What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Artificial intelligence
- Statistics
Artificial intelligence, Spike, Biological system, Artificial neural network and Neuroscience are his primary areas of study.
His Spike research includes elements of Algorithm, Linear system, Complex system and Synchronization.
His studies deal with areas such as Stochastic process and Neuron morphology as well as Biological system.
The concepts of his Artificial neural network study are interwoven with issues in Computational neuroscience, Distributed computing and Feed forward.
His research in Neuroscience intersects with topics in Synaptic weight and Spike-timing-dependent plasticity.
He works mostly in the field of Synfire chain, limiting it down to topics relating to Millisecond and, in certain cases, Spiking neural network, as a part of the same area of interest.
His most cited work include:
- Spike Synchronization and Rate Modulation Differentially Involved in Motor Cortical Function (814 citations)
- Stable propagation of synchronous spiking in cortical neural networks (804 citations)
- Stable propagation of synchronous spiking in cortical neural networks (804 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Neuroscience, Spike, Statistical physics and Biological system.
His Artificial intelligence study integrates concerns from other disciplines, such as Feed forward, Machine learning and Pattern recognition.
His research integrates issues of Surrogate data, Algorithm and Synchronization in his study of Spike.
His work deals with themes such as Biological neuron model and White noise, which intersect with Statistical physics.
His biological study spans a wide range of topics, including Local field potential and Spiking neural network.
He interconnects Computational neuroscience, Network model and Decorrelation in the investigation of issues within Artificial neural network.
He most often published in these fields:
- Artificial intelligence (28.33%)
- Neuroscience (24.08%)
- Spike (14.45%)
What were the highlights of his more recent work (between 2015-2021)?
- Network model (15.58%)
- Computational neuroscience (11.33%)
- Artificial neural network (14.16%)
In recent papers he was focusing on the following fields of study:
Markus Diesmann mainly investigates Network model, Computational neuroscience, Artificial neural network, Neuroscience and Spiking neural network.
His Network model research includes themes of Randomness, Electrophysiology, Reference implementation, Algorithm and Machine learning.
The study incorporates disciplines such as Supercomputer, Biological system and Library science in addition to Computational neuroscience.
The various areas that Markus Diesmann examines in his Artificial neural network study include Theoretical computer science, Structure, Statistical physics, Nonlinear system and Event.
In general Neuroscience, his work in Cortex, Macaque and Cortical network is often linked to MEDLINE linking many areas of study.
Markus Diesmann has included themes like Decorrelation and Computer architecture in his Spiking neural network study.
Between 2015 and 2021, his most popular works were:
- Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model. (59 citations)
- Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers. (53 citations)
- Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks. (49 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Artificial intelligence
- Statistics
His primary scientific interests are in Network model, Local field potential, Visual cortex, Artificial intelligence and Artificial neural network.
His Visual cortex study introduces a deeper knowledge of Neuroscience.
His research in Artificial intelligence intersects with topics in Machine learning and Spike, Spike train.
His Artificial neural network research includes elements of Network simulation and Reference implementation.
Electrophysiology is frequently linked to Biological system in his study.
His research in Biological system tackles topics such as Network dynamics which are related to areas like Computational neuroscience.
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