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Dr Max Garagnani

Staff details

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

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

  • Ph.D. in Computational Cognitive Neuroscience, University of Cambridge (UK), 2009
  • Ph.D. in Artificial Intelligence, University of Durham (UK), 1999
  • “Laurea” (= B.Sc. Hons. + M.Sc. 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

Article

Neurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex
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

A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords
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,

Brain connections of words, perceptions and actions: A neurobiological model of spatio-temporal semantic activation in the human cortex
Tomasello, R.; Garagnani, M.; Wennekers, T. and Pulvermüller, F.. 2016. Brain connections of words, perceptions and actions: A neurobiological model of spatio-temporal semantic activation in the human cortex. Neuropsychologia, ISSN 0028-3932

Conceptual grounding of language in action and perception: a neurocomputational model of the emergence of category specificity and semantic hubs
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, pp. 721-732. ISSN 0953-816X

From sensorimotor learning to memory cells in prefrontal and temporal association cortex: A neurocomputational study of disembodiment
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

Thinking in circuits: toward neurobiological explanation in cognitive neuroscience
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

Auditory processing and sensory behaviours in children with autism spectrum disorders as revealed by mismatch negativity
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

Neuronal correlates of decisions to speak and act: Spontaneous emergence and dynamic topographies in a computational model of frontal and temporal areas
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

From sounds to words: A neurocomputational model of adaptation, inhibition and memory processes in auditory change detection
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

Effects of attention on what is known and what is not: MEG evidence for functionally discrete memory circuits
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,

Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network
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

A neuroanatomically grounded Hebbian-learning model of attention–language interactions in the human brain
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