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MPCS: Neuroplastic Continual Learning via Multi-Component Plasticity and Topology-Aware EWC Combining Trained Models in Reinforcement Learning Training Non-Differentiable Networks via Optimal Transport ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization Probe-Geometry Alignment: Erasing the Cross-Sequence Memorization Signature Below Chance Benchmarking local Hebbian learning rules for memory storage and prototype extraction Robust volatility updates for Hierarchical Gaussian Filtering Spiking Sequence Machines and Transformers Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks Attractor FCM Physical Foundation Models: Fixed hardware implementations of large-scale neural networks When Does Structure Matter in Continual Learning? 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An Improved Intelligent Agent for Mining Real-Time Databases Using Modified Cortical Learning Algorithms
N. E. Osegi · 2016-01-03 · via cs.NE updates on arXiv.org

Cortical Learning Algorithms based on the Hierarchical Temporal Memory, HTM have been developed by Numenta Incorporation from which variations and modifications are currently being investigated upon. HTM offers better promises as a future computational model of the neocortex the seat of intelligence in the brain. Currently, intelligent agents are embedded in almost every modern day electronic system found in homes, offices and industries worldwide. In this paper, we present a first step in realising useful HTM like applications specifically for mining a synthetic and real time dataset based on a novel intelligent agent framework, and demonstrate how a modified version of this very important computational technique will lead to improved recognition.

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