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Anthony N. Burkitt
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2020 – today
- 2024
- [j59]Parvin Zarei Eskikand
, Artemio Soto-Breceda
Neural dynamics and seizure correlations: Insights from neural mass models in a Tetanus Toxin rat model of epilepsy. Neural Networks 180: 106746 (2024) - [c33]Jonas F. Haderlein, Andre D. H. Peterson, Parvin Zarei Eskikand, Anthony N. Burkitt, Iven M. Y. Mareels, David B. Grayden:
On the benefit of overparameterisation in state reconstruction: An empirical study of the nonlinear case. ANZCC 2024: 49-54 - [c32]Jonas F. Haderlein, Andre D. H. Peterson, Parvin Zarei Eskikand, Mark J. Cook, Anthony N. Burkitt, David B. Grayden, Iven M. Y. Mareels:
Revisiting Seizure Prediction with Path Signatures: the Limitations of System Identification. ANZCC 2024: 192-200 - [c31]Suleman Rasheed, James D. Bennett, Peter E. Yoo, Nicholas L. Opie, Anthony N. Burkitt, David B. Grayden:
Comparing cardiac artifact removal algorithms for endovascular brain-computer interface recordings. BCI 2024: 1-6 - 2023
- [j58]Syeda R. Zehra
Evaluation of Optimal Stimuli for SSVEP-Based Augmented Reality Brain-Computer Interfaces. IEEE Access 11: 87305-87315 (2023) - [j57]Tianlin Ying, Anthony N. Burkitt
Combining the neural mass model and Hodgkin-Huxley formalism: Neuronal dynamics modelling. Biomed. Signal Process. Control. 79(Part): 104026 (2023) - [j56]Parvin Zarei Eskikand
Inhibitory stabilized network behaviour in a balanced neural mass model of a cortical column. Neural Networks 166: 296-312 (2023) - [j55]Charlie M. Sexton
Spike-timing dependent plasticity partially compensates for neural delays in a multi-layered network of motion-sensitive neurons. PLoS Comput. Biol. 19(9) (2023) - [c30]Jonas F. Haderlein, Andre D. H. Peterson, Anthony N. Burkitt, Iven M. Y. Mareels, David B. Grayden:
Autoregressive models for biomedical signal processing. EMBC 2023: 1-6 - [c29]Syeda R. Zehra, Jing Mu
Effect of alpha range activity on SSVEP decoding in brain-computer interfaces. EMBC 2023: 1-4 - [c28]Andi Partovi, Anthony N. Burkitt, David B. Grayden:
A Self-Supervised Task-Agnostic Embedding for EEG Signals. NER 2023: 1-4 - [i2]Jonas F. Haderlein, Andre D. H. Peterson, Anthony N. Burkitt, Iven M. Y. Mareels, David B. Grayden:
Autoregressive models for biomedical signal processing. CoRR abs/2304.11070 (2023) - [i1]Jonas F. Haderlein, Andre D. H. Peterson, Parvin Zarei Eskikand, Mark J. Cook, Anthony N. Burkitt, Iven M. Y. Mareels, David B. Grayden:
Path Signatures for Seizure Forecasting. CoRR abs/2308.09312 (2023) - 2022
- [j54]Catherine E. Davey, David B. Grayden, Anthony N. Burkitt:
Emergence of radial orientation selectivity: Effect of cell density changes and eccentricity in a layered network. Frontiers Comput. Neurosci. 16 (2022) - 2021
- [j53]Catherine E. Davey
Impact of axonal delay on structure development in a multi-layered network. Neural Networks 144: 737-754 (2021) - [j52]Yanbo Lian
Learning receptive field properties of complex cells in V1. PLoS Comput. Biol. 17(3) (2021) - [c27]James D. Bennett, Sam E. John, David B. Grayden, Anthony N. Burkitt:
Universal neurophysiological interpretation of EEG brain-computer interfaces. BCI 2021: 1-6 - [c26]Jonas F. Haderlein, Iven M. Y. Mareels, Andre D. H. Peterson
On the benefit of overparameterization in state reconstruction. CDC 2021: 1580-1585 - 2020
- [c25]Andi Partovi, Farhad Goodarzy, Ewan S. Nurse, Philippa J. Karoly
A Convolutional Neural Network Model for Decoding EEG signals in a Hand-Squeeze Task1. BCI 2020: 1-6
2010 – 2019
- 2019
- [j51]Jordan D. Chambers, Diego Elgueda
Computational Neural Modeling of Auditory Cortical Receptive Fields. Frontiers Comput. Neurosci. 13: 28 (2019) - 2018
- [j50]Martin J. Spencer
Compensation for Traveling Wave Delay Through Selection of Dendritic Delays Using Spike-Timing-Dependent Plasticity in a Model of the Auditory Brainstem. Frontiers Comput. Neurosci. 12: 36 (2018) - [j49]Hinze Hogendoorn
Predictive coding of visual object position ahead of moving objects revealed by time-resolved EEG decoding. NeuroImage 171: 55-61 (2018) - [j48]Parvin Zarei Eskikand
A biologically-based computational model of visual cortex that overcomes the X-junction illusion. Neural Networks 102: 10-20 (2018) - [j47]Matias I. Maturana
Electrical receptive fields of retinal ganglion cells: Influence of presynaptic neurons. PLoS Comput. Biol. 14(2) (2018) - [c24]Tatiana Kameneva, Hamish Meffin
Bistability in Hodgkin-Huxley-type equations. EMBC 2018: 4728-4731 - 2017
- [j46]Elma O'Sullivan-Greene, Levin Kuhlmann
Probing to Observe Neural Dynamics Investigated with Networked Kuramoto Oscillators. Int. J. Neural Syst. 27(1): 1650038:1-1650038:13 (2017) - [p1]Thomas J. Oxley, Nicholas L. Opie, Sam E. John, Gil S. Rind, Stephen M. Ronayne, Anthony N. Burkitt, David B. Grayden, Clive N. May, Terence J. O'Brien:
A Minimally Invasive Endovascular Stent-Electrode Array for Chronic Recordings of Cortical Neural Activity. Brain-Computer Interface Research (6) 2017: 55-63 - 2016
- [j45]Nafise Erfanian Saeedi, Peter J. Blamey
Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks. PLoS Comput. Biol. 12(4) (2016) - [c23]Nicholas L. Opie
Feasibility of a chronic, minimally invasive endovascular neural interface. EMBC 2016: 4455-4458 - [c22]Timothy Esler, Anthony N. Burkitt, David B. Grayden, Robert R. Kerr, Bahman Tahayori, Hamish Meffin
A computational model of orientation-dependent activation of retinal ganglion cells. EMBC 2016: 5447-5450 - 2015
- [j44]Tatiana Kameneva, Miganoosh Abramian, Daniele Zarelli, Dragan Nesic
Spike history neural response model. J. Comput. Neurosci. 38(3): 463-481 (2015) - 2014
- [j43]Elma O'Sullivan-Greene
Observability limits for networked oscillators. Autom. 50(4): 1087-1099 (2014) - [j42]Tatiana Kameneva, Daniele Zarelli, Dragan Nesic
A comparison of open-loop and closed-loop stimulation strategies to control excitation of retinal ganglion cells. Biomed. Signal Process. Control. 14: 164-174 (2014) - [j41]Matias I. Maturana
The effect of morphology upon electrophysiological responses of retinal ganglion cells: simulation results. J. Comput. Neurosci. 36(2): 157-175 (2014) - [j40]Fabiano Baroni
Interplay of Intrinsic and Synaptic Conductances in the Generation of High-Frequency Oscillations in Interneuronal Networks with Irregular Spiking. PLoS Comput. Biol. 10(5) (2014) - [c21]Tatiana Kameneva, David B. Grayden, Hamish Meffin
Feedback stimulation strategy: Control of retinal ganglion cells activation. EMBC 2014: 1703-1706 - 2013
- [j39]Robert R. Kerr, Anthony N. Burkitt, Doreen A. Thomas
Delay Selection by Spike-Timing-Dependent Plasticity in Recurrent Networks of Spiking Neurons Receiving Oscillatory Inputs. PLoS Comput. Biol. 9(2) (2013) - [c20]Matias I. Maturana
Multicompartment retinal ganglion cells response to high frequency bi-phasic pulse train stimulation: Simulation results. EMBC 2013: 69-72 - [c19]Matias I. Maturana
Retinal ganglion cells electrophysiology: The effect of cell morphology on impulse waveform. EMBC 2013: 2583-2586 - [c18]Filiz Isabell Kiral-Kornek, Craig O. Savage, Elma O'Sullivan-Greene
Embracing the irregular: A patient-specific image processing strategy for visual prostheses. EMBC 2013: 3563-3566 - [c17]Hamish Meffin
Internal inconsistencies in models of electrical stimulation in neural tissue. EMBC 2013: 5946-5949 - [c16]Nicholas V. Apollo, David B. Grayden, Anthony N. Burkitt, Hamish Meffin
Modeling intrinsic electrophysiology of AII amacrine cells: Preliminary results. EMBC 2013: 6551-6554 - 2012
- [j38]Martin J. Spencer
An investigation of dendritic delay in octopus cells of the mammalian cochlear nucleus. Frontiers Comput. Neurosci. 6: 83 (2012) - [j37]Matthieu Gilson
Frequency Selectivity Emerging from Spike-Timing-Dependent Plasticity. Neural Comput. 24(9): 2251-2279 (2012) - [j36]Matthieu Gilson
Spectral Analysis of Input Spike Trains by Spike-Timing-Dependent Plasticity. PLoS Comput. Biol. 8(7) (2012) - [j35]Nicholas L. Opie
Heating of the Eye by a Retinal Prosthesis: Modeling, Cadaver and In Vivo Study. IEEE Trans. Biomed. Eng. 59(2): 339-345 (2012) - [c15]Alexey S. Moroz, Mark D. McDonnell
Information theoretic inference of the optimal number of electrodes for future cochlear implants using a spiral cochlea model. EMBC 2012: 2965-2968 - [c14]Nicholas A. Venables, Bahman Tahayori
Determining the electrical impedance of the retina from a complex voltage map. EMBC 2012: 3005-3008 - [c13]Craig O. Savage, Tatiana Kameneva, David B. Grayden, Hamish Meffin
Minimisation of required charge for desired neuronal spike rate. EMBC 2012: 3009-3012 - [c12]Filiz Isabell Kiral-Kornek, Craig O. Savage, David B. Grayden, Anthony N. Burkitt:
Feature accentuation in phosphenated images. EMBC 2012: 5915-5918 - 2011
- [j34]Timothy S. Nelson, Courtney L. Suhr, Dean R. Freestone
Closed-Loop Seizure Control with Very High Frequency Electrical Stimulation at Seizure Onset in the Gaers Model of Absence Epilepsy. Int. J. Neural Syst. 21(2): 163-173 (2011) - [j33]Tatiana Kameneva, Hamish Meffin
Modelling intrinsic electrophysiological properties of ON and OFF retinal ganglion cells. J. Comput. Neurosci. 31(3): 547-561 (2011) - [j32]Sean Byrnes, Anthony N. Burkitt, David B. Grayden, Hamish Meffin
Learning a Sparse Code for Temporal Sequences Using STDP and Sequence Compression. Neural Comput. 23(10): 2567-2598 (2011) - [c11]Dean R. Freestone
Probing for cortical excitability. EMBC 2011: 1644-1647 - [c10]Craig O. Savage, David B. Grayden, Hamish Meffin
Predicting phosphene elicitation in patients with retinal implants: A mathematical study. EMBC 2011: 6246-6249 - [c9]Bahman Tahayori
Theoretical framework for estimating the conductivity map of the retina through finite element analysis. EMBC 2011: 6721-6724 - [c8]Tatiana Kameneva, David B. Grayden, Hamish Meffin
Simulating electrical stimulation of degenerative retinal ganglion cells with bi-phasic pulse trains. EMBC 2011: 7103-7106 - 2010
- [j31]Matthieu Gilson
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks V: self-organization schemes and weight dependence. Biol. Cybern. 103(5): 365-386 (2010) - [j30]Matthieu Gilson
STDP in Recurrent Neuronal Networks. Frontiers Comput. Neurosci. 4: 23 (2010) - [j29]Sean Byrnes, Anthony N. Burkitt, David B. Grayden, Hamish Meffin
Spiking Neuron Model for Temporal Sequence Recognition. Neural Comput. 22(1): 61-93 (2010) - [j28]Daniel A. Taft, David B. Grayden, Anthony N. Burkitt:
Across-Frequency Delays Based on the Cochlear Traveling Wave: Enhanced Speech Presentation for Cochlear Implants. IEEE Trans. Biomed. Eng. 57(3): 596-606 (2010) - [j27]Mark D. McDonnell
A channel model for inferring the optimal number of electrodes for future cochlear implants. IEEE Trans. Inf. Theory 56(2): 928-940 (2010)
2000 – 2009
- 2009
- [j26]Matthieu Gilson
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity-strengthening correlated input pathways. Biol. Cybern. 101(2): 81-102 (2009) - [j25]Matthieu Gilson
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. II. Input selectivity - symmetry breaking. Biol. Cybern. 101(2): 103-114 (2009) - [j24]Matthieu Gilson
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks III: Partially connected neurons driven by spontaneous activity. Biol. Cybern. 101(5-6): 411-426 (2009) - [j23]Matthieu Gilson
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV. Biol. Cybern. 101(5-6): 427-444 (2009) - [j22]Daniel A. Taft, David B. Grayden, Anthony N. Burkitt:
Speech coding with traveling wave delays: Desynchronizing cochlear implant frequency bands with cochlea-like group delays. Speech Commun. 51(11): 1114-1123 (2009) - [c7]Elma O'Sullivan-Greene
Observability issues in networked clocks with applications to epilepsy. CDC 2009: 3527-3532 - 2008
- [c6]Daniel A. Taft, David B. Grayden, Anthony N. Burkitt:
Traveling wave based group delays for cochlear implant speech processing. INTERSPEECH 2008: 2207 - 2007
- [j21]Anthony N. Burkitt, Matthieu Gilson
Spike-timing-dependent plasticity for neurons with recurrent connections. Biol. Cybern. 96(5): 533-546 (2007) - [c5]Matthieu Gilson, David B. Grayden, J. Leo van Hemmen, Doreen A. Thomas
Spike-Timing Dependent Plasticity in Recurrently Connected Networks with Fixed External Inputs. ICONIP (1) 2007: 102-111 - 2006
- [j20]Anthony N. Burkitt:
A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input. Biol. Cybern. 95(1): 1-19 (2006) - [j19]Anthony N. Burkitt:
A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties. Biol. Cybern. 95(2): 97-112 (2006) - 2005
- [j18]Hamish Meffin
Dynamically adjustable contrast enhancement from cortical background activity. Neurocomputing 65-66: 633-639 (2005) - 2004
- [j17]Hamish Meffin
An Analytical Model for the 'Large, Fluctuating Synaptic Conductance State' Typical of Neocortical Neurons In Vivo. J. Comput. Neurosci. 16(2): 159-175 (2004) - [j16]Anthony N. Burkitt, Hamish Meffin
Spike-Timing-Dependent Plasticity: The Relationship to Rate-Based Learning for Models with Weight Dynamics Determined by a Stable Fixed Point. Neural Comput. 16(5): 885-94 (2004) - 2003
- [j15]Anthony N. Burkitt, Hamish Meffin
Study of neuronal gain in a conductance-based leaky integrate-and-fire neuron model with balanced excitatory and inhibitory synaptic input. Biol. Cybern. 89(2): 119-125 (2003) - [j14]Anthony N. Burkitt, J. Leo van Hemmen:
How synapses in the auditory system wax and wane: theoretical perspectives. Biol. Cybern. 89(5): 318-332 (2003) - [j13]Anthony N. Burkitt, Hamish Meffin, David B. Grayden:
Gain modulation and balanced synaptic input in a conductance-based neural model. Neurocomputing 52-54: 221-226 (2003) - 2002
- [j12]Anthony N. Burkitt:
An information-theoretic analysis of the coding of a periodic synaptic input by integrate-and-fire neurons. Neurocomputing 44-46: 69-74 (2002) - [j11]Levin Kuhlmann
Summation of Spatiotemporal Input Patterns in Leaky Integrate-and-Fire Neurons: Application to Neurons in the Cochlear Nucleus Receiving Converging Auditory Nerve Fiber Input. J. Comput. Neurosci. 12(1): 55-73 (2002) - 2001
- [j10]Anthony N. Burkitt:
Balanced neurons: analysis of leaky integrate-and-fire neurons with reversal potentials. Biol. Cybern. 85(4): 247-255 (2001) - [j9]Anthony N. Burkitt, Graeme M. Clark:
Synchronization of the Neural Response to Noisy Periodic Synaptic Input. Neural Comput. 13(12): 2639-2672 (2001) - 2000
- [j8]Anthony N. Burkitt, Graeme M. Clark:
Analysis of synchronization in the response of neurons to noisy periodic synaptic input. Neurocomputing 32-33: 67-75 (2000) - [j7]Anthony N. Burkitt:
Interspike interval variability for balanced networks with reversal potentials for large numbers of inputs. Neurocomputing 32-33: 313-321 (2000) - [j6]Anthony N. Burkitt, Graeme M. Clark:
Calculation of Interspike Intervals for Integrate-and-Fire Neurons with Poisson Distribution of Synaptic Inputs. Neural Comput. 12(8): 1789-1820 (2000)
1990 – 1999
- 1999
- [j5]Anthony N. Burkitt, Graeme M. Clark:
New technique for analyzing integrate and fire neurons. Neurocomputing 26-27: 93-99 (1999) - [j4]Anthony N. Burkitt, Graeme M. Clark:
Analysis of Integrate-and-Fire Neurons: Synchronization of Synaptic Input and Spike Output. Neural Comput. 11(4): 871-901 (1999) - [c4]Anthony N. Burkitt, Graeme M. Clark:
Synchronization of the neural response to noisy periodic synaptic input. IJCNN 1999: 268-273 - [c3]Anthony N. Burkitt:
Analysis of neural response for excitation-inhibition balanced networks with reversal potentials for large numbers of inputs. IJCNN 1999: 305-308 - 1995
- [c2]Anthony N. Burkitt:
Study of analogue neural networks that obey Dale's law using mean-field theory. ICNN 1995: 2583-2586 - 1993
- [c1]Anthony N. Burkitt:
Optimised Attractor Neural Networks with External Inputs. IWANN 1993: 167-172 - 1992
- [j3]Anthony N. Burkitt, Peer Ueberholz:
Refined Pruning Techniques for Feed-forward Neural Networks. Complex Syst. 6(6) (1992) - 1991
- [b1]Dieter W. Heermann, Anthony N. Burkitt:
Parallel algorithms in computational science. Springer series in information sciences 24, Springer 1991, ISBN 0387534180, pp. I-XIII, 1-183 - [j2]Anthony N. Burkitt:
Optimization of the Architecture of Feed-forward Neural Networks with Hidden Layers by Unit Elimination. Complex Syst. 5(4) (1991) - 1990
- [j1]D. W. Heermann, Anthony N. Burkitt:
Parallelization of the Ising model and its performance evaluation. Parallel Comput. 13(3): 345-357 (1990)
Coauthor Index
[j59] [c33] [c32] [c31] [j58] [j56] [c30] [c29] [c28] [i2] [i1] [j54] [j53] [j52] [c27] [c26] [c25] [j51] [j50] [j48] [j47] [c24] [j46] [p1] [j45] [c23] [c22] [j44] [j42] [j41] [j40] [c21] [j39] [c20] [c19] [c18] [c17] [c16] [j38] [j35] [c15] [c14] [c13] [c12] [j32] [c11] [c10] [c9] [c8] [j31] [j29] [j28] [j27] [j26] [j25] [j24] [j23] [j22] [c6] [c5] [j18] [j17] [j16] [j15] [j13]
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last updated on 2024-12-10 21:41 CET by the dblp team
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