465 citations · 1.5k across the 12 of their papers we have counts for
12 papers
Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments
Abhiram Iyer, Karan Grewal, Akash Velu +3
A key challenge for AI is to build embodied systems that operate in dynamically changing environments. Such systems must adapt to changing task contexts and learn continuously. Alt…
Two Sparsities Are Better Than One: Unlocking the Performance Benefits of Sparse-Sparse Networks
Kevin Lee Hunter, Lawrence Spracklen, Subutai Ahmad
In principle, sparse neural networks should be significantly more efficient than traditional dense networks. Neurons in the brain exhibit two types of sparsity; they are sparsely i…
Grid Cell Path Integration For Movement-Based Visual Object Recognition
Niels Leadholm, Marcus Lewis, Subutai Ahmad
Grid cells enable the brain to model the physical space of the world and navigate effectively via path integration, updating self-position using information from self-movement. Rec…
Long Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling
Jeremy Gordon, David Rawlinson, Subutai Ahmad
In sequence learning tasks such as language modelling, Recurrent Neural Networks must learn relationships between input features separated by time. State of the art models such as…
How Can We Be So Dense? The Benefits of Using Highly Sparse Representations
Subutai Ahmad, Luiz Scheinkman
Most artificial networks today rely on dense representations, whereas biological networks rely on sparse representations. In this paper we show how sparse representations can be mo…
Real-Time Anomaly Detection for Streaming Analytics
Subutai Ahmad, Scott Purdy
Much of the worlds data is streaming, time-series data, where anomalies give significant information in critical situations. Yet detecting anomalies in streaming data is a difficul…
How do neurons operate on sparse distributed representations? A mathematical theory of sparsity, neurons and active dendrites
Subutai Ahmad, Jeff Hawkins
We propose a formal mathematical model for sparse representations and active dendrites in neocortex. Our model is inspired by recent experimental findings on active dendritic proce…
Continuous online sequence learning with an unsupervised neural network model
Yuwei Cui, Subutai Ahmad, Jeff Hawkins
The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarc…
Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex
Jeff Hawkins, Subutai Ahmad
Neocortical neurons have thousands of excitatory synapses. It is a mystery how neurons integrate the input from so many synapses and what kind of large-scale network behavior this…
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark
Alexander Lavin, Subutai Ahmad
Much of the world's data is streaming, time-series data, where anomalies give significant information in critical situations; examples abound in domains such as finance, IT, securi…
Porting HTM Models to the Heidelberg Neuromorphic Computing Platform
Sebastian Billaudelle, Subutai Ahmad
Hierarchical Temporal Memory (HTM) is a computational theory of machine intelligence based on a detailed study of the neocortex. The Heidelberg Neuromorphic Computing Platform, dev…
Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory
Subutai Ahmad, Jeff Hawkins
Empirical evidence demonstrates that every region of the neocortex represents information using sparse activity patterns. This paper examines Sparse Distributed Representations (SD…