This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...

A team led by the BRAINS Center for Brain-Inspired Computing at the University of Twente has demonstrated a new way to make electronic materials adapt in a manner comparable to machine learning. Their ...

In today's volatile business landscape, relying on sporadic flashes of inspiration can be a fragile strategy for maintaining a competitive edge. The truth is, creativity is not a mystical gift ...

The proposed algorithm applies the concept of forward-forward network to CNNs, avoiding away traditional back-propagation and its limitations SEOUL, South Korea, Oct. 16, 2025 /PRNewswire/ -- Deep ...

VFF-Net introduces three new methodologies: label-wise noise labelling (LWNL), cosine similarity-based contrastive loss (CSCL), and layer grouping (LG), addressing the challenges of applying a forward ...

For more than eighty years, deep learning has relied on a simplified model of brain function. The 1943 McCulloch-Pitts model of the neuron fueled breakthroughs in image recognition, speech synthesis ...

Kerala-based Bloq Quantum enables enterprises to create bespoke algorithms, run them on quantum computers, and connect them to their systems through APIs The startup translates academic algorithms ...

Google’s June 2025 Core Update just finished. What’s notable is that while some say it was a big update, it didn’t feel disruptive, indicating that the changes may have been more subtle than game ...

ABSTRACT: This paper proposes a unique approach to load forecasting using a fast convergent artificial neural network (ANN) and is driven by the critical need for power system planning. The Mazoon ...

One July afternoon in 2024, Ryan Williams set out to prove himself wrong. Two months had passed since he’d hit upon a startling discovery about the relationship between time and memory in computing.

Abstract: The resistance distance is a fundamental metric in circuit networks, with broad applications across various domains. While signed graphs provide a more expressive framework for modeling real ...