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Hopfield Networks Meet Big Data: A Brain-Inspired Deep Learning Framework for Semantic Data Linking

Ashwin Viswanathan Kannan, Johnson P Thomas, Abhimanyu Mukerji

TL;DR

The paper tackles the challenge of maintaining semantic integrity across massive, heterogeneous datasets by introducing a brain-inspired distributed framework that fuses deep Hopfield networks with MapReduce on HDFS. It models dual-hemisphere processing where the right hemisphere learns data usage patterns via Hopfield associative memory and the left hemisphere recalls learned representations to identify semantic links, enabling scalable data cleaning and integration. Key contributions include a palimpsest memory system, threshold-based pattern binarization, Hebbian and Oja-based learning rules, and a MapReduce-enabled architecture that reinforces useful associations while adapting to evolving data usage. The results demonstrate improved semantic linking and disambiguation in large-scale data contexts and point to future extensions with continuous Hopfield networks and transformer-based approaches to capture long-range dependencies. This work offers a biologically inspired, scalable pathway for semantic discovery and knowledge-graph-like data linking in big data ecosystems.

Abstract

The exponential rise in data generation has led to vast, heterogeneous datasets crucial for predictive analytics and decision-making. Ensuring data quality and semantic integrity remains a challenge. This paper presents a brain-inspired distributed cognitive framework that integrates deep learning with Hopfield networks to identify and link semantically related attributes across datasets. Modeled on the dual-hemisphere functionality of the human brain, the right hemisphere assimilates new information while the left retrieves learned representations for association. Our architecture, implemented on MapReduce with Hadoop Distributed File System (HDFS), leverages deep Hopfield networks as an associative memory mechanism to enhance recall of frequently co-occurring attributes and dynamically adjust relationships based on evolving data patterns. Experiments show that associative imprints in Hopfield memory are reinforced over time, ensuring linked datasets remain contextually meaningful and improving data disambiguation and integration accuracy. Our results indicate that combining deep Hopfield networks with distributed cognitive processing offers a scalable, biologically inspired approach to managing complex data relationships in large-scale environments.

Hopfield Networks Meet Big Data: A Brain-Inspired Deep Learning Framework for Semantic Data Linking

TL;DR

The paper tackles the challenge of maintaining semantic integrity across massive, heterogeneous datasets by introducing a brain-inspired distributed framework that fuses deep Hopfield networks with MapReduce on HDFS. It models dual-hemisphere processing where the right hemisphere learns data usage patterns via Hopfield associative memory and the left hemisphere recalls learned representations to identify semantic links, enabling scalable data cleaning and integration. Key contributions include a palimpsest memory system, threshold-based pattern binarization, Hebbian and Oja-based learning rules, and a MapReduce-enabled architecture that reinforces useful associations while adapting to evolving data usage. The results demonstrate improved semantic linking and disambiguation in large-scale data contexts and point to future extensions with continuous Hopfield networks and transformer-based approaches to capture long-range dependencies. This work offers a biologically inspired, scalable pathway for semantic discovery and knowledge-graph-like data linking in big data ecosystems.

Abstract

The exponential rise in data generation has led to vast, heterogeneous datasets crucial for predictive analytics and decision-making. Ensuring data quality and semantic integrity remains a challenge. This paper presents a brain-inspired distributed cognitive framework that integrates deep learning with Hopfield networks to identify and link semantically related attributes across datasets. Modeled on the dual-hemisphere functionality of the human brain, the right hemisphere assimilates new information while the left retrieves learned representations for association. Our architecture, implemented on MapReduce with Hadoop Distributed File System (HDFS), leverages deep Hopfield networks as an associative memory mechanism to enhance recall of frequently co-occurring attributes and dynamically adjust relationships based on evolving data patterns. Experiments show that associative imprints in Hopfield memory are reinforced over time, ensuring linked datasets remain contextually meaningful and improving data disambiguation and integration accuracy. Our results indicate that combining deep Hopfield networks with distributed cognitive processing offers a scalable, biologically inspired approach to managing complex data relationships in large-scale environments.

Paper Structure

This paper contains 21 sections, 10 equations, 3 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Data Cleaning
  4. Biological Neuron Framework for Semantic Identification
  5. Overview of Brain Based Architecture
  6. Brain Inspired Architecture for Identifying Associations
  7. Right Hemisphere (RH):
  8. Threshold Estimator (TE):
  9. Modeling the Hopfield neurons in RH
  10. Left Hemisphere (LH):
  11. Palimpsest Memory
  12. Hebbs and Oja's Learning rule:
  13. Experiments
  14. Dataset:
  15. Evaluation:
  16. ...and 6 more sections

Figures (3)

  • Figure 1: Human Brain Representation
  • Figure 2: Brain Model Overview. An input class pattern (vectors) is first fed to the layer of Hopfield neurons which forms the temporal lobe region in the right hemisphere (RH). Normalized vectors are stored in weight matrix (Hippocampus). Noisy patterns sent to Wernicke region in left hemisphere (LH) processes and predicts the output (Broca's region). The LH receives learned information from RH.
  • Figure : Brain Model Overview. An input class pattern (vectors) is first fed to the layer of Hopfield neurons which forms the temporal lobe region in the right hemisphere (RH). Normalized vectors are stored in weight matrix (Hippocampus). Noisy patterns sent to Wernicke region in left hemisphere (LH) processes and predicts the output (Broca's region). The LH receives learned information from RH.