28 citations · 276 across the 81 of their papers we have counts for
85 papers
Uncertainty-Driven Replay Memory for Reinforcement Learning
Sheeraja Rajakrishnan, Alexander G. Ororbia, Travis Desell +1
Uncertainty estimation provides promising capabilities for reinforcement learning (RL) agents. Notably, estimating uncertainty can reduce the training time and enable agents to obt…
Unsupervised Continual Learning with Growing Self-Organizing Maps and Synthetic Replay
Pujan Thapa, Alexander Ororbia, Travis Desell
This work presents a generative continual learning framework based on growing self-organizing maps (GSOMs) that are augmented with learned distributional statistics as well as enco…
Error Highways: Scaling Predictive Coding to Very Deep Networks
Amirhossein Mohammadi, Alexander G. Ororbia
Predictive coding networks (PCNs) offer a biologically-plausible, local-learning alternative to back-propagation of errors (backprop). Nevertheless, they have remained largely conf…
Intrinsic Vicarious Conditioning for Deep Reinforcement Learning
Rodney A Sanchez, Ferat Sahin, Alex Ororbia +1
Advancements in reinforcement learning have produced a variety of complex and useful intrinsic driving forces; crucially, these drivers operate under a direct conditioning paradigm…
NeuroAI and Beyond: Bridging Between Advances in Neuroscience and Artificial Intelligence
Anthony Zador, Jean-Marc Fellous, Terrence Sejnowski +28
Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected. Based on a workshop convened by the National Sci…
Optimizing Neurorobot Policy under Limited Demonstration Data through Preference Regret
Viet Dung Nguyen, Yuhang Song, Anh Nguyen +3
Robot reinforcement learning from demonstrations (RLfD) assumes that expert data is abundant; this is usually unrealistic in the real world given data scarcity as well as high coll…
Enhancing Eye Feature Estimation from Event Data Streams through Adaptive Inference State Space Modeling
Viet Dung Nguyen, Mobina Ghorbaninejad, Chengyi Ma +5
Eye feature extraction from event-based data streams can be performed efficiently and with low energy consumption, offering great utility to real-world eye tracking pipelines. Howe…
Beyond the Class Subspace: Teacher-Guided Training for Reliable Out-of-Distribution Detection in Single-Domain Models
Hong Yang, Devroop Kar, Qi Yu +2
Out-of-distribution (OOD) detection methods perform well on multi-domain benchmarks, yet many practical systems are trained on single-domain data. We show that this regime induces…
Formulating Reinforcement Learning for Human-Robot Collaboration through Off-Policy Evaluation
Saurav Singh, Rodney Sanchez, Alexander Ororbia +1
Reinforcement learning (RL) has the potential to transform real-world decision-making systems by enabling autonomous agents to learn from experience. Deploying RL in real-world set…
Domain Feature Collapse: Implications for Out-of-Distribution Detection and Solutions
Hong Yang, Devroop Kar, Qi Yu +2
Why do state-of-the-art OOD detection methods exhibit catastrophic failure when models are trained on single-domain datasets? We provide the first theoretical explanation for this…
Class Incremental Continual Learning with Self-Organizing Maps and Synthetic Replay
Pujan Thapa, Alexander Ororbia, Travis Desell
This work introduces a novel generative continual learning framework based on self-organizing maps (SOMs), a brain-inspired natural computing model, extended with learned distribut…
Directly Learning Stock Trading Strategies Through Profit Guided Loss Functions
Devroop Kar, Zimeng Lyu, Sheeraja Rajakrishnan +4
Stock trading has always been a challenging task due to the highly volatile nature of the stock market. Making sound trading decisions to generate profit is particularly difficult…
Extending Spike-Timing Dependent Plasticity to Learning Synaptic Delays
Marissa Dominijanni, Alexander Ororbia, Kenneth W. Regan
Synaptic delays play a crucial role in biological neuronal networks, where their modulation has been observed in mammalian learning processes. In the realm of neuromorphic computin…
Avoiding Death through Fear Intrinsic Conditioning
Rodney Sanchez, Ferat Sahin, Alexander Ororbia +1
Biological and psychological concepts have inspired reinforcement learning algorithms to create new complex behaviors that expand agents' capacity. These behaviors can be seen in t…
A Practical Guide to Tuning Spiking Neuronal Dynamics for Computational Neuroscience and NeuroAI Research
William Gebhardt, Nathan McDonald, Clare Thiem +3
In this work, we examine and study the fundamental elements of spiking neural networks (SNNs) as well as how to tune them. Concretely, we focus on two different foundational neuron…
Meta-Representational Predictive Coding: Neuroscience-Informed Self-Supervised Learning
Alexander Ororbia, Karl Friston, Rajesh P. N. Rao
Self-supervised learning has become an important paradigm in the domains of machine intelligence and computational neuroscience. Nevertheless, current work on self-supervised learn…
Tight Stability, Convergence, and Robustness Bounds for Predictive Coding Networks
Ankur Mali, Tommaso Salvatori, Alexander Ororbia
Energy-based learning algorithms, such as predictive coding (PC), have garnered significant attention in the machine learning community due to their theoretical properties, such as…
R-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models
Viet Dung Nguyen, Zhizhuo Yang, Christopher L. Buckley +1
Although research has produced promising results demonstrating the utility of active inference (AIF) in Markov decision processes (MDPs), there is relatively less work that builds…
Contrastive Learning in Memristor-based Neuromorphic Systems
Cory Merkel, Alexander Ororbia
Spiking neural networks, the third generation of artificial neural networks, have become an important family of neuron-based models that sidestep many of the key limitations facing…
Predictive Coding with Spiking Neural Networks: a Survey
Antony W. N'dri, William Gebhardt, Céline Teulière +4
In this article, we review a class of neuro-mimetic computational models that we place under the label of spiking predictive coding. Specifically, we review the general framework o…
Time-Integrated Spike-Timing-Dependent-Plasticity
William Gebhardt, Alexander G. Ororbia
In this work, we propose time-integrated spike-timing-dependent plasticity (TI-STDP), a mathematical model of synaptic plasticity that allows spiking neural networks to continuousl…
Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems
Viet Dung Nguyen, Reynold Bailey, Gabriel J. Diaz +3
Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. Segmentation models trained using supervised machine learning can e…
Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps
Hitesh Vaidya, Travis Desell, Ankur Mali +1
An intelligent system capable of continual learning is one that can process and extract knowledge from potentially infinitely long streams of pattern vectors. The major challenge t…
A Review of Neuroscience-Inspired Machine Learning
Alexander Ororbia, Ankur Mali, Adam Kohan +2
One major criticism of deep learning centers around the biological implausibility of the credit assignment schema used for learning -- backpropagation of errors. This implausibilit…