Effects of Transcranial Magneto-acoustic Electrical Stimulation on Discharge propagation in feed-forward neural network

The mammalian brain has an extremely complex, diversified and highly modular structure, and information dissemination in the modular brain network affects various brain diseases. Although a variety of neuromodulation techniques have been used to study the discharge characteristics of neural networks, the effects of transcranial magneto-acoustic electrical stimulation(TMAES) have rarely been mentioned. Based on the excitatory and inhibitory Izhikevich neuron model, we constructs a feed-forward neural network connected by electrical synapses and chemical synapses, and analyzes the firing frequency of the neural network under TMAES and magnetic stimulation and the differences in each layer types of firing patterns of neurons. The results showed that the discharge patterns of neurons in each layer were different, the discharge frequency of inhibitory neurons was higher than that of excited neurons, and the stimulation signal could be transmitted to the whole network layer.The maximum discharge frequency of neural network connected by electrical coupling can reach 0.94kHz, and the discharge frequency of neural network connected by chemical coupling is less than 0.5 kHz.With the increase of coupling degree, the discharge frequency of neurons in each network layer under TMAES is much greater than that under magnetic stimulation.When the induced current is less than 26.5μA/cm 2 , magnetic stimulation can promote the inhibitory neurons, and TMAES has a variety of regulatory effects on the inhibitory neurons in the neural network. The results show that TMAES has better regulation effect than magnetic stimulation, and the regulation effect is affected by neural network structure and stimulation parameters.

Multi-cancer classification; an analysis of neural network complexity

Background: Cancer identification is generally framed as binary classification, normally discrimination of a control group from a single cancer group. However, such models lack any cancer-specific information, as they are only trained on one cancer type. The models fail to account for competing cancer risks. For example, an ostensibly healthy individual may have any number of different cancer types, and a tumor may originate from one of several primary sites. Pan-cancer evaluation requires a model trained on multiple cancer types, and controls, simultaneously, so that a physician can be directed to the correct area of the body for further testing. Methods: We introduce novel neural network models to address multi-cancer classification problems across several data types commonly applied in cancer prediction, including circulating miRNA expression, protein, and mRNA. In particular, we present an analysis of neural network depth and complexity, and investigate how this relates to classification performance. Comparisons of our models with state-of-the-art neural networks from the literature are also presented. Results: Our analysis evidences that shallow, feed-forward neural net architectures offer greater performance when compared to more complex deep feed-forward, Convolutional Neural Network (CNN), and Graph CNN (GCNN) architectures considered in the literature. Conclusion: The results show that multiple cancers and controls can be classified accurately using the proposed models, across a range of expression technologies in cancer prediction. Impact: This study addresses the important problem of pan-cancer classification, which is often overlooked in the literature. The promising results highlight the urgency for further research.

Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Sodium-Ion Battery

Estimating the accurate State of Charge (SOC) of a battery is important to avoid the over/undercharging and protect the battery pack from low cycle life. Current methods of SOC estimation use complex equations in the Extended Kalman Filter (EKF) and the equivalent circuit model. In this paper, we used a Feed Forward Neural Network (FNN) to estimate the SOC value accurately where battery parameters such as current, voltage, and charge are mapped directly to the SOC value at the output. A FNN could self-learn the weights with each training data point and update the model parameters such as weights and bias using a combination of two gradient descents (Adam). This model comprises the Dropout technique, which can have many neural network architectures by dropping the neuron/mode at each epoch/training cycle using the same weights and biases. Our FNN model was trained with data comprising different current rates and tested for different cycling data, for example, 5th, 10th, 20th, and 50th cycles and at a different cutoff voltage (4.5 V). The battery used for estimating the SOC value was a Na-ion based battery, which is highly non-linear, and it was fabricated in a house using Na0.67Fe0.5Mn0.5O2 (NFM) as a cathode and Na metal as a reference electrode. The FNN successfully estimated the SOC value for the highly non-linear nature of the Na-ion battery at different current rates (0.05 C, 0.1 C, 0.5 C, 1 C, 2 C), for different cycling data, and at higher cut-off voltage of –4.5 V Na+, reaching the R2 value of ~0.97–~0.99, ~0.99, and ~0.98, respectively.

Prototyping Feed-Forward Artificial Neural Network on Spartan 3S1000 FPGA for Blood Type Classification

In this research, a Feed-Forward Artificial Neural Network design was implemented on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board and prototyped blood type classification device. This research uses blood sample images as a system input. The system was built using VHSIC Hardware Description Language to describe the feed-forward propagation with a backpropagation neural network algorithm. We use three layers for the feed-forward ANN design with two hidden layers. The hidden layer designed has two neurons. In this study, the accuracy of detection obtained for four-type blood image resolutions results from 86%-92%, respectively.