arXiv subjects

Gouri Lakshmi S

Publications and source records attributed to Gouri Lakshmi S.

2 recordsLinked to original sources

arXiv2026

Building Supervision into Hebbian Plasticity through Spike Agreement

Supervised learning in spiking neural networks (SNNs) typically requires either gradient-based backpropagation, which sacrifices the Hebbian, spike-driven character of biological plasticity, or reward-modulated Spike-Timing-Dependent Plasticity (STDP), in which class supervision enters only as a scalar gate on an otherwise class-agnostic correlation signal. We propose Supervised Spike Agreement-Dependent Plasticity (Supervised SADP), a gradient-free supervised Hebbian learning algorithm in which class information is embedded directly into the Hebbian plasticity computation rather than introduced through reward modulation. SADP trains the output layer via a supervised Hebbian rule that encodes class labels into output spike patterns, then trains the hidden layer by measuring each hidden neuron's chance-corrected temporal agreement, Cohen's kappa, with the correct-class output spike train produced by the forward pass without gradient computation or external reward. A K-shift extension aggregates agreement over temporal offsets, providing robustness to spike-timing jitter at linear computational cost. We evaluate Supervised SADP against reward-modulated STDP across six benchmark and medical imaging datasets, four input encoding strategies, K_shift in {5,25}, and three reward modes (none, binary, margin). Supervised SADP outperforms STDP in a significant majority of comparisons. Under Poisson encoding, SADP achieves 86.46% on MNIST and 76.62% on Fashion-MNIST, outperforming the best STDP configurations by 23.66 and 23.29 percentage points, respectively. Across the encodings tested, including CNN-extracted features, SADP outperforms STDP in the large majority of cells and trains 1.47x faster on average, with up to 2.86x speedup under Poisson inputs. These results position Supervised SADP as a stable, efficient, gradient-free alternative to reward-modulated STDP for supervised SNN learning.

cs.NE↗

arXiv2026

Towards Modality-Agnostic Medical Image Anomaly Detection: A Training-Free Manifold Refinement Approach

Deploying AI-based anomaly detection across diverse clinical imaging settings remains challenging because most existing methods rely on modality-specific architectures, anatomical priors, or extensive retraining, limiting their use as general-purpose screening tools. One-class classification (OCC) offers a label-efficient alternative by training exclusively on normal data, but conventional two-stage pipelines fit a density estimator directly on raw pretrained embeddings, leaving substantial discriminative structure in the latent space unexploited. We introduce a training-free, modality-agnostic framework that inserts an explicit manifold-refinement stage between feature extraction and anomaly scoring. Empirical density weights, estimated via a UMAP-derived neighborhood graph, guide an iterative shift of embeddings toward locally dense regions, compacting normal samples, leaving anomalies relatively isolated prior to Gaussian density estimation and Mahalanobis-based scoring. This refinement introduces no additional trainable parameters and no architectural modification, allowing it to be layered onto any pretrained encoder. Evaluated on the MedIAnomaly benchmark across seven datasets spanning five imaging modalities (X-ray, MRI, fundus, dermatoscopy, histopathology), the framework achieves the best AUC on four datasets and the best Average Precision on five datasets among methods evaluated in the benchmark, outperforming specialized reconstruction and diffusion-based methods with a single fixed hyperparameter configuration across all modalities. These results demonstrate that meaningful gains can be achieved through post-hoc geometric refinement of existing representations rather than bespoke encoders, offering a practical and scalable AI screening framework for real-world, multi-modality clinical workflows where retraining and abnormal-case annotation are costly or infeasible.

cs.CV↗