- Influence of language on perception and concept formation in a brain-constrained deep neural network model Henningsen-Schomers, Malte R.; Garagnani, M. and Pulvermüller, Friedemann. 2023. Influence of language on perception and concept formation in a brain-constrained deep neural network model. Philosophical Transactions of the Royal Society B: Biological Sciences, 378(1870), 20210373. ISSN 0962-8436
- Semantic grounding of novel spoken words in the primary visual cortex Garagnani, M.; Kirilina, E. and Pulvermüller, F. 2021. Semantic grounding of novel spoken words in the primary visual cortex. Frontiers in Human Neuroscience, 15, 581847. ISSN 1662-5161
- Visual cortex recruitment during language processing in blind individuals is explained by Hebbian learning Tomasello, R.; Wennekers, T.; Garagnani, M. and Pulvermüller, F.. 2019. Visual cortex recruitment during language processing in blind individuals is explained by Hebbian learning. Scientific Reports, 9, 3579. ISSN 2045-2322
- A neurobiologically constrained cortex model of semantic grounding with spiking neurons and brain-like connectivity 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),
- 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.. 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
- 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, 10, 145. ISSN 1662-5188
- 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(6), pp. 721-737. ISSN 0953-816X
- 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
- 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
- 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
- Investigating cognitive representations with brain-like networks and MEG/EEG Garagnani, M. and Pulvermüller, Friedemann. 2011. Investigating cognitive representations with brain-like networks and MEG/EEG. Clinical Neurophysiology, 122, S12. ISSN 1388-2457
- 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
- Explaining the effects of attention on lexical processes using a single Hebbian neuronal model of the language cortex 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
- A neuronal model of the language cortex 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
- Language models based on Hebbian cell assemblies 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
Dr Max Garagnani
Max uses computer modelling and simulations to explore how we learn to speak and how the mind works.
Staff details
Links
Max is a Senior Lecturer in Computer Science and co-director of the MSc in Computational Cognitive Neuroscience programme. He holds a PhD in Computational Cognitive Neuroscience from the University of Cambridge, UK (2009) and a PhD in Artificial Intelligence from the University of Durham (1999).
Max's research focuses on building and applying biologically realistic, deep neural networks (mimicking structural and functional features of the cortex) to study the spontaneous emergence of cognitive functions, with a particular focus on natural language processing and acquisition, and spontaneous decisions.
His previous posts include Postdoctoral Researcher at the Centre for Robotics & Neural Systems (University of Plymouth), Investigator Scientist at MRC Cognition & Brain Sciences Unit (Cambridge, UK), and Visiting Scholar at the International Computer Science Institute (Berkeley, CA). He is also visiting researcher at the Brain Language Lab of the Free University of Berlin (Germany).
Academic qualifications
- PhD in Computational Cognitive Neuroscience 2009
- PhD in Artificial Intelligence 1999
- PG Certificate in Teaching and Learning in Higher Education 2023
- Laurea (BSc + MSc) in Computer Science 1994
Teaching and supervision
I supervise projects in my areas of interest (see "Research Interests" below).
I teach on the following programmes:
Research interests
My research centres on building and applying biologically realistic neural networks. Specifically, I model the neural mechanisms underlying the emergence of cognitive functions (in particular, language acquisition and processing, spontaneous decision making) in the brain. I simulate the emergence of such higher-level functions starting from an initially randomly connected uniform neural substrate, and by means of unsupervised learning mechanisms closely mimicking synaptic plasticity processes known to exist in the cortex. In parallel to the computational modelling, I also collaborate with experimentalists to apply brain imaging and behavioural methods to test and validate the predictions emerging from the computational models.
Unlike most of the modelling work in this area, the neural architecture I have developed over the past two decades adopts a "first principles" approach, i.e., it is not designed to explain a specific dataset or cognitive function. In fact, while it was originally applied mainly to simulate spoken language acquisition processes, its generality later allowed its successful application to explain the emergence of (and the mechanisms underlying) a range of other cognitive phenomena. These include, prediction error and automatic change-detection responses, attention, the emergence of memory cells and working memory processes, the recruitment of visual cortex in blind individuals, and the emergence of spontaneous (or "free") decisions to act (see list of publications below).
I have worked closely and for several years with the Brain Language Lab at the Freie Universität Berlin (Germany), directed by Prof. Friedemann Pulvermüller, and was co-PI and staff member of the jointly EPSRC/BBSRC-funded interdisciplinary project BABEL, which investigated the neural mechanisms underlying embodied word learning by joint use of neuroimaging, brain-inspired modelling, neuromorphic engineering and real-time implementation on the humanoid robot iCub.
Featured publications
2023:
Influence of language on perception and concept formation in a brain-constrained deep neural network model.
Philosophical Transactions of the Royal Society B, 378: 20210373.
2023:
Breakdown of category-specific word representations in a brain-constrained neurocomputational model of semantic dementia.
Scientific Reports 13:19572.
2017:
Neurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex
The Journal of Neuroscience, 37(11), pp. 3045-3055
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. 721-737.
2014:
Thinking in circuits: toward neurobiological explanation in cognitive neuroscience.
Biological Cybernetics, 108(5), pp. 573-593.
Publications and research outputs
Book Section
- Perception-action circuits for word learning and semantic grounding: a neurocomputational model and neuroimaging study Garagnani, M.; Kirilina, E. and Pulvermüller, F.. 2020. 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
- A Diagrammatic Inter-Lingua for Planning Domain Descriptions 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
- A Framework for Hybrid and Analogical Planning 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
Article
- On the ability of standard and brain-constrained deep neural networks to support cognitive superposition: a position paper Garagnani, M.. 2024. On the ability of standard and brain-constrained deep neural networks to support cognitive superposition: a position paper. Cognitive Neurodynamics, 18, pp. 3383-3400. ISSN 1871-4080
- Distributed representations of prediction error signals across the cortical hierarchy are synergistic Gelens, Frank; Aijala, Julio; Roberts, Louis; Komatsu, Misako; Uran, Cem; Jensen, Michael A.; Miller, Kai J.; Ince, Robin A.A.; Garagnani, M.; Vinck, Martin and Canales-Johnson, Andres. 2024. Distributed representations of prediction error signals across the cortical hierarchy are synergistic. Nature Communications, 15, 3941. ISSN 2041-1723
- Breakdown of category-specific word representations in a brain-constrained neurocomputational model of semantic dementia Shtyrov, Y.; Efremov, A.; Kuptsova, A.; Wennekers, T.; Gutkin, B. and Garagnani, M.. 2023. Breakdown of category-specific word representations in a brain-constrained neurocomputational model of semantic dementia. Scientific Reports, 13, 19572. ISSN 2045-2322
Conference or Workshop Item
- Using information theory to measure the emergence of artificial free will in a spiking brain-constrained model of the human cortex Bourne, Josh; Rosas, Fernando, E. and Garagnani, M.. 2023. 'Using information theory to measure the emergence of artificial free will in a spiking brain-constrained model of the human cortex'. In: 32nd Annual Computational Neuroscience Meeting. Leipzig, Germany 15 - 19 July 2023.
- A brain-constrained deep neural-network model that can account for the readiness potential in self-initiated volitional action Ušacka, A.; Schurger, A. and Garagnani, M.. 2023. 'A brain-constrained deep neural-network model that can account for the readiness potential in self-initiated volitional action'. In: 32nd Annual Computational Neuroscience Meeting. Leipzig, Germany 15 - 19 July 2023.
- Standard feedforward neural networks with backprop cannot support cognitive superposition Vanegdom, A.; Nikolaev, N. and Garagnani, M.. 2022. 'Standard feedforward neural networks with backprop cannot support cognitive superposition'. In: Bernstein Conference 2022. Berlin, Germany 13-16 September 2022.
