85 citations · 220 across the 13 of their papers we have counts for
14 papers
Adaptive Synaptic Failure Enables Sampling from Posterior Predictive Distributions in the Brain
Kevin McKee, Ian Crandell, Rishidev Chaudhuri +1
Bayesian interpretations of neural processing require that biological mechanisms represent and operate upon probability distributions in accordance with Bayes' theorem. Many have s…
A Neural Network Model of Continual Learning with Cognitive Control
Jacob Russin, Maryam Zolfaghar, Seongmin A. Park +2
Neural networks struggle in continual learning settings from catastrophic forgetting: when trials are blocked, new learning can overwrite the learning from previous blocks. Humans…
Locally Learned Synaptic Dropout for Complete Bayesian Inference
Kevin L. McKee, Ian C. Crandell, Rishidev Chaudhuri +1
The Bayesian brain hypothesis postulates that the brain accurately operates on statistical distributions according to Bayes' theorem. The random failure of presynaptic vesicles to…
Statistical Learning in Speech: A Biologically Based Predictive Learning Model
John Rohrlich, Randall C. O'Reilly
Infants, adults, non-human primates and non-primates all learn patterns implicitly, and they do so across modalities. The biological evidence supports the hypothesis that the mecha…
The Structure of Systematicity in the Brain
Randall C. O'Reilly, Charan Ranganath, Jacob L. Russin
A hallmark of human intelligence is the ability to adapt to new situations, by applying learned rules to new content (systematicity) and thereby enabling an open-ended number of in…
Learning Representations that Support Extrapolation
Taylor W. Webb, Zachary Dulberg, Steven M. Frankland +3
Extrapolation -- the ability to make inferences that go beyond the scope of one's experiences -- is a hallmark of human intelligence. By contrast, the generalization exhibited by c…
Deep Predictive Learning in Neocortex and Pulvinar
Randall C. O'Reilly, Jacob L. Russin, Maryam Zolfaghar +1
How do humans learn from raw sensory experience? Throughout life, but most obviously in infancy, we learn without explicit instruction. We propose a detailed biological mechanism f…
Neural Mechanisms of Human Decision-Making
Seth Herd, Kai Krueger, Ananta Nair +2
We present a computational and theoretical model of the neural mechanisms underlying human decision-making. We propose a detailed model of the interaction between brain regions, un…
Compositional generalization in a deep seq2seq model by separating syntax and semantics
Jake Russin, Jason Jo, Randall C. O'Reilly +1
Standard methods in deep learning for natural language processing fail to capture the compositional structure of human language that allows for systematic generalization outside of…
Deep Predictive Learning: A Comprehensive Model of Three Visual Streams
Randall C. O'Reilly, Dean R. Wyatte, John Rohrlich
How does the neocortex learn and develop the foundations of all our high-level cognitive abilities? We present a comprehensive framework spanning biological, computational, and cog…
The cerebellum could solve the motor error problem through error increase prediction
Sergio Verduzco-Flores, Randall C. O'Reilly
We present a cerebellar architecture with two main characteristics. The first one is that complex spikes respond to increases in sensory errors. The second one is that cerebellar m…
Learning Through Time in the Thalamocortical Loops
Randall C. O'Reilly, Dean Wyatte, John Rohrlich
We present a comprehensive, novel framework for understanding how the neocortex, including the thalamocortical loops through the deep layers, can support a temporal context represe…
Goal-Driven Cognition in the Brain: A Computational Framework
Randall C. O'Reilly, Thomas E. Hazy, Jessica Mollick +2
Current theoretical and computational models of dopamine-based reinforcement learning are largely rooted in the classical behaviorist tradition, and envision the organism as a pure…
Surely You Must All be Joking: An Outsider's Critique of Quantum Physics
Randall C. O'Reilly
A critique of the state of current quantum theory in physics is presented, based on a perspective outside the normal physics training. From this perspective, the acceptance of quan…