Thomas Nowotny


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Person Info

NAME

Thomas

SURNAME

Nowotny

Publications in SciGraph latest 50 shown

  • 2019-12 Correction to: Computing reward prediction errors and learning valence in the insect mushroom body in BMC NEUROSCIENCE
  • 2017-10-24 A Biophysical Model of the Early Olfactory System of Honeybees in NEURAL INFORMATION PROCESSING
  • 2017-08 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 in BMC NEUROSCIENCE
  • 2017 Dynamics of Odor-Evoked Activity Patterns in the Olfactory System in ADVANCES IN DYNAMICS, PATTERNS, COGNITION
  • 2016-05 GeNN: a code generation framework for accelerated brain simulations in SCIENTIFIC REPORTS
  • 2016 Input-Modulation as an Alternative to Conventional Learning Strategies in ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING – ICANN 2016
  • 2015-12 Estimating numerical error in neural network simulations on Graphics Processing Units in BMC NEUROSCIENCE
  • 2015-12 More flexibility for code generation with GeNN v2.1 in BMC NEUROSCIENCE
  • 2015-12 Spiking neural network model of reinforcement learning in the honeybee implemented on the GPU in BMC NEUROSCIENCE
  • 2015 Dynamic Clamp in ENCYCLOPEDIA OF NANOTECHNOLOGY
  • 2015 Dynamic Clamp Technique in ENCYCLOPEDIA OF COMPUTATIONAL NEUROSCIENCE
  • 2015 Gap Junctions in Small Networks in ENCYCLOPEDIA OF COMPUTATIONAL NEUROSCIENCE
  • 2015 Voltage Clamp Technique in ENCYCLOPEDIA OF COMPUTATIONAL NEUROSCIENCE
  • 2015 Patch Clamp Technique in ENCYCLOPEDIA OF COMPUTATIONAL NEUROSCIENCE
  • 2014-07 SpineML and Brian 2.0 interfaces for using GPU enhanced Neuronal Networks (GeNN) in BMC NEUROSCIENCE
  • 2014-07 Simulating spiking neural networks on massively parallel graphical processing units using a code generation approach with GeNN in BMC NEUROSCIENCE
  • 2014-07 Classifying chemical sensor data using GPU-accelerated bio-mimetic neuronal networks based on the insect olfactory system in BMC NEUROSCIENCE
  • 2014-04 A modelling framework for the olfactory system of the honeybee using GeNN (GPU enhanced Neuronal Network simulation environment) in FLAVOUR
  • 2014-04 Stimulus-onset asynchrony can aid odor segregation in FLAVOUR
  • 2014-04 Feature selection in Enose applications in FLAVOUR
  • 2014 Dynamic Clamp Technique in ENCYCLOPEDIA OF COMPUTATIONAL NEUROSCIENCE
  • 2014 Voltage-Clamp Technique in ENCYCLOPEDIA OF COMPUTATIONAL NEUROSCIENCE
  • 2014 Gap Junctions in Small Networks in ENCYCLOPEDIA OF COMPUTATIONAL NEUROSCIENCE
  • 2014 Patch Clamp Technique in ENCYCLOPEDIA OF COMPUTATIONAL NEUROSCIENCE
  • 2013-07 A numerical renormalisation group method for the analysis of critical spreading activity in spiking neural networks in BMC NEUROSCIENCE
  • 2013-07 Data-driven honeybee antennal lobe model demonstrates how stimulus-onset asynchrony can aid odor segregation in BMC NEUROSCIENCE
  • 2013 The Green Brain Project – Developing a Neuromimetic Robotic Honeybee in BIOMIMETIC AND BIOHYBRID SYSTEMS
  • 2012-07 Benchmarking Drosophilareceptor neurons for technical applications in BMC NEUROSCIENCE
  • 2012 Dynamic Clamp in ENCYCLOPEDIA OF NANOTECHNOLOGY
  • 2011-12 Transient dynamics between displaced fixed points: an alternate nonlinear dynamical framework for olfaction in BMC NEUROSCIENCE
  • 2011-12 Dynamic observer: ion channel measurement beyond voltage clamp in BMC NEUROSCIENCE
  • 2011-12 Criteria for robustness of heteroclinic cycles in neural microcircuits in THE JOURNAL OF MATHEMATICAL NEUROSCIENCE
  • 2011-12 The effect of intrinsic subthreshold oscillations on the spontaneous dynamics of a ring network with distance-dependent delays in BMC NEUROSCIENCE
  • 2011-12 Coarse-grained statistics for attributing criticality to heterogeneous neural networks in BMC NEUROSCIENCE
  • 2011-12 Flexible neuronal network simulation framework using code generation for NVidia® CUDA™ in BMC NEUROSCIENCE
  • 2011-03 Dynamic clamp with StdpC software in NATURE PROTOCOLS
  • 2009-09 Moving beyond convergence in the pheromone system of the moth in BMC NEUROSCIENCE
  • 2009-09 Divergence alone cannot guarantee stable sparse activity patterns if connections are dense in BMC NEUROSCIENCE
  • 2009 “Sloppy Engineering” and the Olfactory System of Insects in BIOLOGICALLY INSPIRED SIGNAL PROCESSING FOR CHEMICAL SENSING
  • 2005-12 Self-organization in the olfactory system: one shot odor recognition in insects in BIOLOGICAL CYBERNETICS
  • 2003-12 Explaining synchrony in feed-forward networks: in BIOLOGICAL CYBERNETICS
  • 2003-10 Explaining synchrony in feed-forward networks: in BIOLOGICAL CYBERNETICS
  • 2003-09 Decoding Temporal Information Through Slow Lateral Excitation in the Olfactory System of Insects in JOURNAL OF COMPUTATIONAL NEUROSCIENCE
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