Authorship

... neural coding models that depend on phase, increasing the informational content of neural representations; and (4) likely originate from the dynamics of ... Sep 11, 2020 ... Alternatively, it is. 10. Page 29. 2.1. Preliminaries on convolutional neural networks often found that adding depth to the network by stacking ... The informational aspects of neural networks performing associative memory following Hebbian learning are analysed in detail. The recall process is decomposed ... We present a review on the recent advances and emerging opportunities around the theme of analyzing deep neural networks (DNNs) with information-theoretic ... ... neural representation of familiar faces and the degree to which peri-frontal neural networks contribute to familiar face recognition. He also gives an example using a neural network class with fixed architecture as the hypothesis class. He is able to predict the qualitative effects of ... Classically, external receptors of the body transmit information from the environment to the cerebral cortex via the thalamus. This review explains and ... Jun 29, 2022 ... Neural Networks are one particular type of Machine Learning technique. They are a type of artificial intelligence modeled on the brain. Abstract. The concept of Learning by Stimulus Avoidance (LSA) has been proposed in recent literature, and the methods of avoiding stimuli: action, prediction, ... in the upper network level. Moreover, in a slightly different direction, research on multi-task learning neural networks, an approach aiming at internal ...