FP7 Marie Curie Initial Training Networks
Adaptive Brain Computations.

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Adaptive interactions with the environment depend on sophisticated brain plasticity mechanisms at multiple levels: from single neurons to large-scale brain networks. Traditionally, the study of plasticity has been fragmented into sensory, motor or decision-related circuits. However, translating brain plasticity findings to brain repair requires an understanding of the interactions between these circuits.

Our aim is to integrate this study of learning and plasticity to promote wellbeing and advance healthcare interventions. We take a multidisciplinary approach, synthesising methods from physiology, cellular neurobiology, pharmacology, brain imaging, behavioural science and computational modelling to reveal plasticity at multiple scales (cellular, structural and functional). We will test how learning modifies sensory representations, perceptual decisions and motor outputs. Further, we will examine brain reorganisation and long-term plasticity in cases of congenital or acquired sensory and motor deficits.

Our approach requires the coordinated efforts of international research leaders who work in top Universities and market-leading companies. This research training environment will deliver a cohort of young researchers experienced in transcending traditional disciplinary boundaries to deliver deeper insight into brain plasticity. Further, ABC’s approach of cementing interactions between sectors into the training programme means that our ESRs will have the skills necessary to move between sectors and can maximise the translation of their findings for human health and wellbeing.

The work has impact in assistive technology, education and rehabilitation and expert recognition systems. The involvement of private sector companies, and a pronounced focus on translational aspects within the network’s activities (i.e. at all training events), will enhance European capacities for brain plasticity research and its applications.

Unified probabilistic modelling of adaptive spatial-temporal structures in the human brain.

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Learning from experience and adapting our behaviour to new situations is a fundamental skill for our everyday interactions. But what are the brain plasticity mechanisms that mediate an individual's ability to make progress during training on complex tasks? What is it that differentiates `good' from `poor' learners in their ability to adapt? Recent advances in functional brain imaging technology provide us with the unique opportunity to study how the human brain changes with learning. However, the existing methods focus predominantly on modelling brain activity data within a single session rather than across training sessions. As such, these methods are not capable of capturing larger scale dependencies emerging in brain activity as training progresses. We will develop a novel methodology that allows holistic unified modelling of a series of brain imaging data measured during the course of learning. Using this methodology we will study brain changes that result from extensive training on complex visual tasks.

BBSRC United States Partnering Award (USPA): Birmingham, UK - Berkeley, USA.
Lifelong learning and cortical plasticity in the human brain.

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In our everyday interactions we encounter a plethora of novel situations in different social contexts that require prompt decisions for successful actions. Extracting the key information from the highly complex input of the natural world, integrating information into coherent evidence, and deciding how to interpret it is a computationally challenging task that is far from understood. The goal of the proposed collaboration is to advance our understanding of the neural basis of learning for efficient decisions in complex, novel and uncertain environments by bringing together interdisciplinary expertise in advanced computational, behavioural and brain imaging methods.

SPARC:Strategic Promotion of Ageing Research Capacity (2007-2008): Z. Kourtzi, A. Bagshaw
Research into Ageing: Categorical Decisions in the Ageing Human Brain.

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We aim to develop new tools for cognitive ageing research by combining methods from mathematics, computer science, psychology, and neuroscience. In particular, we will combine advanced mathematical approaches (i.e. machine learning) for the analysis of biological data (behavioural performance, functional brain activations) with multimodal brain imaging techniques (structural MR, functional MRI, EEG) and behavioural methods. This integration of advanced measurement and analysis methods will allow us to develop new sensitive tools for studying the variability of cognitive ageing across individuals from rapid decline to sustained high levels of performance. Our methods and findings will provide new insights in understanding life-long learning and cortical plasticity and will be potentially useful for early diagnosis and intervention in normal and pathological ageing.

Help the Aged (2007-2008): Z. Kourtzi
Research into Ageing: Categorical Decisions in the Ageing Human Brain.

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Our everyday decisions are determined by knowledge about our environments and abstract rules that allow us to interpret novel experiences in different contexts. We will examine changes in brain structure and function that underlie our ability to learn decision rules across the life span. We will combine mathematical, behavioural and non-invasive imaging methods that allow us to study the human brain in vivo. Our findings will provide novel insights in understanding the mechanisms that mediate rapid cognitive decline in some adults while graceful ageing in others and have potential implications for diagnosis and intervention in neurodegenerative disorders common in ageing.

Cognitive Systems Foresight BBSRC/EPSRC (2007-2010): Z. Kourtzi, A. Bagshaw, G. Barnes, S. Wu
Classification Decisions in Machines and Human brains.

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Our ability to extract abstract information from our experiences and group it into meaningful units (categories) is a fundamental cognitive skill for interpreting the complex environments we inhabit. How does the human brain learn about the regularities and context of novel perceptual experiences that have not been honed by evolution and development and decide on their interpretation and classification? We propose a novel interdisciplinary approach that integrates advanced multimodal imaging (fMRI, MEG, EEG) methods and state-of-the art machine learning algorithms to examine the neural architecture that underlies classification learning and decisions in the human brain. We aim to a) create an electrical-haemodynamic signal space in which neuronal assemblies and their interactions can be characterised, and b) to develop a unified algorithmic method for efficiently analyzing neural imaging and behavioural data. In particular, we will use machine pattern classifiers to define perceptual decision images that reveal the critical stimulus features on which the observers base their perceptual classifications, and neural decision images that reveal the neural selectivity, plasticity and dynamics with which these features are encoded and learnt by the human brain. Our methodological and theoretical developments will provide a) novel and sensitive tools for the assessment of the behavioural and neural signatures of perceptual decisions in neuroscience, and b) novel challenges and insights in machine learning for the optimisation of biologically-constrained algorithms with direct applications for expert recognition systems. Further, our findings will advance our understanding of the link between sensory input, neural code and human behaviour and have potential applications for studying the development of perceptual decision processes across the life span, and their impairment and potential for recovery of function in ageing and disorders of visual and social cognition.

BBSRC (2005-2008): Z. Kourtzi
Perceptual Learning of Shapes in the Human Visual Cortex

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The detection and recognition of visual objects is a vital skill for our interactions in the world. To achieve it, the brain has to group local image features into global perceptual units (objects). This process of perceptual integration is a challenging operation for the visual system as objects are often camouflaged in the cluttered environments we inhabit. The extent of the difficulty of this problem is highlighted by the fact that perceptual integration processes develop slowly (between 5 and 14 years of age) and are severely disrupted by visual deficits early in life. Recent behavioural studies have shown that learning can be a key facilitator in perceptual integration for the detection and recognition of objects in cluttered scenes. Further, neurophysiological studies suggest that learning enhances the sensitivity of neural processing. However, little is known about the role of learning in shaping perceptual integration and visual recognition processes across stages of visual analysis in the human brain. The aim of the proposed research is to use human psychophysics and brain imaging to provide significant new insight into the neural plasticity mechanisms that support behavioural improvement in perceptual integration and visual recognition.

National Institute of Health: NEI (2005-2008): B. Tjan, Z. Kourtzi
Uncertainty and the Order of Visual Processing in Cortex

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Knowing the connections between different brain regions and the direction of the flow of information along these connections is of paramount importance for understanding the functioning of the visual system both its healthy and diseased states. By adapting a body of theoretical and empirical results from basic research in low-level visual psychophysics, we propose that functional magnetic resonance imagingExample Stimuli (fMRI) can be used to image the flow of information without the need of a very high temporal resolution. With reasonable assumptions, we can show that when noise is added to a visual stimulus, the impact of this noise on neural activity will depend on the amount of nonlinear processing that occurs between the stimulus and the brain region of interest. This general effect leads to a number of fMRI-measurable quantities that we can used determine the input-output ordering between adjacent cortical areas and subregions. The goal of this study is to develop and test this novel method, based on clear-cut predictions from the underlying theory, in those cortical regions of the human visual system where large-scale connectivity is reasonably well known.