The proposed study will model the retina by using a spatial–temporal signal processing method to model the response of the retina neurons to the dynamic visual stimuli, along with a nonlinear spike generation mechanism that is biologically interpretable. An architecture based on Difference-of-Gaussians (DoG) receptive field modelling, Gaussian spatial filtering, temporal adaptation, membrane potential dynamics and stochastic Poisson spike generation to simulate the ganglion cell output under various visual conditions. To enhance physiological plausibility and the computational interpretability, the receptive field convolution, contrast normalization, nonlinear activation and firing rate saturation are added as mathematical formulations. Experimental validation indicates that the responses of the firing is in good agreement with the biological for various kinds of stimuli with the RMSE values ranging from 0.87 to 1.36 spikes/s. The framework can be applied in various computational neuroscience, neuromorphic vision systems and retinal prosthetic modelling applications.
Deploying artificial intelligence systems for medical image analysis in clinical settings involves considerations that go beyond model accuracy: infrastructure constraints, integration with existing workflows, and generalization across patient populations all determine whether a…
An assistive camera system that uses lightweight processing to guide visually challenged and low-vision users around everyday obstacles in real time with ambiguity. There is a need to identify the visual ambiguities between geometric and photometric, texture and elevation. A dete…
Real-Time Session Control Using Temporal Logic Enforcement presents a formal system of checking and enforcing the secure behaviors of the session in dynamically computing environments. The suggested model models session states and events as formal transitions between states and u…
Accurate and prompt prediction of PM2.5 concentration is crucial to reduce the impacts of air pollution on human health and city ecosystems. In this study, a hybrid ensemble learning model for hourly PM2.5 predictions is proposed, combining advanced data preprocessing, temporal f…
The early and effective diagnosis of tomato leaf diseases is very important to enhance crop yield and reduce economic loss in precision agriculture. The conventional image-based methods are typically based on single architecture model, which cannot capture fine-grained lesion det…
- Enhancing agricultural productivity and attaining sustainable crop management depend on the early and precise identification of leaf disease. Using state-of-the-art technologies in precision agriculture like machine learning (ML) and image processing greatly increases the effec…