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Found 12 papers, 8 papers with code
Real-Time Anomaly Detection for Streaming Analytics
Much of the worlds data is streaming, time-series data, where anomalies give significant information in critical situations.
Ranked #8 on
Anomaly Detection
on Numenta Anomaly Benchmark
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark
Here we propose the Numenta Anomaly Benchmark (NAB), which attempts to provide a controlled and repeatable environment of open-source tools to test and measure anomaly detection algorithms on streaming data.
Ranked #4 on
Anomaly Detection
on Numenta Anomaly Benchmark
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.
How Can We Be So Dense? The Benefits of Using Highly Sparse Representations
Most artificial networks today rely on dense representations, whereas biological networks rely on sparse representations.
Grid Cell Path Integration For Movement-Based Visual Object Recognition
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.
Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments
Next, we study the performance of this architecture on two separate benchmarks requiring task-based adaptation: Meta-World, a multi-task reinforcement learning environment where a robotic agent must learn to solve a variety of manipulation tasks simultaneously; and a continual learning benchmark in which the model's prediction task changes throughout training.
Long Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling
In sequence learning tasks such as language modelling, Recurrent Neural Networks must learn relationships between input features separated by time.
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.
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.
Porting HTM Models to the Heidelberg Neuromorphic Computing Platform
Hierarchical Temporal Memory (HTM) is a computational theory of machine intelligence based on a detailed study of the neocortex.
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.
Two Sparsities Are Better Than One: Unlocking the Performance Benefits of Sparse-Sparse Networks
In this article we introduce Complementary Sparsity, a novel technique that significantly improves the performance of dual sparse networks on existing hardware.
