Continuous online sequence learning with an unsupervised neural network model (1512.05463v2)

Abstract: 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, hierarchical temporal memory (HTM) sequence memory is recently proposed as a theoretical framework for sequence learning in the cortex. In this paper, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variable-order temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average (ARIMA), feedforward neural networks: online sequential extreme learning machine (ELM), and recurrent neural networks: long short-term memory (LSTM) and echo-state networks (ESN), on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyper- parameters tuning. Therefore the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem, but is also applicable to a wide range of real-world problems such as discrete and continuous sequence prediction, anomaly detection, and sequence classification.

• The paper presents HTM sequence memory for continuous online learning, employing an unsupervised Hebbian-style rule to achieve high-order sequence predictions.
• It leverages sparse distributed representations to process temporal data dynamically, reducing the need for extensive hyperparameter tuning common in traditional models.
• Experiments, including tests on NYC taxi demand data, confirm HTM's ability to handle noise and ambiguous sequences, promising efficient real-time analytics.

Overview of Continuous Online Sequence Learning with Unsupervised Neural Network Model

The paper explores the application of Hierarchical Temporal Memory (HTM) sequence memory for continuous online sequence learning, leveraging an unsupervised neural network approach. This work investigates the properties of HTM sequence memory, which is proposed as a theoretical framework for sequence learning in the cortex, offering a comparison with traditional sequence learning algorithms including ARIMA, LSTM, ELM, and ESN across a variety of sequence prediction problems.

Summary of Methodological Approach

At the core of this research is HTM sequence memory, which employs sparse distributed representations (SDRs) in processing temporal data sequences. Unlike traditional machine learning paradigms requiring extensive hyperparameter tuning and batch learning, HTM emphasizes continuous learning using an unsupervised Hebbian-style learning rule. This facilitates learning from each data input individually, making it well-suited to adapt dynamically to evolving data environments. HTM is particularly noted for its ability to handle high-order sequence predictions, offer robustness to noise and system faults, and eliminate the need for task-specific hyperparameter optimization.

Comparative Analysis and Results

The evaluation framework juxtaposes the HTM model with prevalent methods in sequence learning, notably ARIMA, ELM, LSTM, and ESN. Experiments spanned both synthetic high-order sequences and real-world datasets, such as the New York City taxi passenger demand data. HTM demonstrated comparable accuracy to LSTM, the state-of-the-art model, by maintaining strong flexibility and responsiveness to non-stationary data without explicit retraining phases or reliance on historical data buffers, which are typical prerequisites in LSTM processing.

Furthermore, the HTM model's inherent ability to provide multiple predictions from its sparse representations underscored its adeptness at handling branching sequences. For instance, in scenarios requiring simultaneous multiple predictions, HTM rapidly achieved complete accuracy, indicating the model’s efficacy in handling ambiguous or multivariate future outcomes.

Implications and Future Directions

The implications of this paper extend to both practical applications and theoretical advancements. Practically, the HTM model's architecture is advantageous for real-time analytics in complex and dynamically changing environments, holding potential in domains such as anomaly detection and sequence classification. Theoretically, this work advances our understanding of learning and processing in the brain, mirroring the adaptive capacities of the cortex in machine learning contexts.

Despite its advantages, the paper acknowledges the sensitivity of the HTM model to temporal noise within sequences and its potential limitations in learning very long-term dependencies from single-pass training. Future research is anticipated to explore hierarchical implementations to mitigate these issues and expand capabilities to higher-dimensional data inputs, possibly through integration with deep learning frameworks.

The HTM sequence memory presents a promising paradigm for modeling biological learning processes in artificial systems and sets a foundation for further innovations in relentless, adaptive learning models.