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Continual Learning with Columnar Spiking Neural Networks
Denis Larionov, Nikolay Bazenkov, Mikhail Kiselev
https://arxiv.org/abs/2506.17169 https://…

Continual Learning with Columnar Spiking Neural Networks
This study investigates columnar-organized spiking neural networks (SNNs) for continual learning and catastrophic forgetting. Using CoLaNET (Columnar Layered Network), we show that microcolumns adapt most efficiently to new tasks when they lack shared structure with prior learning. We demonstrate how CoLaNET hyperparameters govern the trade-off between retaining old knowledge (stability) and acquiring new information (plasticity). Our optimal configuration learns ten sequential MNIST tasks effe…

High-accuracy inference using HfO$_x$S$_y$/HfS$_2$ Memristors
Aferdita Xhameni, Antonio Lombardo
https://arxiv.org/abs/2506.17174 https://

High-accuracy inference using HfO$_x$S$_y$/HfS$_2$ Memristors
We demonstrate high accuracy classification for handwritten digits from the MNIST dataset ($\sim$98.00$\%$) and RGB images from the CIFAR-10 dataset ($\sim$86.80$\%$) by using resistive memories based on a 2D van-der-Waals semiconductor: hafnium disulfide (HfS$_2$). These memories are fabricated via dry thermal oxidation, forming vertical crossbar HfO$_x$S$_y$/HfS$_2$ devices with a highly-ordered oxide-semiconductor structure. Our devices operate without electroforming or current compliance an…

A Hybrid Multi-Well Hopfield-CNN with Feature Extraction and K-Means for MNIST Classification
Ahmed Farooq
https://arxiv.org/abs/2507.08766 https://…

A Hybrid Multi-Well Hopfield-CNN with Feature Extraction and K-Means for MNIST Classification
This study presents a hybrid model for classifying handwritten digits in the MNIST dataset, combining convolutional neural networks (CNNs) with a multi-well Hopfield network. The approach employs a CNN to extract high-dimensional features from input images, which are then clustered into class-specific prototypes using k-means clustering. These prototypes serve as attractors in a multi-well energy landscape, where a Hopfield network performs classification by minimizing an energy function that b…

Improving Diagnostic Accuracy of Pigmented Skin Lesions With CNNs: an Application on the DermaMNIST Dataset
Nerma Kadric, Amila Akagic, Medina Kapo
https://arxiv.org/abs/2507.12961

Improving Diagnostic Accuracy of Pigmented Skin Lesions With CNNs: an Application on the DermaMNIST Dataset
Pigmented skin lesions represent localized areas of increased melanin and can indicate serious conditions like melanoma, a major contributor to skin cancer mortality. The MedMNIST v2 dataset, inspired by MNIST, was recently introduced to advance research in biomedical imaging and includes DermaMNIST, a dataset for classifying pigmented lesions based on the HAM10000 dataset. This study assesses ResNet-50 and EfficientNetV2L models for multi-class classification using DermaMNIST, employing transf…

SFATTI: Spiking FPGA Accelerator for Temporal Task-driven Inference -- A Case Study on MNIST
Alessio Caviglia, Filippo Marostica, Alessio Carpegna, Alessandro Savino, Stefano Di Carlo
https://arxiv.org/abs/2507.10561

SFATTI: Spiking FPGA Accelerator for Temporal Task-driven Inference -- A Case Study on MNIST
Hardware accelerators are essential for achieving low-latency, energy-efficient inference in edge applications like image recognition. Spiking Neural Networks (SNNs) are particularly promising due to their event-driven and temporally sparse nature, making them well-suited for low-power Field Programmable Gate Array (FPGA)-based deployment. This paper explores using the open-source Spiker+ framework to generate optimized SNNs accelerators for handwritten digit recognition on the MNIST dataset. S…

Integration of Contrastive Predictive Coding and Spiking Neural Networks
Emirhan Bilgi\c{c}, Neslihan Serap \c{S}eng\"or, Nam{\i}k Berk Yalab{\i}k, Yavuz Selim \.I\c{s}ler, Aykut G\"orkem Gelen, Rahmi Elibol
https://arxiv.org/abs/2506.09194

Integration of Contrastive Predictive Coding and Spiking Neural Networks
This study examines the integration of Contrastive Predictive Coding (CPC) with Spiking Neural Networks (SNN). While CPC learns the predictive structure of data to generate meaningful representations, SNN mimics the computational processes of biological neural systems over time. In this study, the goal is to develop a predictive coding model with greater biological plausibility by processing inputs and outputs in a spike-based system. The proposed model was tested on the MNIST dataset and achie…

Comparative Analysis of Novel NIRMAL Optimizer Against Adam and SGD with Momentum
Nirmal Gaud, Surej Mouli, Preeti Katiyar, Vaduguru Venkata Ramya
https://arxiv.org/abs/2508.04293

Comparative Analysis of Novel NIRMAL Optimizer Against Adam and SGD with Momentum
This study proposes NIRMAL (Novel Integrated Robust Multi-Adaptation Learning), a novel optimization algorithm that combines multiple strategies inspired by the movements of the chess piece. These strategies include gradient descent, momentum, stochastic perturbations, adaptive learning rates, and non-linear transformations. We carefully evaluated NIRMAL against two widely used and successful optimizers, Adam and SGD with Momentum, on four benchmark image classification datasets: MNIST, Fashion…

Transfer entropy and O-information to detect grokking in tensor network multi-class classification problems
Domenico Pomarico, Roberto Cilli, Alfonso Monaco, Loredana Bellantuono, Marianna La Rocca, Tommaso Maggipinto, Giuseppe Magnifico, Marlis Ontivero Ortega, Ester Pantaleo, Sabina Tangaro, Sebastiano Stramaglia, Roberto Bellotti, Nicola Amoroso
https://

Transfer entropy and O-information to detect grokking in tensor network multi-class classification problems
Quantum-enhanced machine learning, encompassing both quantum algorithms and quantum-inspired classical methods such as tensor networks, offers promising tools for extracting structure from complex, high-dimensional data. In this work, we study the training dynamics of Matrix Product State (MPS) classifiers applied to three-class problems, using both fashion MNIST and hyper-spectral satellite imagery as representative datasets. We investigate the phenomenon of grokking, where generalization emer…

Tangma: A Tanh-Guided Activation Function with Learnable Parameters
Shreel Golwala
https://arxiv.org/abs/2507.10560 https://arxiv.org…

Tangma: A Tanh-Guided Activation Function with Learnable Parameters
Activation functions are key to effective backpropagation and expressiveness in deep neural networks. This work introduces Tangma, a new activation function that combines the smooth shape of the hyperbolic tangent with two learnable parameters: $α$, which shifts the curve's inflection point to adjust neuron activation, and $γ$, which adds linearity to preserve weak gradients and improve training stability. Tangma was evaluated on MNIST and CIFAR-10 using custom networks composed of convolutio…

Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architectures
Siyu Yu, Zihan Qin, Tingshan Liu, Beiya Xu, R. Jacob Vogelstein, Jason Brown, Joshua T. Vogelstein
https://arxiv.org/abs/2507.10951

Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architectures
The complete connectome of the Drosophila larva brain offers a unique opportunity to investigate whether biologically evolved circuits can support artificial intelligence. We convert this wiring diagram into a Biological Processing Unit (BPU), a fixed recurrent network derived directly from synaptic connectivity. Despite its modest size 3,000 neurons and 65,000 weights between them), the unmodified BPU achieves 98% accuracy on MNIST and 58% on CIFAR-10, surpassing size-matched MLPs. Scaling the…

A Unified Platform to Evaluate STDP Learning Rule and Synapse Model using Pattern Recognition in a Spiking Neural Network
Jaskirat Singh Maskeen, Sandip Lashkare
https://arxiv.org/abs/2506.19377

A Unified Platform to Evaluate STDP Learning Rule and Synapse Model using Pattern Recognition in a Spiking Neural Network
We develop a unified platform to evaluate Ideal, Linear, and Non-linear $\text{Pr}_{0.7}\text{Ca}_{0.3}\text{MnO}_{3}$ memristor-based synapse models, each getting progressively closer to hardware realism, alongside four STDP learning rules in a two-layer SNN with LIF neurons and adaptive thresholds for five-class MNIST classification. On MNIST with small train set and large test set, our two-layer SNN with ideal, 25-state, and 12-state nonlinear memristor synapses achieves 92.73 %, 91.07 %, an…

Spiffy: Efficient Implementation of CoLaNET for Raspberry Pi
Andrey Derzhavin, Denis Larionov
https://arxiv.org/abs/2506.18306 https://

Spiffy: Efficient Implementation of CoLaNET for Raspberry Pi
This paper presents a lightweight software-based approach for running spiking neural networks (SNNs) without relying on specialized neuromorphic hardware or frameworks. Instead, we implement a specific SNN architecture (CoLaNET) in Rust and optimize it for common computing platforms. As a case study, we demonstrate our implementation, called Spiffy, on a Raspberry Pi using the MNIST dataset. Spiffy achieves 92% accuracy with low latency - just 0.9 ms per training step and 0.45 ms per inference …