<commit_before>---
author: "<NAME>, <NAME> & <NAME>"
description: "Analysis of HTM sequence memory applied to various sequence learning and prediction problems. Written with a machine learning perspective, the paper contains some comparisons to statistical and Deep Learning techniques."
category: machine-learning, sequence-learning
date: 2016/11/01
hideImage: true
image: ../images/continuous-online-sequence-learning.png
link: http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00893#.WCEj8uErI18
org: Published in Neural Computation, November 2016, Vol 28. No. 11
sort: bd
title: "Continuous Online Sequence Learning with an Unsupervised Neural Network Model"
type: post
---

| | |
|-|-|
| **Authors** | <NAME>, <NAME>, and <NAME> |
| **Journal** | Neural Computation 28, `2474–2504` (2016) |
| **Online** | [Read or Download this Paper][1] |
| **Published** | November 1, 2016 |

This paper demonstrates how HTM sequence memory, a theoretical
framework for sequence learning in the cortex, helps us understand how the brain
can solve sequence learning problems and how we can apply this understanding to
real-world sequence learning problems with continuous data streams. In this
paper, we show how various properties of HTM sequence memory are applied to
sequence learning and prediction on streaming data. We then show comparisons
between HTM sequence memory and other sequence learning algorithms. These
include statistical methods, (autoregressive integrated moving average),
feedforward neural networks (time delay neural network and online sequential
extreme learning machine), and recurrent neural networks (long short-term memory
and echo-state networks). HTM achieves comparable results while displaying
several important properties like continuous online learning, the ability to
handle multiple predictions, robustness to sensor noise and fault tolerance, and
good performance without the need for task-specific tuning.

![Taxi passenger predictions](../images/continuous-online-sequence-learning.png)
> *We used HTM and other algorithms to predict taxi
  passenger count in the New York City.
  **LEFT:** Overall prediction error for various algorithms.
  **RIGHT:** After a new pattern is introduced (black dashed line), HTM quickly
  learns the new pattern and gives better prediction accuracy than LSTM due to
  its ability of continuous learning.*

## Frequently Asked Questions About This Research

### Q. What was the purpose of this study?

The purpose of this study was to compare HTM sequence memory to other sequence
learning algorithms. This is a companion paper to
[Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex][2],
which appeared in Frontiers in Neural Circuits, March 2016.

### Q. What are the key takeaways?

HTM performs well, particularly with respect to some important properties that apply<commit_msg>Meta tags - sequence learning paper<commit_after>---
author: "<NAME>, <NAME> & <NAME>"
description: "This paper contains an analysis of HTM sequence memory applied to various sequence learning and prediction problems. Written with a machine learning perspective, the paper contains some comparisons to statistical and Deep Learning techniques."
category: machine-learning, sequence-learning
date: 2016/11/01
hideImage: true
image: ../images/continuous-online-sequence-learning.png
link: http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00893#.WCEj8uErI18
org: Published in Neural Computation, November 2016, Vol 28. No. 11
sort: bd
title: "Continuous Online Sequence Learning with an Unsupervised Neural Network Model"
header: "Continuous Online Sequence Learning with an Unsupervised Neural Network Model"
keywords: "HTM sequence memory"
type: post
---

| | |
|-|-|
| **Authors** | <NAME>, <NAME>, and <NAME> |
| **Journal** | Neural Computation 28, `2474–2504` (2016) |
| **Online** | [Read or Download this Paper][1] |
| **Published** | November 1, 2016 |

This paper demonstrates how HTM sequence memory, a theoretical
framework for sequence learning in the cortex, helps us understand how the brain
can solve sequence learning problems and how we can apply this understanding to
real-world sequence learning problems with continuous data streams. In this
paper, we show how various properties of HTM sequence memory are applied to
sequence learning and prediction on streaming data. We then show comparisons
between HTM sequence memory and other sequence learning algorithms. These
include statistical methods, (autoregressive integrated moving average),
feedforward neural networks (time delay neural network and online sequential
extreme learning machine), and recurrent neural networks (long short-term memory
and echo-state networks). HTM achieves comparable results while displaying
several important properties like continuous online learning, the ability to
handle multiple predictions, robustness to sensor noise and fault tolerance, and
good performance without the need for task-specific tuning.

![HTM sequence memory - Taxi passenger predictions](../images/continuous-online-sequence-learning.png)
> *We used HTM and other algorithms to predict taxi
  passenger count in the New York City.
  **LEFT:** Overall prediction error for various algorithms.
  **RIGHT:** After a new pattern is introduced (black dashed line), HTM quickly
  learns the new pattern and gives better prediction accuracy than LSTM due to
  its ability of continuous learning.*

## Frequently Asked Questions About This Research

### Q. What was the purpose of this study?

The purpose of this study was to compare HTM sequence memory to other sequence
learning algorithms. This is a companion paper to
[Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex][2],
which appeared in Frontiers in Neural Circuits, March 2016.

### Q. What are the key takeaways?

HTM performs well, particularly with respect to some important properties that apply
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