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

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.

Neural Models of Task Adaptation: A Tutorial on Spiking Networks for Executive Control

Understanding cognitive flexibility and task-switching mechanisms in neural systems requires biologically plausible computational models. This tutorial presents a step-by-step approach to constructing a spiking neural network (SNN) that simulates task-switching dynamics within the cognitive control network. The model incorporates biologically realistic features, including lateral inhibition, adaptive synaptic weights through unsupervised Spike Timing-Dependent Plasticity (STDP), and precise neuronal parameterization within physiologically relevant ranges. The SNN is implemented using Leaky Integrate-and-Fire (LIF) neurons, which represent excitatory (glutamatergic) and inhibitory (GABAergic) populations. We utilize two real-world datasets as tasks, demonstrating how the network learns and dynamically switches between them. Experimental design follows cognitive psychology paradigms to analyze neural adaptation, synaptic weight modifications, and emergent behaviors such as Long-Term Potentiation (LTP), Long-Term Depression (LTD), and Task-Set Reconfiguration (TSR). Through a series of structured experiments, this tutorial illustrates how variations in task-switching intervals affect performance and multitasking efficiency. The results align with empirically observed neuronal responses, offering insights into the computational underpinnings of executive function. By following this tutorial, researchers can develop and extend biologically inspired SNN models for studying cognitive processes and neural adaptation.

Valuing an Engagement Surface using a Large Scale Dynamic Causal Model

With recent rapid growth in online shopping, AI-powered Engagement Surfaces (ES) have become ubiquitous across retail services. These engagement surfaces perform an increasing range of functions, including recommending new products for purchase, reminding customers of their orders and providing delivery notifications. Understanding the causal effect of engagement surfaces on value driven for customers and businesses remains an open scientific question. In this paper, we develop a dynamic causal model at scale to disentangle value attributable to an ES, and to assess its effectiveness. We demonstrate the application of this model to inform business decision-making by understanding returns on investment in the ES, and identifying product lines and features where the ES adds the most value.

Unsupervised Spiking Neural Network Model of Prefrontal Cortex to study Task Switching with Synaptic deficiency

In this study, we build a computational model of Prefrontal Cortex (PFC) using Spiking Neural Networks (SNN) to understand how neurons adapt and respond to tasks switched under short and longer duration of stimulus changes. We also explore behavioral deficits arising out of the PFC lesions by simulating lesioned states in our Spiking architecture model. Although there are some computational models of the PFC, SNN's have not been used to model them. In this study, we use SNN's having parameters close to biologically plausible values and train the model using unsupervised Spike Timing Dependent Plasticity (STDP) learning rule. Our model is based on connectionist architectures and exhibits neural phenomena like sustained activity which helps in generating short-term or working memory. We use these features to simulate lesions by deactivating synaptic pathways and record the weight adjustments of learned patterns and capture the accuracy of learning tasks in such conditions. All our experiments are trained and recorded using a real-world Fashion MNIST (FMNIST) dataset and through this work, we bridge the gap between bio-realistic models and those that perform well in pattern recognition tasks