Dr Max Garagnani

Staff details

Position Lecturer in Computer Science
Department Computing
Email M.Garagnani (@gold.ac.uk)
Dr Max Garagnani

Dr Max Garagnani is a lecturer and co-programme leader of the MSc in Computational Cognitive Neuroscience.

Max’s research lies at the intersection of computational and cognitive neuroscience. More specifically, he focuses on the implementation of biologically realistic neural-network models closely mimicking structure, connectivity, and physiology of the human cortex.

These models are applied to simulate and explain the cortical mechanisms underlying the spontaneous emergence of cognitive function (especially, language, but also, memory, attention, and “free” decisions) starting from a completely randomly connected, uniform neural substrate, and by means of purely biologically realistic learning mechanisms.

In parallel to the modelling work, Max collaborates with external experimental cognitive neuroscience labs to apply brain imaging techniques as a tool to test and validate the specific predictions emerging from the models in groups of healthy volunteers.

A recently concluded example of such an interdisciplinary project is the 4-year jointly EPSRC/BBSRC-funded project BABEL, which investigated the neural mechanisms underlying embodied word learning by joint use of neuroimaging, brain-inspired neurocomputational modelling, neuromorphic engineering and real-time implementations on the humanoid robot iCub.

Academic qualifications

  • PhD in Computational Cognitive Neuroscience, University of Cambridge (UK), 2009
  • PhD in Artificial Intelligence, University of Durham (UK), 1999
  • “Laurea” (= BSc Hons. + MSc by Research) in Computer Science, University of Bologna (Italy), 1994

Career history

  • Lecturer, Department of Computing, Goldsmiths University of London, UK (Sep. 2016 – present)
  • Visiting Researcher – Brain & Language Laboratory, Free University of Berlin, Germany (Sep. 2013 – present)
  • Postdoctoral Research Fellow – Centre for Robotics & Neural Systems, University of Plymouth, Plymouth, UK (Nov. 2012 – October 2016)
  • Visiting Researcher – University of Cambridge, Department of Experimental Psychology, Cambridge, UK (Nov. 2012 – Jun. 2013)
  • Investigator Scientist – UK Medical Research Council, Cognition and Brain Sciences Unit, Cambridge (Oct. 2008 – Jul. 2012)
  • Visiting Scholar – International Computer Science Institute, Berkeley, USA (Apr. – Oct. 2001)
  • Research Fellow – The Open University, Department of Computing, Milton Keynes, UK (Mar. 1999 – July 2005)

Research Interests

  • Neural networks, computational neuroscience
  • Brain basis of language acquisition and processing
  • Neuroanatomically and neurobiologically realistic models of the cortex
  • Neurocomputational models of cognitive processes (in particular, language, memory, action planning)
  • Neuroimaging as a tool.

PhD supervision

Broadly speaking, Max is interested in supervising projects targeting the implementation and application of biologically realistic neural network models that are able to exhibit the spontaneous emergence of cognitive function (especially, language) and other phenomena (e.g., the spontaneous emergence of thoughts and unconscious decisions to act) starting from an initially uniform, randomly connected multi-layer neuronal substrate, using biologically-realistic Hebbian learning mechanisms.

Recent publications

Tomasello, R., Garagnani, M., Wennekers, T. & Pulvermüller, F. (in press) Brain connections of words, perceptions and actions: A neurobiological model of spatio-temporal semantic activation in the human cortex. Neuropsychologia. 2016. DOI: 10.1016/j.neuropsychologia.2016.07.004

Garagnani, M. & Pulvermüller, F. (2016) Conceptual grounding in action and perception: a neurocomputational model of the emergence of category specificity and semantic hubs. European Journal of Neuroscience, 43 (6):721– 737. DOI:10.1111/ejn.13145

Pulvermüller, F., Garagnani, M., & Wennekers, T. (2014) Thinking in circuits: toward neurobiological explanation in cognitive neuroscience. Biological Cybernetics, 108(5): 573– 593

Pulvermüller, F. & Garagnani, M. (2014) From sensorimotor learning to memory cells in prefrontal and anterior-temporal cortex: A neurocomputational study of disembodiment. Cortex, 57 :1– 21

Garagnani, M. & Pulvermüller, F. (2013) Neuronal correlates of decisions to speak and act: spontaneous emergence and dynamic topographies in a computational model of frontal and temporal areas. Brain and Language, 127(1):75–85

Publications

Book Section

Garagnani, M.; Kirilina, E. and Pulvermüller, Friedemann. 2019. Perception-action circuits for word learning and semantic grounding: a neurocomputational model and neuroimaging study. In: Maria Raposo; Paulo Ribeiro; Susanna Sério; Antonino Staiano and Angelo Ciaramella, eds. Computational Intelligence Methods for Bioinformatics and Biostatistics: 15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, 2018, Revised Selected Papers. Cham, Switzerland: Springer International Publishing. ISBN 9783030345846

Garagnani, M.. 2005. A Diagrammatic Inter-Lingua for Planning Domain Descriptions. In: Luis Castillo; Daniel Borrajo; Miguel A. Salido and Angelo Oddi, eds. Planning, Scheduling and Constraint Satisfaction: From Theory to Practice. 117 Amsterdam: IOS Press, pp. 129-138. ISBN 9781586034849

Garagnani, M.. 2005. A Framework for Hybrid Planning. In: Max Bramer; Frans Coenen and Tony Allen, eds. Research and Development in Intelligent Systems XXI: Proceedings of AI-2004, the Twenty-fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. London: Springer, pp. 214-227. ISBN 9781852339074

Garagnani, M.. 2005. A Framework for Hybrid and Analogical Planning. In: Ioannis Vlahavas and Dimitris Vrakas, eds. Intelligent Techniques for Planning. Hershey, Pennsylvania: Idea Group Publishing, pp. 35-89. ISBN 9781591404507

Garagnani, M.. 2004. Model-based Planning in Physical domains using SetGraphs. In: Frans Coenen; Alun Preece and Ann L. Macintosh, eds. Research and Development in Intelligent Systems XX: Proceedings of AI2003, the Twenty-third SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. London: Springer, pp. 295-308. ISBN 9781852337803

Garagnani, M.. 2001. A Correct Algorithm for Efficient Planning with Preprocessed Domain Axioms. In: Max Bramer; Alun Preece and Frans Coenen, eds. Research and Development in Intelligent Systems XVII: Proceedings of ES2000, the Twentieth SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence, Cambridge, December 2000. London: Springer, pp. 363-374. ISBN 9781852334031

Reed, Chris; Long, Derek; Fox, Maria and Garagnani, M.. 1997. Persuasion as a form of inter-agent negotiation. In: Chengqi Zhang and Dickson Lukose, eds. Multi-Agent Systems Methodologies and Applications: Second Australian Workshop on Distributed Artificial Intelligence Cairns, QLD, Australia, August 27, 1996 Selected Papers. Berlin: Springer, pp. 120-136. ISBN 9783540634126

Article

Tomasello, Rosario; Wennekers, Thomas; Garagnani, M. and Pulvermüller, Friedemann. 2019. Visual cortex recruitment during language processing in blind individuals is explained by Hebbian learning. Scientific Reports, 9(3579),

Tomasello, R.; Garagnani, M.; Wennekers, T. and Pulvermüller, F.. 2018. A neurobiologically constrained cortex model of semantic grounding with spiking neurons and brain-like connectivity. Frontiers in Computational Neuroscience, 12(88),

Tomasello, R.; Garagnani, M.; Wennekers, T. and Pulvermüller, F.. 2017. Brain connections of words, perceptions and actions: A neurobiological model of spatio-temporal semantic activation in the human cortex. Neuropsychologia, 98, pp. 111-129. ISSN 0028-3932

Schomers, M.R.; Garagnani, M. and Pulvermüller, F.. 2017. Neurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex. The Journal of Neuroscience, 37(11), pp. 3045-3055. ISSN 0270-6474

Garagnani, M.; Lucchese, G.; Tomasello, R.; Wennekers, T. and Pulvermüller, F.. 2017. A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords. Frontiers in Computational Neuroscience, 10(145),

Garagnani, M. and Pulvermüller, F.. 2016. Conceptual grounding of language in action and perception: a neurocomputational model of the emergence of category specificity and semantic hubs. European Journal of Neuroscience, 43(6), pp. 721-732. ISSN 0953-816X

Pulvermüller, F.; Garagnani, M. and Wennekers, T.. 2014. Thinking in circuits: toward neurobiological explanation in cognitive neuroscience. Biological Cybernetics, 108(5), pp. 573-593. ISSN 0340-1200

Pulvermüller, F. and Garagnani, M.. 2014. From sensorimotor learning to memory cells in prefrontal and temporal association cortex: A neurocomputational study of disembodiment. Cortex, 57, pp. 1-21. ISSN 0010-9452

Ludlow, A.; Mohr, B.; Whitmore, A.; Garagnani, M.; Pulvermüller, F. and Gutierrez, R.. 2014. Auditory processing and sensory behaviours in children with autism spectrum disorders as revealed by mismatch negativity. Brain and Cognition, 86, pp. 55-63. ISSN 0278-2626

Garagnani, M. and Pulvermüller, F.. 2013. Neuronal correlates of decisions to speak and act: Spontaneous emergence and dynamic topographies in a computational model of frontal and temporal areas. Brain & Language, 127(1), pp. 75-85. ISSN 0093-934X

Garagnani, M. and Pulvermüller, Friedemann. 2011. Investigating cognitive representations with brain-like networks and MEG/EEG. Clinical Neurophysiology, 122, S12. ISSN 1388-2457

Garagnani, M. and Pulvermüller, F.. 2011. From sounds to words: A neurocomputational model of adaptation, inhibition and memory processes in auditory change detection. Neuroimage, 54(1), pp. 170-181. ISSN 1053-8119

Garagnani, M.; Shtyrov, Y. and Pulvermüller, F.. 2009. Effects of attention on what is known and what is not: MEG evidence for functionally discrete memory circuits. Frontiers in Human Neuroscience,

Garagnani, M.; Wennekers, T. and Pulvermüller, F.. 2009. Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network. Cognitive Computation, 1(2), pp. 160-176. ISSN 1866-9956

Garagnani, M.; Wennekers, T. and Pulvermüller, F.. 2008. A neuroanatomically grounded Hebbian-learning model of attention–language interactions in the human brain. European Journal of Neuroscience, 27(2), pp. 492-513. ISSN 0953-816X

Garagnani, M.; Wennekers, Thomas and Pulvermüller, Friedemann. 2007. Explaining the effects of attention on lexical processes using a single Hebbian neuronal model of the language cortex. Neural Plasticity, 2007, pp. 66-67. ISSN 2090-5904

Garagnani, M.; Wennekers, Thomas and Pulvermüller, Friedemann. 2006. A neuronal model of the language cortex. Neurocomputing, 70(10-12), pp. 1914-1919. ISSN 0925-2312

Wennekers, Thomas; Garagnani, M. and Pulvermüller, Friedemann. 2006. Language models based on Hebbian cell assemblies. Journal of Physiology-Paris, 100(1-3), pp. 16-30. ISSN 0928-4257

Conference or Workshop Item

Tomasello, R.; Wennekers, T.; Garagnani, M. and Pulvermüller, F.. 2019. 'Recruitment of visual cortex for language processing in blind individuals: A neurobiological model'. In: 2019 Annual meeting of the Cognitive Neuroscience Society (CNS 2019). San Francisco, United States 25 March 2019.

Garagnani, M.. 2017. 'Simulating word learning and high-frequency brain responses to linguistic items in a neurobiologically realistic model of the cortex'. In: 7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL/EPIROB 2017) – 2nd Workshop on Language Learning. Lisbon, Portugal 18-21 September 2017.

Tomasello, Rosario; Garagnani, M.; Wennekers, Thomas and Pulvermüller, Friedemann. 2017. 'Semantic grounding in a neurobiologically-constrained cortex-model with realistic connectivity and spiking neurons'. In: 24th Annual Meeting of the Cognitive Neuroscience Society (CNS 2017). San Francisco, United States 25-28 March 2017.

Garagnani, M.. 2016. 'Word acquisition and semantic grounding in a neuroanatomically realistic, Hebbian-learning, spiking neural network model of the cortex'. In: Neuroinformatics 2016. Reading, United Kingdom 3-4 September 2016.

Schomers, M.R.; Garagnani, M. and Pulvermüller, F.. 2015. 'Highway to (verbal) memory: Neurocomputational consequences of specifically human connectivity in perisylvian cortex.'. In: 23rd Annual Meeting of the Cognitive Neuroscience Society (CNS 2016). New York, United States.

Adams, S.V.; Wennekers, T.; Cangelosi, A.; Garagnani, M. and Pulvermüller, F.. 2015. 'Learning Visual-Motor Cell Assemblies for the iCub Robot using a Neuroanatomically Grounded Neural Network'. In: IEEE Symposium Series on Computational Intelligence, Cognitive Algorithms, Mind and Brain (SSCI-CCMB 2014). Orlando, United States 9-12 December 2014.

Shtyrov, Y.; Kimppa, L. and Garagnani, M.. 2014. 'Electrophysiological and haemodynamic biomarkers of rapid acquisition of novel wordforms'. In: Microstructures of Learning: Novel methods and approaches for assessing structural and functional changes underlying knowledge acquisition in the brain. Lund, Sweden 23 May, 2014.

Garagnani, M.; Shtyrov, Y. and Davis, M.. 2012. 'Effects of sleep and number of repetitions on novel spoken word learning: fMRI evidence.'. In: 20th Annual Meeting of the Cognitive Neuroscience Society (CNS 2013). San Francisco, United States.

Garagnani, M.; Wennekers, Thomas and Pulvermüller, Friedemann. 2009. 'MMN reflections of language and attention: a neurocomputational model'. In: MMN 09 Fifth Conference on Mismatch Negativity (MMN) and its Clinical and Scientific Applications. Budapest, Hungary 4-7 April 2009.

Garagnani, M.; Shtyrov, Yury and Pulvermüller, Friedemann. 2009. 'Explaining Attention and Language interactions: magnetic MMN validation of neurocomputational predictions'. In: MMN 09 Fifth Conference on Mismatch Negativity (MMN) and its Clinical and Scientific Applications. Budapest, Hungary 4-7 April 2009.

Garagnani, M.; Shastri, Lokendra and Wendelken, Carter. 2002. 'A Connectionist model of Planning via Back-chaining Search'. In: 24th Annual Meeting of the Cognitive Science Society (CogSci 2002). Fairfax, VA, United States 7-10 August 2002.