H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Neuroscience H-index 68 Citations 16,865 210 World Ranking 971 National Ranking 33

Overview

What is he best known for?

The fields of study he is best known for:

  • Neuroscience
  • Statistics
  • Quantum mechanics

His primary areas of study are Neuroscience, Stimulus, Neural coding, Sensory system and Artificial intelligence. His Neuroscience and Local field potential, Visual cortex, Visual perception, Perception and Sensory cortex investigations all form part of his Neuroscience research activities. His Stimulus research includes elements of Neurophysiology and Somatosensory system, Barrel cortex.

In his study, Spike is inextricably linked to Coding, which falls within the broad field of Neural coding. Phase coding, Rhythm, Natural sounds and Speech processing is closely connected to Auditory cortex in his research, which is encompassed under the umbrella topic of Sensory system. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Information theory, Spike train and Pattern recognition.

His most cited work include:

  • Extracting information from neuronal populations: information theory and decoding approaches. (554 citations)
  • Extracting information from neuronal populations: information theory and decoding approaches. (554 citations)
  • Modelling and analysis of local field potentials for studying the function of cortical circuits (495 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Neuroscience, Artificial intelligence, Stimulus, Sensory system and Neural coding. All of his Neuroscience and Local field potential, Somatosensory system, Visual cortex, Auditory cortex and Perception investigations are sub-components of the entire Neuroscience study. His work carried out in the field of Artificial intelligence brings together such families of science as Information theory, Machine learning, Spike and Pattern recognition.

The concepts of his Stimulus study are interwoven with issues in Neurophysiology, Spike train, Neuron, Barrel cortex and Decoding methods. His Sensory system study typically links adjacent topics like Neural decoding. His Neural coding study combines topics in areas such as Mutual information and Coding.

He most often published in these fields:

  • Neuroscience (50.66%)
  • Artificial intelligence (34.38%)
  • Stimulus (25.98%)

What were the highlights of his more recent work (between 2016-2021)?

  • Neuroscience (50.66%)
  • Sensory system (25.20%)
  • Artificial intelligence (34.38%)

In recent papers he was focusing on the following fields of study:

Stefano Panzeri mainly investigates Neuroscience, Sensory system, Artificial intelligence, Stimulus and Artificial neural network. Neuroscience is a component of his Perception, Prefrontal cortex, Local field potential, Locus coeruleus and Cortex studies. His biological study spans a wide range of topics, including Receptive field, Macaque, Neuron, Optogenetics and Visual cortex.

His work focuses on many connections between Artificial intelligence and other disciplines, such as Pattern recognition, that overlap with his field of interest in Spike train, Muscle activity and Matrix decomposition. His study looks at the intersection of Stimulus and topics like Neural coding with Mutual information and Neural decoding. His Artificial neural network study integrates concerns from other disciplines, such as Network dynamics, Bifurcation, Network model, Binary number and Multistability.

Between 2016 and 2021, his most popular works were:

  • Distinct timescales of population coding across cortex (173 citations)
  • The threshold for conscious report: Signal loss and response bias in visual and frontal cortex (137 citations)
  • Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior. (127 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Artificial intelligence
  • Neuroscience

The scientist’s investigation covers issues in Neuroscience, Sensory system, Artificial intelligence, Pattern recognition and Set. Stefano Panzeri regularly links together related areas like Information coding in his Neuroscience studies. He combines subjects such as Perception, Retina, Retinal ganglion, Biological neural network and Neural coding with his study of Sensory system.

Stefano Panzeri interconnects Stimulus, Sound localization, Sensory cortex, Machine learning and Posterior parietal cortex in the investigation of issues within Neural coding. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Domain and Spike train. His Pattern recognition research includes elements of Temporal muscle, Muscle activity, Motor control and Spinal cord.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Extracting information from neuronal populations: information theory and decoding approaches.

Rodrigo Quian Quiroga;Stefano Panzeri;Stefano Panzeri.
Nature Reviews Neuroscience (2009)

717 Citations

Modelling and analysis of local field potentials for studying the function of cortical circuits

Gaute T. Einevoll;Christoph Kayser;Nikos K. Logothetis;Stefano Panzeri.
Nature Reviews Neuroscience (2013)

569 Citations

Sensory neural codes using multiplexed temporal scales

Stefano Panzeri;Nicolas Brunel;Nicolas Brunel;Nikos K. Logothetis;Nikos K. Logothetis;Christoph Kayser.
Trends in Neurosciences (2010)

476 Citations

Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns.

Christoph Kayser;Marcelo A. Montemurro;Nikos K. Logothetis;Nikos K. Logothetis;Stefano Panzeri;Stefano Panzeri.
Neuron (2009)

467 Citations

The role of spike timing in the coding of stimulus location in rat somatosensory cortex.

Stefano Panzeri;Rasmus S. Petersen;Simon R. Schultz;Michael Lebedev.
Neuron (2001)

461 Citations

Analytical estimates of limited sampling biases in different information measures.

Stefano Panzeri;Alessandro Treves.
Network: Computation In Neural Systems (1996)

451 Citations

Correcting for the Sampling Bias Problem in Spike Train Information Measures

Stefano Panzeri;Riccardo Senatore;Marcelo A. Montemurro;Rasmus S. Petersen.
Journal of Neurophysiology (2007)

424 Citations

Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information.

Andrei Belitski;Arthur Gretton;Cesare Magri;Yusuke Murayama.
The Journal of Neuroscience (2008)

416 Citations

Speech rhythms and multiplexed oscillatory sensory coding in the human brain.

Joachim Gross;Nienke Hoogenboom;Gregor Thut;Philippe G. Schyns.
PLOS Biology (2013)

392 Citations

The upward bias in measures of information derived from limited data samples

Alessandro Treves;Stefano Panzeri.
Neural Computation (1995)

387 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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