It is argued that NeuroAI, neuroscience-informed artificial intelligence, has the potential to overcome limitations of current AI while deepening the authors' understanding of biological neural computation, and requires a new generation of researchers trained across the boundary between neuroscience and engineering.
A. Zador, J. Fellous, Terrence J. Sejnowski et al.· arXiv.org· 3 citations
A neuroscience-informed SSL model based on PC and active perception that sidesteps the need for a generative model of sensory input by learning to predict representations of data across parallel streams, resulting in an encoder-only learning-and-inference scheme.
Alexander G. Ororbia, K. Friston, Rajesh P. N. Rao· 0 citations
Experimental results demonstrate that the proposed uncertainty-aware replay buffer enables an RL agent to obtain higher rewards during training compared to other existing uncertainty-aware RL frameworks.
Sheeraja Rajakrishnan, Alexander G. Ororbia, Travis J. Desell et al.· 0 citations
The results demonstrate that SOM-based statistical replay offers a scalable, label-free, exemplar-free approach to class-incremental continual learning under standard task-boundary protocols.
Pujan Thapa, Alexander G. Ororbia, Travis J. Desell· 0 citations
This work highlights the effectiveness of an unsupervised, adaptive, topology-driven neural form of statistical replay as a scalable, flexible approach to continual learning.
Pujan Thapa, Alexander G. Ororbia, Travis J. Desell· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
