Nicolangelo Iannella
Senior Research fellow, The University of Oslo, Faculty of Mathematics and Natural Sciences
Research Expertise
About
Publications
π − π scattering in a QCD-based model field
theory
Physical Review D / Jan 01, 1994
Roberts, C. D., Cahill, R. T., Sevior, M. E., & Iannella, N. (1994). <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>π</mml:mi><mml:mo>−</mml:mo><mml:mi>π</mml:mi></mml:math>scattering in a QCD-based model field theory. Physical Review D, 49(1), 125–137. https://doi.org/10.1103/physrevd.49.125
Spike-Based Synaptic Plasticity in Silicon: Design, Implementation, Application, and Challenges
Proceedings of the IEEE / May 01, 2014
Rahimi Azghadi, M., Iannella, N., Al-Sarawi, S. F., Indiveri, G., & Abbott, D. (2014). Spike-Based Synaptic Plasticity in Silicon: Design, Implementation, Application, and Challenges. Proceedings of the IEEE, 102(5), 717–737. https://doi.org/10.1109/jproc.2014.2314454
A spiking neural network architecture for nonlinear function approximation
Neural Networks / Jul 01, 2001
Iannella, N., & Back, A. D. (2001). A spiking neural network architecture for nonlinear function approximation. Neural Networks, 14(6–7), 933–939. https://doi.org/10.1016/s0893-6080(01)00080-6
A neuromorphic VLSI design for spike timing and rate based synaptic plasticity
Neural Networks / Sep 01, 2013
Rahimi Azghadi, M., Al-Sarawi, S., Abbott, D., & Iannella, N. (2013). A neuromorphic VLSI design for spike timing and rate based synaptic plasticity. Neural Networks, 45, 70–82. https://doi.org/10.1016/j.neunet.2013.03.003
Finding iterative roots with a spiking neural network
Information Processing Letters / Sep 01, 2005
Iannella, N., & Kindermann, L. (2005). Finding iterative roots with a spiking neural network. Information Processing Letters, 95(6), 545–551. https://doi.org/10.1016/j.ipl.2005.05.022
A unified computational model for cortical post-synaptic plasticity
eLife / Jul 30, 2020
Mäki-Marttunen, T., Iannella, N., Edwards, A. G., Einevoll, G. T., & Blackwell, K. T. (2020). A unified computational model for cortical post-synaptic plasticity. ELife, 9. CLOCKSS. https://doi.org/10.7554/elife.55714
Revisiting Special Relativity: A Natural Algebraic Alternative to Minkowski Spacetime
PLoS ONE / Dec 31, 2012
Chappell, J. M., Iqbal, A., Iannella, N., & Abbott, D. (2012). Revisiting Special Relativity: A Natural Algebraic Alternative to Minkowski Spacetime. PLoS ONE, 7(12), e51756. https://doi.org/10.1371/journal.pone.0051756
Tunable Low Energy, Compact and High Performance Neuromorphic Circuit for Spike-Based Synaptic Plasticity
PLoS ONE / Feb 13, 2014
Rahimi Azghadi, M., Iannella, N., Al-Sarawi, S., & Abbott, D. (2014). Tunable Low Energy, Compact and High Performance Neuromorphic Circuit for Spike-Based Synaptic Plasticity. PLoS ONE, 9(2), e88326. https://doi.org/10.1371/journal.pone.0088326
Formulation and Implementation of Nonlinear Integral Equations to Model Neural Dynamics Within the Vertebrate Retina
International Journal of Neural Systems / Jul 18, 2018
Eshraghian, J. K., Baek, S., Kim, J.-H., Iannella, N., Cho, K., Goo, Y. S., Iu, H. H. C., Kang, S.-M., & Eshraghian, K. (2018). Formulation and Implementation of Nonlinear Integral Equations to Model Neural Dynamics Within the Vertebrate Retina. International Journal of Neural Systems, 28(07), 1850004. https://doi.org/10.1142/s0129065718500041
Maximization of Crossbar Array Memory Using Fundamental Memristor Theory
IEEE Transactions on Circuits and Systems II: Express Briefs / Dec 01, 2017
Eshraghian, J. K., Cho, K.-R., Iu, H. H. C., Fernando, T., Iannella, N., Kang, S.-M., & Eshraghian, K. (2017). Maximization of Crossbar Array Memory Using Fundamental Memristor Theory. IEEE Transactions on Circuits and Systems II: Express Briefs, 64(12), 1402–1406. https://doi.org/10.1109/tcsii.2017.2767078
Signal Flow Platform for Mapping and Simulation of Vertebrate Retina for Sensor Systems
IEEE Sensors Journal / Aug 01, 2016
Cho, K., Baek, S., Cho, S.-W., Kim, J.-H., Goo, Y. S., Eshraghian, J. K., Iannella, N., & Eshraghian, K. (2016). Signal Flow Platform for Mapping and Simulation of Vertebrate Retina for Sensor Systems. IEEE Sensors Journal, 16(15), 5856–5866. https://doi.org/10.1109/jsen.2016.2561310
A review of methods for identifying stochastic resonance in simulations of single neuron models
Network: Computation in Neural Systems / Mar 11, 2015
McDonnell, M. D., Iannella, N., To, M.-S., Tuckwell, H. C., Jost, J., Gutkin, B. S., & Ward, L. M. (2015). A review of methods for identifying stochastic resonance in simulations of single neuron models. Network: Computation in Neural Systems, 26(2), 35–71. https://doi.org/10.3109/0954898x.2014.990064
Memristor-based synaptic networks and logical operations using in-situ computing
2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing / Dec 01, 2011
Kavehei, O., Al-Sarawi, S., Cho, K.-R., Iannella, N., Kim, S.-J., Eshraghian, K., & Abbott, D. (2011, December). Memristor-based synaptic networks and logical operations using in-situ computing. 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing. https://doi.org/10.1109/issnip.2011.6146610
Efficient design of triplet based Spike-Timing Dependent Plasticity
The 2012 International Joint Conference on Neural Networks (IJCNN) / Jun 01, 2012
Azghadi, M. R., Al-Sarawi, S., Iannella, N., & Abbott, D. (2012, June). Efficient design of triplet based Spike-Timing Dependent Plasticity. The 2012 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2012.6252820
29th Annual Computational Neuroscience Meeting: CNS*2020
BMC Neuroscience / Dec 01, 2020
29th Annual Computational Neuroscience Meeting: CNS*2020. (2020). BMC Neuroscience, 21(S1). https://doi.org/10.1186/s12868-020-00593-1
Novel VLSI implementation for triplet-based spike-timing dependent plasticity
2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing / Dec 01, 2011
Azghadi, M. R., Kavehei, O., Al-Sarawi, S., Iannella, N., & Abbott, D. (2011, December). Novel VLSI implementation for triplet-based spike-timing dependent plasticity. 2011 Seventh International Conference on Intelligent Sensors, Sensor Networks and Information Processing. https://doi.org/10.1109/issnip.2011.6146525
Design and implementation of BCM rule based on spike-timing dependent plasticity
The 2012 International Joint Conference on Neural Networks (IJCNN) / Jun 01, 2012
Rahimi Azghadi, M., Al-Sarawi, S., Iannella, N., & Abbott, D. (2012, June). Design and implementation of BCM rule based on spike-timing dependent plasticity. The 2012 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2012.6252778
Simulation of electromyographic recordings following transcranial magnetic stimulation
Journal of Neurophysiology / Nov 01, 2018
Moezzi, B., Schaworonkow, N., Plogmacher, L., Goldsworthy, M. R., Hordacre, B., McDonnell, M. D., Iannella, N., Ridding, M. C., & Triesch, J. (2018). Simulation of electromyographic recordings following transcranial magnetic stimulation. Journal of Neurophysiology, 120(5), 2532–2541. https://doi.org/10.1152/jn.00626.2017
Modelling and analysis of signal flow platform implementation into retinal cell pathway
2016 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) / Oct 01, 2016
Eshraghian, J. K., Baek, S., Cho, K., Iannella, N., Kim, J.-H., Goo, Y. S., Iu, H. H. C., Fernando, T., & Eshraghian, K. (2016, October). Modelling and analysis of signal flow platform implementation into retinal cell pathway. 2016 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). https://doi.org/10.1109/apccas.2016.7804011
Ion channel noise can explain firing correlation in auditory nerves
Journal of Computational Neuroscience / Aug 02, 2016
Moezzi, B., Iannella, N., & McDonnell, M. D. (2016). Ion channel noise can explain firing correlation in auditory nerves. Journal of Computational Neuroscience, 41(2), 193–206. https://doi.org/10.1007/s10827-016-0613-9
ANALYTICAL SOLUTIONS FOR NONLINEAR CABLE EQUATIONS WITH CALCIUM DYNAMICS I: DERIVATIONS
Journal of Integrative Neuroscience / Jun 01, 2006
IANNELLA, N., & TANAKA, S. (2006). ANALYTICAL SOLUTIONS FOR NONLINEAR CABLE EQUATIONS WITH CALCIUM DYNAMICS I: DERIVATIONS. Journal of Integrative Neuroscience, 05(02), 249–272. https://doi.org/10.1142/s0219635206001124
Time As a Geometric Property of Space
Frontiers in Physics / Nov 17, 2016
Chappell, J. M., Hartnett, J. G., Iannella, N., Iqbal, A., & Abbott, D. (2016). Time As a Geometric Property of Space. Frontiers in Physics, 4. https://doi.org/10.3389/fphy.2016.00044
A new compact analog VLSI model for Spike Timing Dependent Plasticity
2013 IFIP/IEEE 21st International Conference on Very Large Scale Integration (VLSI-SoC) / Oct 01, 2013
Azghadi, M. R., Al-Sarawi, S., Iannella, N., & Abbott, D. (2013, October). A new compact analog VLSI model for Spike Timing Dependent Plasticity. 2013 IFIP/IEEE 21st International Conference on Very Large Scale Integration (VLSI-SoC). https://doi.org/10.1109/vlsi-soc.2013.6673236
Optimization in the Design of Natural Structures, Biomaterials, Bioinformatics and Biometric Techniques for Solving Physiological Needs and Ultimate Performance of Bio-devices
Current Bioinformatics / Jun 28, 2019
Wong, K. K. L. (2019). Optimization in the Design of Natural Structures, Biomaterials, Bioinformatics and Biometric Techniques for Solving Physiological Needs and Ultimate Performance of Bio-devices. Current Bioinformatics, 14(5), 374–375. https://doi.org/10.2174/157489361405190628122355
27th Annual Computational Neuroscience Meeting (CNS*2018): Part One
BMC Neuroscience / Oct 01, 2018
27th Annual Computational Neuroscience Meeting (CNS*2018): Part One. (2018). BMC Neuroscience, 19(S2). https://doi.org/10.1186/s12868-018-0452-x
ANALYTICAL SOLUTIONS FOR NONLINEAR CABLE EQUATIONS WITH CALCIUM DYNAMICS II: SALTATORY TRANSMISSION IN A SPARSELY EXCITABLE CABLE MODEL
Journal of Integrative Neuroscience / Jun 01, 2007
IANNELLA, N., & TANAKA, S. (2007). ANALYTICAL SOLUTIONS FOR NONLINEAR CABLE EQUATIONS WITH CALCIUM DYNAMICS II: SALTATORY TRANSMISSION IN A SPARSELY EXCITABLE CABLE MODEL. Journal of Integrative Neuroscience, 06(02), 241–277. https://doi.org/10.1142/s0219635207001489
Modulating STDP Balance Impacts the Dendritic Mosaic
Frontiers in Computational Neuroscience / Jun 09, 2017
Iannella, N., & Launey, T. (2017). Modulating STDP Balance Impacts the Dendritic Mosaic. Frontiers in Computational Neuroscience, 11. https://doi.org/10.3389/fncom.2017.00042
Pairing frequency experiments in visual cortex reproduced in a neuromorphic STDP circuit
2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS) / Dec 01, 2013
Azghadi, M. R., Al-Sarawi, S., Abbott, D., & Iannella, N. (2013, December). Pairing frequency experiments in visual cortex reproduced in a neuromorphic STDP circuit. 2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS). https://doi.org/10.1109/icecs.2013.6815396
Time evolution of receptive fields
1996 Australian New Zealand Conference on Intelligent Information Systems. Proceedings. ANZIIS 96
Iannella, N., & Bouzerdoum, A. (n.d.). Time evolution of receptive fields. 1996 Australian New Zealand Conference on Intelligent Information Systems. Proceedings. ANZIIS 96. https://doi.org/10.1109/anziis.1996.573907
Synaptic efficacy mosaics and the impact of morphology
2017 International Joint Conference on Neural Networks (IJCNN) / May 01, 2017
Iannella, N., & Launey, T. (2017, May). Synaptic efficacy mosaics and the impact of morphology. 2017 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn.2017.7966335
Live demonstration: Signal flow platform implementation into retinal cell pathway
2016 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) / Oct 01, 2016
Baek, S., Eshraghian, J. K., Cho, K., Iannella, N., Kim, J.-H., Iu, H. H. C., Fernando, T., & Eshraghian, K. (2016, October). Live demonstration: Signal flow platform implementation into retinal cell pathway. 2016 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). https://doi.org/10.1109/apccas.2016.7804033
Global sensitivity and uncertainty analysis of Transients in the Universal Line Model
2014 16th International Conference on Harmonics and Quality of Power (ICHQP) / May 01, 2014
Shahabi, N., Zivanovic, R., Iannella, N., & Al-Sarawi, S. (2014, May). Global sensitivity and uncertainty analysis of Transients in the Universal Line Model. 2014 16th International Conference on Harmonics and Quality of Power (ICHQP). https://doi.org/10.1109/ichqp.2014.6842920
Approximate analytical solution of a (V,m,h) reduced system for backpropagating action potentials in sparsely excitable dendrites
Journal of Multiscale Neuroscience / Jul 25, 2023
Iannella, N., & Poznanski, R. R. (2023). Approximate analytical solution of a (V,m,h) reduced system for backpropagating action potentials in sparsely excitable dendrites. Journal of Multiscale Neuroscience, 2(2), 350–374. https://doi.org/10.56280/1583164092
A new derivation of the Minkowski metric
Journal of Physics Communications / Jun 01, 2023
Chappell, J. M., Hartnett, J. G., Iannella, N., Iqbal, A., Berkahn, D. L., & Abbott, D. (2023). A new derivation of the Minkowski metric. Journal of Physics Communications, 7(6), 065001. https://doi.org/10.1088/2399-6528/acd986
A unified computational model for cortical post-synaptic plasticity
Jan 28, 2020
Mäki-Marttunen, T., Iannella, N., Edwards, A. G., Einevoll, G. T., & Blackwell, K. T. (2020). A unified computational model for cortical post-synaptic plasticity. https://doi.org/10.1101/2020.01.27.921254
Neurons and Plasticity: What Do Glial Cells Have to Do with This?
Brain Informatics and Health / Jan 01, 2020
Iannella, N., & Condemine, M. (2020). Neurons and Plasticity: What Do Glial Cells Have to Do with This? In Functional Brain Mapping: Methods and Aims (pp. 13–46). Springer Singapore. https://doi.org/10.1007/978-981-15-6883-1_2
25th Annual Computational Neuroscience Meeting: CNS-2016
BMC Neuroscience / Aug 01, 2016
Sharpee, T. O., Destexhe, A., Kawato, M., Sekulić, V., Skinner, F. K., Wójcik, D. K., Chintaluri, C., Cserpán, D., Somogyvári, Z., Kim, J. K., Kilpatrick, Z. P., Bennett, M. R., Josić, K., Elices, I., Arroyo, D., Levi, R., Rodriguez, F. B., Varona, P., … Chhabria, K. (2016). 25th Annual Computational Neuroscience Meeting: CNS-2016. BMC Neuroscience, 17(S1). https://doi.org/10.1186/s12868-016-0283-6
A Nonlinear Cable Framework for Bidirectional Synaptic Plasticity
PLoS ONE / Aug 22, 2014
Iannella, N., Launey, T., Abbott, D., & Tanaka, S. (2014). A Nonlinear Cable Framework for Bidirectional Synaptic Plasticity. PLoS ONE, 9(8), e102601. https://doi.org/10.1371/journal.pone.0102601
Modeling the influence of short term depression in vesicle release and stochastic calcium channel gating on auditory nerve spontaneous firing statistics
Frontiers in Computational Neuroscience / Dec 23, 2014
Moezzi, B., Iannella, N., & McDonnell, M. D. (2014). Modeling the influence of short term depression in vesicle release and stochastic calcium channel gating on auditory nerve spontaneous firing statistics. Frontiers in Computational Neuroscience, 8. https://doi.org/10.3389/fncom.2014.00163
Motif-Role-Fingerprints: The Building-Blocks of Motifs, Clustering-Coefficients and Transitivities in Directed Networks
PLoS ONE / Dec 08, 2014
McDonnell, M. D., Yaveroğlu, Ö. N., Schmerl, B. A., Iannella, N., & Ward, L. M. (2014). Motif-Role-Fingerprints: The Building-Blocks of Motifs, Clustering-Coefficients and Transitivities in Directed Networks. PLoS ONE, 9(12), e114503. https://doi.org/10.1371/journal.pone.0114503
Spike timing-dependent plasticity as the origin of the formation of clustered synaptic efficacy engrams
Frontiers in Computational Neuroscience / Jan 01, 2010
Iannella. (2010). Spike timing-dependent plasticity as the origin of the formation of clustered synaptic efficacy engrams. Frontiers in Computational Neuroscience. https://doi.org/10.3389/fncom.2010.00021
Synaptic efficacy cluster formation across the dendrite via STDP
Neuroscience Letters / Jul 01, 2006
Iannella, N., & Tanaka, S. (2006). Synaptic efficacy cluster formation across the dendrite via STDP. Neuroscience Letters, 403(1–2), 24–29. https://doi.org/10.1016/j.neulet.2006.03.079
Firing properties of a stochastic PDE model of a rat sensory cortex layer 2/3 pyramidal cell
Mathematical Biosciences / Mar 01, 2004
Iannella, N., Tuckwell, H. C., & Tanaka, S. (2004). Firing properties of a stochastic PDE model of a rat sensory cortex layer 2/3 pyramidal cell. Mathematical Biosciences, 188(1–2), 117–132. https://doi.org/10.1016/j.mbs.2003.10.002
Long-Range Horizontal Connections Assist the Formation of Robust Orientation Maps
The Neural Basis of Early Vision / Jan 01, 2003
Iannella, N., Ribot, J., & Tanaka, S. (2003). Long-Range Horizontal Connections Assist the Formation of Robust Orientation Maps. In The Neural Basis of Early Vision (pp. 221–224). Springer Japan. https://doi.org/10.1007/978-4-431-68447-3_75
27th Annual Computational Neuroscience Meeting (CNS*2018): Part Two
BMC Neuroscience / Oct 01, 2018
27th Annual Computational Neuroscience Meeting (CNS*2018): Part Two. (2018). BMC Neuroscience, 19(S2). https://doi.org/10.1186/s12868-018-0451-y
Education
University of Adelaide
Graduate Certificate in Education (Higher Education) , School of Electrical & Electronic engineering / December, 2012
Denki Tsushin Daigaku
PhD (Eng), Information and Communications Engineering / March, 2009
Experience
University of Oslo
Postdoctoral Fellow / July, 2018 — Present
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