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 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.
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.
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.
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Extracting information from neuronal populations: information theory and decoding approaches.
Rodrigo Quian Quiroga;Stefano Panzeri;Stefano Panzeri.
Nature Reviews Neuroscience (2009)
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)
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)
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.
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.
Analytical estimates of limited sampling biases in different information measures.
Stefano Panzeri;Alessandro Treves.
Network: Computation In Neural Systems (1996)
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)
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)
Speech rhythms and multiplexed oscillatory sensory coding in the human brain.
Joachim Gross;Nienke Hoogenboom;Gregor Thut;Philippe G. Schyns.
PLOS Biology (2013)
The upward bias in measures of information derived from limited data samples
Alessandro Treves;Stefano Panzeri.
Neural Computation (1995)
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