ArticleFrontiers in computational neuroscience2017
The HTM Spatial Pooler-A Neocortical Algorithm for Online Sparse Distributed Coding.
Article in Frontiers in computational neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- NeoSLAM: Long-Term SLAM Using Computational Models of the Brain.Sensors (Basel, Switzerland) · 2024Article
- GridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos.Sensors (Basel, Switzerland) · 2023Article
- Information-theoretic analysis of Hierarchical Temporal Memory-Spatial Pooler algorithm with a new upper bound for the standard information bottleneck method.Frontiers in computational neuroscience · 2023Article
- Discovering anomalies in big data: a review focused on the application of metaheuristics and machine learning techniques.Frontiers in big data · 2023Review
- The neural coding framework for learning generative models.Nature communications · 2022Article
- Hierarchical intrinsically motivated agent planning behavior with dreaming in grid environments.Brain informatics · 2022Article
- Study on the diversity of mental states and neuroplasticity of the brain during human-machine interaction.Frontiers in neuroscience · 2022Article
- Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments.Frontiers in neurorobotics · 2022Article
- Orientation Invariant Sensorimotor Object Recognition Using Cortical Grid Cells.Frontiers in neural circuits · 2021Article
- Anomalous Behavior Detection Framework Using HTM-Based Semantic Folding Technique.Computational and mathematical methods in medicine · 2021Article
- Gait Recognition and Understanding Based on Hierarchical Temporal Memory Using 3D Gait Semantic Folding.Sensors (Basel, Switzerland) · 2020Article
- Hybrid Circuit of Memristor and Complementary Metal-Oxide-Semiconductor for Defect-Tolerant Spatial Pooling with Boost-Factor Adjustment.Materials (Basel, Switzerland) · 2019Article
- Memristor-CMOS Hybrid Circuit for Temporal-Pooling of Sensory and Hippocampal Responses of Cortical Neurons.Materials (Basel, Switzerland) · 2019Article
- A neural data structure for novelty detection.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
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3 authors.
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Abstract
Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the SP, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells, and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the SP outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the SP in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.
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