Nicolangelo Iannella

Senior Research fellow, The University of Oslo, Faculty of Mathematics and Natural Sciences

Oslo

Research Expertise

Neuromorphic circuits
Neural networks, Neural learning and applications
Theoretical and Mathematical neuroscience
Computational neuroscience
Artificial Intelligence
Cognitive Neuroscience
Signal Processing
Computer Networks and Communications
Cellular and Molecular Neuroscience
Sensory Systems
Modeling and Simulation
Applied Mathematics
Mathematical Physics
Biophysics
Biochemistry
Genetics
Computational Mathematics
Neural learning and applications
Computational neuroscience a

About

Following pre-doctoral studies in Mathematics and Theoretical Physics, I received a PhD in Computational Neuroscience from the University of Electro-Communications, Japan in 2009. From 2009, I was a Postdoctoral Researcher in RIKEN BSI. In 2010, I won the prestigious Australian Research Council (ARC) Australian Postdoctoral Award (APD) fellowship, based at the University of Adelaide from 2010–2014. In 2012 he completed a Graduate Certificate in Education (Higher Education) (GCEHE) from the University of Adelaide. From 2014–2017 he was an adjunct research fellow at the University of South Australia. From 2016–2018, he was a Cascade (Marie Curie) Research Fellow in Mathematical Sciences at the University of Nottingham. From 2018- a research fellow at the University of Oslo. His research interests include AI, Artificial and spiking neural networks and learning algorithms, synaptic plasticity, neuronal dynamics, and neuromorphic engineering. Dr. Iannella is a member of SFN and a Senior member of the IEEE.

Publications

ππscattering 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

Adelaide, South Australia, Australia

Denki Tsushin Daigaku

PhD (Eng), Information and Communications Engineering / March, 2009

Chofu

Experience

University of Oslo

Postdoctoral Fellow / July, 2018Present

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