Towards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits

no code implementations • 28 Mar 2024 • S. J. Ben Yoo, Luis El-Srouji, Suman Datta, Shimeng Yu, Jean Anne Incorvia, Alberto Salleo, Volker Sorger, Juejun Hu, Lionel C Kimerling, Kristofer Bouchard, Joy Geng, Rishidev Chaudhuri, Charan Ranganath, Randall O'Reilly

The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system has been able to match.

Self-Learning Self-Supervised Learning

The Relational Bottleneck as an Inductive Bias for Efficient Abstraction

no code implementations • 12 Sep 2023 • Taylor W. Webb, Steven M. Frankland, Awni Altabaa, Simon Segert, Kamesh Krishnamurthy, Declan Campbell, Jacob Russin, Tyler Giallanza, Zack Dulberg, Randall O'Reilly, John Lafferty, Jonathan D. Cohen

A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience.

Inductive Bias

Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain

no code implementations • 4 Oct 2022 • Kevin McKee, Ian Crandell, Rishidev Chaudhuri, Randall O'Reilly

Bayesian interpretations of neural processing require that biological mechanisms represent and operate upon probability distributions in accordance with Bayes' theorem.

Bayesian Inference