33 citations · 148 across the 13 of their papers we have counts for
13 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…
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…
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…
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…
Sudden synchrony leaps accompanied by frequency multiplications in neuronal activity
Roni Vardi, Amir Goldental, Shoshana Guberman +3
A classical view of neural coding relies on temporal firing synchrony among functional groups of neurons; however the underlying mechanism remains an enigma. Here we experimentally…
An experimental evidence-based computational paradigm for new logic-gates in neuronal activity
Roni Vardi, Shoshana Guberman, Amir Goldental +1
We propose a new experimentally corroborated paradigm in which the functionality of the brain's logic-gates depends on the history of their activity, e.g. an OR-gate that turns int…