As the name suggests, a neural network is a collection of connected artificial neurons. Each artificial neuron is based on a simplified model of the neurons found in the human brain. The complexity of the...
we aimed to identify simple models that could both effectively reduce the biological space to a set of useful parameters for cell type classification and recreate spiking behavior for a diverse set of neurons for use in network models. In the adult cortex, the majority of communication...
restart. Such a framework enables us to classify cells to unseen Cell Ontology terms based on their distances to other seen terms on the Cell Ontology graph (Fig.1). OnClass is a Python-based open source package able to compute cell type similarities between the hierarchical structure of exist...
Convolutional neural network (CNN) A neural network is a computational system that simu- lates neurons of the brain. Every neural network has in- put, hidden, and output layers. Each layer has a structure in which multiple nodes are connected by edges. A "deep neural network" is defined ...
RMS disease severity is associated with whole-blood methylation at genes related to neuronal structure and function. Moreover, correlated whole-blood methylation patterns can assign disease severity in females with RMS more accurately than clinical data available at diagnosis. ...
although invasive electrodes are sometimes used in specific applications. EEG measures voltage fluctuations resulting from ionic current within the neurons of the brain. In clinical contexts, EEG refers to the recording of the brain's spontaneous electrical activity over a period of time, as recorded...
We performed an extended analysis by including a structure-based investigation on one of our proposed predictions. We provide here a brief description of the structure-based methods were employed in this study and discuss the purpose and rationale of the investigation in the later section of the ...
[14] and froze the first few layers of the network so that their weights were not updated during backpropagation. We can freeze the pre-set weights for neurons of the top (first) few layers that recognize lines, edges and simple geometric shapes. These visual features are not domain-...
[5]. It learns by simulating the neurons in the human brain to process substantial volumes of data [6]. There have been extensive developments in deep learning in recent years with applications such as computer vision, natural language processing and speech recognition [7]. DL has emerged from...
where many neurons in the network end up outputting a constant zero value and therefore stop learning [31]. Moreover, the Leaky ReLU activation function offers a notable advantage over ReLU by providing a non-zero for negative input values. This characteristic enables Leaky ReLU to mitigate the...