has the capacity to not only interpret neural signals, but…
Interactive Web Application for Exploring Matrices of Neural Connectivity
David J. Caldwell, Jing Wu, Kaitlyn Casimo +2
We present here a browser-based application for visualizing patterns of connectivity in 3D stacked data matrices with large numbers of pairwise relations. Visualizing a connectivit…
Electrocorticographic Dynamics Predict Visually Guided Motor Imagery of Grasp Shaping
Jing Wu, Kaitlyn Casimo, David J. Caldwell +2
Identification of intended movement type and movement phase of hand grasp shaping are critical features for the control of volitional neuroprosthetics. We demonstrate that neural d…
Learning Deep Generative Spatial Models for Mobile Robots
Andrzej Pronobis, Rajesh P. N. Rao
We propose a new probabilistic framework that allows mobile robots to autonomously learn deep, generative models of their environments that span multiple levels of abstraction. Unl…
Multistep Model for Predicting Upper-Limb 3D Isometric Force Application from Pre-Movement Electrocorticographic Features
Jing Wu, Benjamin R. Shuman, Bingni W. Brunton +4
Neural correlates of movement planning onset and direction may be present in human electrocorticography in the signal dynamics of both motor and non-motor cortical regions. We use…
Unsupervised decoding of long-term, naturalistic human neural recordings with automated video and audio annotations
Nancy X. R. Wang, Jared D. Olson, Jeffrey G. Ojemann +2
Fully automated decoding of human activities and intentions from direct neural recordings is a tantalizing challenge in brain-computer interfacing. Most ongoing efforts have focuse…
Large Margin Boltzmann Machines and Large Margin Sigmoid Belief Networks
Xu Miao, Rajesh P. N. Rao
Current statistical models for structured prediction make simplifying assumptions about the underlying output graph structure, such as assuming a low-order Markov chain, because ex…