Prediction mechanisms of the brain: a computational taxonomy

Our brains receive a continuous torrent of unsorted, noisy data from the senses - changing patterns of light, distributions of sound frequencies, and signals from our skin and muscles about the position and movements of the body. Yet we perceive an ordered and meaningful world of objects, speech and music (not patterns of light and sound energy), in which we produce actions directed towards behavioural goals (rather than outputting jumble of limb locations and muscle tensions). To create order from chaos, the brain is continually constructing and refining models of the sensory world and the animal's or person's actions in it. These models simplify information processing by capturing the meaningful structure in the external world and ignoring information that is unstructured, meaningless or irrelevant. In this sense we can think of brains almost like scientists, actively trying to understand and predict the world around them by simplifying it down to its core elements.
In my research, I am trying to answer some rather general questions about how the brain constructs internal models that accurately capture the structure of the external world. I will be targeting three information processing challenges that brains face again and again in different contexts, and trying to work out if the brain has dedicated systems for solving these problems, and if so, how those systems work:
- How does the brain focus on relevant variation in stimuli (such as the rate at which cars are approaching a pedestrian crossing) and filter out irrelevant variation (such as the colour of the cars)?
- How does the brain optimise processing of relevant stimuli (for example, focussing attention on stimuli where similar stimuli have very different meanings, and so careful processing is required?
- How does the brain determine how much to change its expectations about the world, as the environment changes? If I experience a surprising event (such as an increase in traffic on my route to work), how much should I update my expectations for future occasions? Can I voluntarily set my brain to learn faster (for example, if I expect a change in traffic flow because I know that construction work is taking place) and to forget again (when the construction work is over)?

My approach to understanding how the brain meets these challenges is to develop algorithms (implemented in computer programmes) that behave in a similar way to human brains in solving these challenges, and then to work out how these algorithms could be computed by populations of neurons in a real brain. Then to test whether the algorithms are correct (that is, whether the brain really works that way), I use my neural network models to generate new predictions about how people will behave in experiments, and how their brain activity will change in different circumstances. I measure brain activity using non-invasive imaging techniques such as MRI.
Some of my models also make predictions about how different brain chemicals (neurotransmitters and neuromodulators) affect the processing of information by the brain. To test these hypotheses, I will give healthy volunteers small doses of drugs that affect neurotransmitter concentrations and observe the resulting changes in their behaviour and brain activity.

The proposed research aims to develop a computational characterization of the roles of different brain systems in the formation and tuning of predictive models of the environment. The central theme of the research is to investigate the notion that the involvement of certain brain structures in a diverse range of cognitive processes can be understood in terms of formally equivalent computational functions provided in a range of superficially different cognitive contexts.
I will focus on three computational challenges that the brain faces when forming predictive internal models of the environment, and which arguably cut across multiple behavioural and cognitive contexts: How to focus on the relevant dimensions of content and ignore irrelevant variation (computation 1: marginalization)? How to focus processing resources within a dimension to reflect the structure of the perceptual world, or task demands (computation 2: reparameterization)? How to balance the need for stability in predictive models with the need to incorporate new information about a changing environment (computation 3: stability/malleability of representations)?
For each identified challenge, I will develop models of the core computational processes at both an algorithmic level to predict behaviour, and a biophysical level to predict brain activity. An important part of the biophysical modelling will be to incorporate physiological characteristics specific to the brain systems underlying each computation, to explain why these particular brain structures are suited for the computations they perform. To test the models, I will use a combination of imaging paradigms (using fMRI and MEG) that are carefully designed to test hypotheses about the neural code underlying brain activity (e.g. MVPA and cross-stimulus suppression techniques). I will also test specific hypotheses about the roles of neuromodulators by combining brain imaging with pharmacological manipulations in healthy volunteers.

The proposed research is basic (non-applied) science and therefore the impact beyond academia will be realized over the longer term. However through laying down basic theory in the field of neuroscience, the intention is to create a robust foundation for applied work which will have value for many years to come.

In addition to the academic impacts of the work (see academic beneficiaries) the intended impacts are as follows:

Health
Disorders of brain function, both in psychiatry and neurology, represent an important and increasing proportion of health problems in the Western world. The proposed research is basic science directed towards understanding the relationships between brain physiology and brain function. Although the research is not directed towards modeling any particular disorder, it is very likely that the models developed during the fellowship could be applied in clinical research. For example, models of the role of acetylcholine in category formation and maintenance in prefrontal cortex could have applications for the understanding of diverse disorders including Alzheimer's disease and ADHD (both of which have a cholinergic aspect and elements of their symptomology that could be characterized in terms of disordered categorical processing). Models of the role of dopamine and noradrenaline in determining the stability of internal models could similarly have applications in the understanding of psychological disorders in which these neuromodulators are disordered, including depression, anxiety and schizophrenia. I note that CDA fellows are encouraged to apply for further grant support during the course of their award and therefore once some of the basic modeling and validation work is underway, I will be looking to develop clinical collaborations to test the application of these models to clinical populations.

Society
Neuroscience research is, quite simply, interesting to the general public. Findings in neuroscience continue to attract media attention (for example, at the time of writing a paper in social neuroscience [Mascaro et al 2013 doi: 10.1073/pnas.1305579110] using methods developed here at FMRIB has been the top hit for all news on the BBC website for 48 hours straight). Whilst recognizing the need to report research responsibly and without over-sensationalizing findings to attract maximal media attention, I do believe it is important to maintain public interest in science as a valuable part of society and culture. Several of my recent studies [CV refs 10,11,13,17] were ranked in the top 5% of published articles in terms of interest in new and social media [www.altmetric.com].

Economic
Education and research are thriving export sectors for the UK, in which our small country punches well above its weight internationally. By basing the fellowship in a university environment in which I am actively involved in teaching, I will contribute to this economic benefit.