50 citations · 242 across the 19 of their papers we have counts for
19 papers
The mechanism underlying successful deep learning
Yarden Tzach, Yuval Meir, Ofek Tevet +4
Deep architectures consist of tens or hundreds of convolutional layers (CLs) that terminate with a few fully connected (FC) layers and an output layer representing the possible lab…
Enhancing the accuracies by performing pooling decisions adjacent to the output layer
Yuval Meir, Yarden Tzach, Ronit D. Gross +3
Learning classification tasks of (2^nx2^n) inputs typically consist of \le n (2x2) max-pooling (MP) operators along the entire feedforward deep architecture. Here we show, using th…
Brain inspired neuronal silencing mechanism to enable reliable sequence identification
Shiri Hodassman, Yuval Meir, Karin Kisos +5
Real-time sequence identification is a core use-case of artificial neural networks (ANNs), ranging from recognizing temporal events to identifying verification codes. Existing meth…
Long anisotropic absolute refractory periods with rapid rise-times to reliable responsiveness
Shira Sardi, Roni Vardi, Yael Tugendhaft +3
Refractoriness is a fundamental property of excitable elements, such as neurons, indicating the probability for re-excitation in a given time-lag, and is typically linked to the ne…
Significant anisotropic neuronal refractory period plasticity
Roni Vardi, Yael Tugendhaft, Shira Sardi +1
Refractory periods are an unavoidable feature of excitable elements, resulting in necessary time-lags for re-excitation. Herein, we measure neuronal absolute refractory periods (AR…
Brain experiments imply adaptation mechanisms which outperform common AI learning algorithms
Shira Sardi, Roni Vardi, Yuval Meir +4
Attempting to imitate the brain functionalities, researchers have bridged between neuroscience and artificial intelligence for decades; however, experimental neuroscience has not d…