33 citations · 132 across the 12 of their papers we have counts for
12 papers
Power-law Scaling to Assist with Key Challenges in Artificial Intelligence
Yuval Meir, Shira Sardi, Shiri Hodassman +4
Power-law scaling, a central concept in critical phenomena, is found to be useful in deep learning, where optimized test errors on handwritten digit examples converge as a power-la…
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…
Embedding information in physically generated random bit sequences while maintaining certified randomness
Shira Sardi, Herut Uzan, Shiri Otmazgin +3
Ultrafast physical random bit generation at hundreds of Gb/s rates, with verified randomness, is a crucial ingredient in secure communication and have recently emerged using optics…
Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity
Roni Vardi, Amir Goldental, Shira Sardi +2
The increasing number of recording electrodes enhances the capability of capturing the network's cooperative activity, however, using too many monitors might alter the properties o…
Vitality of Neural Networks under Reoccurring Catastrophic Failures
Shira Sardi, Amir Goldental, Hamutal Amir +2
Catastrophic failures are complete and sudden collapses in the activity of large networks such as economics, electrical power grids and computer networks, which typically require a…
Fast Reversible Learning based on Neurons functioning as Anisotropic Multiplex Hubs
Roni Vardi, Amir Goldental, Anton Sheinin +2
Neural networks are composed of neurons and synapses, which are responsible for learning in a slow adaptive dynamical process. Here we experimentally show that neurons act like ind…
Oscillations in networks of networks stem from adaptive nodes with memory
Amir Goldental, Herut Uzan, Shira Sardi +1
We present an analytical framework that allows the quantitative study of statistical dynamic properties of networks with adaptive nodes that have memory and is used to examine the…
Mimicking Collective Firing Patterns of Hundreds of Connected Neurons using a Single-Neuron Experiment
Amir Goldental, Pinhas Sabo, Shira Sardi +2
The experimental study of neural networks requires simultaneous measurements of a massive number of neurons, while monitoring properties of the connectivity, synaptic strengths and…
Broadband Macroscopic Cortical Oscillations Emerge from Intrinsic Neuronal Response Failures
Amir Goldental, Roni Vardi, Shira Sardi +2
Broadband spontaneous macroscopic neural oscillations are rhythmic cortical firing which were extensively examined during the last century, however, their possible origination is s…
Neuronal Response Impedance Mechanism Implementing Cooperative Networks with Low Firing Rates and Microseconds Precision
Roni Vardi, Amir Goldental, Hagar Marmari +5
Realizations of low firing rates in neural networks usually require globally balanced distributions among excitatory and inhibitory links, while feasibility of temporal coding is l…