Neuroinformatics at Edinburgh Nigel Goddard Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh

Neuroinformatics Computational Models information-processing in the nervous system Computational Models Neuroinformatics consists of three themes. The first is computational modelling of information prrocessing in the nervous system. This, BTW, exemplifies both the difference with bioinformatics and the reason Neuroinformatics is so exciting. Here, the object of study is the most flexible, powerful and adpative computational device that we know about, with many incredible capabilites which computational models can be used to investigate. Computational modeling has benefits both for understanding the nervous system and for generating new ideas for computation in general

Computational Modelling CNS Basal Ganglia Systems Maps Networks A major challenge in understanding information processing in the brain is to integrate models of function across a range of levels. One brain region we are studying is the basal ganglia, a set of interconnected nuclei deep below the cerebral cortex which are implicated in motor function and dysfunction. For example, modelling at the systems level can give us new insights into Parkinson’s disease. Under simulated Parkinson’s conditions, the neural activity in the subthalamic nucleus and the globus pallidus oscillates at the frequency of Parkinsonian tremor. At a lower level, a model of a synaptic triad can help us understand how the basal ganglia learns motor sequences. Here the dopamine-carrying neurons, shown n purple, modulate the connection to be potentiated rather than depressed. At another level, this learning of motor sequences has been modelled theoretically with reinforcement learning techniques, and begins to indicate how higher-level planning capacities could be based on early motor development. David Willshaw will answer further questions on this theme LTD LTP Neurons Synapses Molecules

Neuroinformatics Computational Models Neural Engineering information-processing in the nervous system Computational Models inspire new hardware and software methods Neural Engineering One of the grand challengesis to utilise effectively the new generation of silicon devices which will come onstream later in this decade. Neural engineering takes ideas from computational models of informaton processing in neural systems to inspire new hardware and software methods. For example, conside what happens as devices get smaller…

Silicon Device Size 2000 2010 50 nm 500 nm Right now we have devices at the 500 nm spatial scale In 2010, we’ll have devices at the 50nm scale. Consider the consequences Requires brain-like computation

Neuron-Silicon Interface New techniques enable patch-clamp spacing of 100 m emergent properties of networks mass assays prosthetics Another research area in Neural Engineering is interfacing silicon to biological tissue. A new method under development at Edinburgh will enagle patch- clamping every 100 microns. This will allow us to study, for the first time, emergent behaviour of networks of real neurons. It will also enable mass assays of candidate drug compounds. And eventually prosthetic devices for implanting in biological nervous tissue. So that’s the Neural engineering theme, Alan Murray can answer questions.

Neuroinformatics Computational Models Neural Engineering information-processing in the nervous system Computational Models inspire new hardware and software methods Neural Engineering collect, analyze, archive, share, simulate and visualize data and models Software Systems The third subarea of Neuroinformatics corresponds most closely the work in Bioinformatics, namely the development of software systems to aid the computational modeling and neural engineering endeavours. This includes techniques for acquisition of experimental data, its analysis, storage in databases, environments for sharing data and models, simulation packages for exploring models, and visualisation methods for neural data and models.

Large Scale Simulation and Visualisation from neuron… … to brain … to network … For example, our work on large-scale simulation is exploiting parallel computers and grid technologies in collaboration with the national e-Science Centre. Here we see a visualisation of a model of a single Purkinje cell, and here embedded in a cerebellar network model. Our pioneering realtime visualisation of functional brain imaging data using parallel computers is enabling a whole new class of experiments.

Collaborative Tools interoperatiblity intelligent cataloguing Neuroscience has become a highly collaborative activity, and we are working on a variety of tools to support collaboration, including interoperability of simulators, databases and analysis packages, intelligent caltalogouing of data and models, and an electronic lab book which will automatically publish data to a web-accessible database. Some of this work is highly generic –our interoperability and cataloguing approach will be widely useable for the Semantic Web. This is my area of expertise and I’ll be happy to answer further questions later. interoperatiblity intelligent cataloguing electronic lab book

Neuroinformatics At the intersection of brain sciences and informatics… Computational Models Neural Engineering Software Systems The third subarea of Neuroinformatics corresponds most closely the work in Bioinformatics, namely the development of software systems to aid the computational modeling and neural engineering endeavours. This includes techniques for acquisition of experimental data, its analysis, storage in databases, environments for sharing data and models, simulation packages for exploring models, and visualisation methods for neural data and models. … bringing benefits to health, education, and technology.