The procedure for updating the weights is similar to that used for the weights of the connections between the output and hidden layers, with the difference being that there are no target values that can be used to calculate the error of each neuron. Instead, you can calculate th...
These computational attributes emerge as signatures for respective parts of the auditory pathway: the auditory periphery and subcortical structures are characterized by ascending feedforward synaptic connections for rapid forward-filtering of signals32, whereas the speech–auditory cortex has a multilayer ...
Intuitively, in this work, we replace the integration (that is, solution) of a nonlinear DE describing the interaction of a neuron with its input nonlinear synaptic connections, with their corresponding nonlinear operators. This could be achieved in principle using functional Taylor expansions (in th...
Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. ...
New synthesis and fabrication methods in recent decades have overcome some of these drawbacks and diamond has enjoyed a surge in interest as a biomedical material. In the field of neural interfaces a grand goal is permanent, high fidelity connections with neural populations. Diamond's longevity, ...
Models are obtained as a result of training the network. In the course of training, the network is represented in the form of input–output pairs related by a simulated transformation. A network trained using these examples is able to predict the output signals from input signals not originally...
neural network (DNN) is general and there are several specific variations, including recurrent neural networks (RNNs) and convolutional neural networks (CNNs). The most basic form of a DNN, which I explain in this article, doesn’t have a special name, so I’ll refer to it just as a ...
According to this theory, structured recurrent connections among N neurons cause the N-dimensional state vector to converge in time to a stable, low-dimensional space called the attractor [1]. Such a network embeds memories as stationary attractors, which may be a discrete set of point ...
However, as it learns to achieve these goals and to optimize its behavioural performance, its constituent neurons face the kind of resource constraints experienced within biological networks. Neurons must balance their finite resources to grow or prune connections, while the cost of a connection is ...
Recently, it was shown that flexible control of speed can be achieved through nonlinear interactions within a simple model consisting of a pair of units with reciprocal inhibitory connections7. In this model, the speed at which the output evolves toward a movement initiation threshold can be adjust...