Carnell A, Nicolaes J. 2022. UCB Biopharma SRL. Medical image analysis system and method for identification of lesions. US Patent pending.

Nicolaes J, Machado P, Baraliakos X, Santosh M, Carnell A, de Peyrecave N, Bennett A. Development of a Deep Learning Algorithm for the Detection of Sacroiliitis on MRI in Patients with Active Axial Spondyloarthritis [abstract]. Arthritis Rheumatol. 2021; 73 (suppl 9). Development of a Deep Learning Algorithm for the Detection of Sacroiliitis on MRI in Patients with Active Axial Spondyloarthritis.

Van Ooyen, A., Carnell, A., De Ridder, S., Tarrigan, B., Mansvelder, H.D., Bijma, F., de Gunst, M. & Van Pelt, J., 2014. Independently outgrowing neurons and geometry-based synapse formation produce networks with realistic synaptic connectivity. PLoSOne.

Olde Scheper, T. & Carnell, A., 2013. A method of controlling a dynamic physical system that exhibits a chaotic behaviour. Patent.

Independently outgrowing neurons with a geometric synapse formation model develop realistic network connectivity patterns with small-world properties. Andrew Carnell, Sander de Ridder, Jaap van Pelt & Arjen van Ooyen. 2011. BMC Neuroscience. CNS. Stockholm, Sweden.

Van Pelt, J., Carnell, A., De Ridder, S., Mansvelder, H.D. & Van Ooyen, A., 2010. An algorithm for finding candidate synaptic sites in computer generated networks of neurons with realistic morphologies. Front. Comput. Neurosci. 4:148. doi: 10.3389/fncom.2010.00148

Simulated Networks With Realistic Neuronal Morphologies Show Small-World Connectivity. Poster presentation at Forum of European Neuroscience (FENS) 2010. Amsterdam, The Netherlands.

Carnell, A., 2009. How much can one neuron learn? – An investigation of the learning of multiple precise I/O spike train associations.

Carnell, A., 2009. An analysis of the use of Hebbian and Anti-Hebbian Spike Time Dependent Plasticity learning functions within the context of recurrent spiking neural networks. Neurocomputing, 72(4-6), 685692.

Carnell, A. & Richardson, D., 2007. Parallel computation in spiking neural nets. Theor. Comput. Sci, 386(1-2), p.57-72.

Carnell, A. & Richardson, D., 2005. Linear algebra for time series of spikes. Proc. ESANN 2005, Bruges, Belgium, 27-29 April 2005, p.363-368.

An analysis of the use of Hebbian and Anti-Hebbian Spike Time Dependent Plasticity learning functions within the context of recurrent spiking neural networks, presented at Brain Inspired Cognitive Systems (BICS) conference 2006. Lesbos, Greece.

Linear algebra for time series of spikes. Presentation at the European Symposium on Artificial Neural Networks (ESANN) 2005. Bruges, Belgium.

Elements of Arithmetic in Spiking Neural nets. Presentation at the 9th Neural Computation and Psychology Workshop (NCPW) 2004. Plymouth, UK.