Search Results for author:
Found 5 papers, 2 papers with code
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Neuroscience has long been an essential driver of progress in artificial intelligence (AI).
How do neurons operate on sparse distributed representations? A mathematical theory of sparsity, neurons and active dendrites
Our model is inspired by recent experimental findings on active dendritic processing and NMDA spikes in pyramidal neurons.
Continuous online sequence learning with an unsupervised neural network model
In this paper, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data.
Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex
Given the similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory is a universal property of neocortical tissue.
Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory
Empirical evidence demonstrates that every region of the neocortex represents information using sparse activity patterns.
