Decoding how the brain computes with variables, such as an object's speed, encoded in populations of neurons is essential for understanding perception, decision-making, and motor control. While experiments consistently reveal correlated firing rates within these populations, the origins of these correlations are still debated. Here, I propose a computational model that carries out operations on population-encoded variables by carefully configuring the connections between neurons through dendritic branches. This model predicts neural correlations as a byproduct of the computation, with correlation coefficients aligning with experimentally measured values in the literature. I also present a mathematical derivation of these coefficients, revealing their dependence on population size. To demonstrate the model's utility, I apply it to compute the prediction error between an observed object's speed and the predicted speed based on self-motion. Importantly, the framework generalizes to compute any functional relationship between population-encoded variables, serving as a building block for perception and behaviour, including tasks such as transforming an object's position into joint angles for grasping.
In recent times, deep learning has been widely used in agriculture fields to identify diseases in crops, weather prediction, and crop yield prediction. However, designing efficient deep learning models that are lightweight, cost-effective, and suitable for deployment on small devices remains a challenge. This paper addresses this gap by proposing a Convolutional Neural Network (CNN) architecture optimized using a Genetic Algorithm (GA) to automate the selection of critical hyperparameters, such as the number and size of filters, ensuring high performance with minimal computational overhead. In this work, we have built our own tea leaf disease dataset consisting of three different tea leaf diseases, two diseases caused by pests, and one due to pathogens (infectious organisms) and environmental conditions. The proposed genetic algorithm-based CNN achieved an accuracy rate of 97.6% on the tea leaf disease dataset. To further validate its robustness, the model was tested on two additional datasets, namely PlantVillage and Rice leaf disease dataset, achieving accuracies of 96.99% and 99%, respectively. Performances of the proposed model are also compared with several state-of-the-art deep learning models, and the results show that the proposed model outperforms several DL architectures with fewer parameters.
Parkinson's Disease (PD) is a progressive neurodegenerative disorder and the early diagnosis is crucial for managing symptoms and slowing disease progression. This paper proposes a framework named Federated Learning Enabled Waterwheel Shuffled Shepherd Optimization-based Efficient-Fuzzy Deep Maxout Network (FedL_WSSO based Eff-FDMNet) for PD detection and classification. In local training model, the input image from the database "Image and Data Archive (IDA)" is given for preprocessing that is performed using Gaussian filter. Consequently, image augmentation takes place and feature extraction is conducted. These processes are executed for every input image. Therefore, the collected outputs of images are used for PD detection using Shepard Convolutional Neural Network Fuzzy Zeiler and Fergus Net (ShCNN-Fuzzy-ZFNet). Then, PD classification is accomplished using Eff-FDMNet, which is trained using WSSO. At last, based on CAViaR, local updation and aggregation are changed in server. The developed method obtained highest accuracy as 0.927, mean average precision as 0.905, lowest false positive rate (FPR) as 0.082, loss as 0.073, Mean Squared Error (MSE) as 0.213, and Root Mean Squared Error (RMSE) as 0.461. The high accuracy and low error rates indicate that the potent framework can enhance patient outcomes by enabling more reliable and personalized diagnosis.
Non-Orthogonal Multiple Access (NOMA) is the successive multiple-access methodologies for modern communication devices. Energy Efficiency (EE) is suggested in the NOMA system. In dynamic network conditions, the consideration of NOMA shows high computational complexity that minimizes the EE to degrade the system performance. This research suggested EE for the Multi-Carrier NOMA (MC-NOMA) models by optimization algorithm. The main scope of this research tends to improve the EE by Hybrid of Sewing Training and Lemur Optimization for optimizing the system parameters. The improvement made in this developed HSTLO algorithm can provide significant impact on MC-NOMA system, which it renders better user capacity while effectively optimizing the system parameters. Moreover, the Dilated Dense Recurrent Neural Network (DDRNN) model is developed. Employing the improvement in the deep learning model for the MC-NOMA system could effectively manage and enhance the system performance. Considering the DDRNN model can leverage to provide better generalization outcomes in different network scenarios that ensures to provide fast and reliable solutions compared to existing methods. Addressing the energy consumption problems in this research study will be analysed to show the advancement in MC-NOMA system that help to enhance the system performance.
