Mathematical modelling, Bridging the gap between Mathematicians and Experimentalists
My talk will walk through some of the research directions I am advancing in the modelling multi-scale spatio-temporal dynamics in the Brain but also novel techniques that could advance experimental neuroscience. The talk also attempts to bridge the gap between Mathematicians and Experimentalists (in Neuroscience) to enable fruitful discussions and future collaborative work.
In the first part I will briefly sketch the idea of finding invariances from multi-scale neuronal observations and determining boundaries between normal and pathological brain activity. This is followed by an example with my work in (macro-scale) modelling of absence seizures and demonstrates the feasibility of classifying sub pathologies but also importantly gives insight as to how seizures could be predicted.
The second part, is inspired by the work of Thomas Südof (2013 Nobel Prize in Physiology and Medicine) on asynchronous neurotransmitter release. We develop a multi-scale model linking the bio-molecular machinery of exocytosis, neurotransmitter release and electrophysiology. The model is able to predict, synchronous, asynchronous and spontaneous neurotransmitter release as captured in dual whole cell recordings (in both CCK cells and Calyx of Held neurons). The phenomena is underpinned by a novel mathematical structure but also hypothesises the proteins involved in the delay of neurotransmitter release (which could be validated in future experiments).
The third part shows how mathematics could help experimentalists to improve closed loop experiments (for example, dynamic-clamp electrophysiology possibly also involving optogenetics) by combining key mathematical techniques (with control theory) to determine in real time regions of activity that are typically in-accessible to the experimentalists. This is a work I would hope to pursue with the Achucarro Neuroscience Centre, which would lead to un-precedented data but also the development of novel technologies, such as clever deep brain-stimulators that could potentially predict the onset of seizures.
The final part of the talk, touches lightly upon the idea that certain brain structures perform Turing computations and it offers the possibility to decode the neural code (for these brain structures alone). This also offers to bridge the gap between Mathematicians and Experimentalists, where information from this mapping could guide new experiments.