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Computational Neuroscience and Neuroinformatics

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Computational Neuroscience and Neuroinformatics KTH ROYAL INSTITUTE OF TECHNOLOGY Introduction to Neuroinformatics Importance of Modelling and Simulations Pawel Herman Department of Computational Science and Technology, School of Computer Science and Communication KTH Royal Institute of Technology, Sweden Neuroscience course, May 17 Why is it important to study brains? • Quest for knowledge • Computational inspiration • Brain disorders and diseases http://www.modernmedicalguide.com/alzheimers-disease/ Marja-Leena Line, INCF 2 Where are we today? • collecting more data, building more advanced models, performing more complex analyses 3 Where are we today? • collecting more data, building more advanced models, performing more complex analyses • BUT: – studying individual components of neural systems with little integration/generalisation effort – focused on a limited set of spatio-temporal scales – working on disconnected data sets using different tools, procedures, protocols – disappointing reproducibility of experimental work (much improved for simulations) – poor understanding of neural mechanisms (computational primitives) at any level of the organisation -> richer computational mindset needed 4 Where are we today? Progress is not satisfactory and the needs are immense, so are the implications of the advancement of brain science What could we do to advance the brain science? 5 Where are we today? Progress is not satisfactory and the needs are immense, so are the implications of the advancement of brain science What could we do to advance the brain science? Combine efforts, collaborate interdisciplinarily, organise and share data, integrate biological evidence, build multi-scale models etc. 6 What is neuroinformatics? NEUROSCIENCE NEUROINFORMATICS COMPUTER SCIENCE INFORMATION NEUROMEDICINE TECHNOLOGY (IT) Neuroinformatics is the mean to connect neuroscience, medical science, information technology and computer science. 7 What is neuroinformatics? NEUROSCIENCE eSCIENCE NEUROSCIENCE NEUROINFORMATICS COMPUTER SCIENCE INFORMATION NEUROMEDICINE TECHNOLOGY (IT) Neuroinformatics is the mean to connect neuroscience, medical science, information technology and computer science. 8 Fundamental goals of neuroinformatics ICT-based brain research – aims and implications • organise and integrate neuroscience data • accelerate our quest for understanding the brain • support neuromedicine, understanding brain diseases • develop brain-inspired future computing technologies, brain-like intelligent systems • promote education, deliver multi-disciplinary training. 9 Fundamental goals of neuroinformatics ICT-based brain research – fundamental goals • organise and integrate neuroscience data 10 Fundamental goals of neuroinformatics ICT-based brain research – fundamental goals • organise and integrate neuroscience data “Data tsunami” “We are drowning in information but starved for knowledge” John Naisbitt 11 Data Age ̶ Multiomic Neuroscience Data From sub-cellular resolution to whole brain resolution Sean Hill, INCF 12 Fundamental goals of neuroinformatics ICT-based brain research – fundamental goals • organise and integrate multi-level data gather existing data across scales and levels, identify missing data Sean Hill, INCF 13 Fundamental goals of neuroinformatics ICT-based brain research – fundamental goals • organise and integrate multi-level data gather existing data across scales and levels, identify missing data from genes to behaviour 14 Integrative and data-oriented neuroinformatics NEUROINFORMATICS Sean Hill, INCF 15 Focus for neuroinformatics – data, theory organise and integrate multi-level data gather existing data at develop tools for build models, simulate, multiple levels, storing, visualising develop theories identify missing data and sharing information build databases, devise new brain atlases approaches to data analysis 13 Focus for neuroinformatics – data, theory organise and integrate multi-level data gather existing data at develop tools for build models, simulate, multiple levels, storing, visualising develop theories identify missing data and sharing information build databases, devise new brain atlases approaches to data analysis 13 Computational neuroscience and neuroinformatics Marja-Leena Linne, INCF 18 Modelling – towards integrative neuroscience What is the purpose of computational modelling? adapted by A Kumar 19 Modelling – towards integrative neuroscience What is the purpose of computational modelling? • to integrate available data and build theories • to describe & understand the underlying mechanisms • to reveal causal relationships • to generate insights and predictions for experimental neuroscience, etc. adapted by A Kumar 20 Modelling in neuroscience • What is a model? Mathematical model is a description of a system using mathematical concepts - rules, mainly in terms of formulae, e.g. du τ m = −u(t) + R I(t) dt subthreshold activity in the integrate-and-fire (IF) model 14 An example of a single neuron model – HH formalism • Various levels of mathematical description – • zooming in (on details) vs. zooming out (abstraction) 15 An example of a single neuron model – rate unit • Various levels of mathematical description – • zooming in (on details) vs. zooming out (abstraction) y y = φ(Σ wi xi) φ 16 An example of a single neuron model – rate unit • Various levels of mathematical description – • zooming in (on details) vs. zooming out (abstraction) y y = φ(Σ w x ) φ i i y time time 16 Modelling strategy – bridging levels EMERGING PHENOMENA, ”top-down” HIGHER-LEVEL FUNCTION / THEORY, GLOBAL / DYNAMICS FUNCTIONAL PRINCIPLES NEURAL DETAIL, WEALTH OF NEURAL IMPLEMENTATION BIOLOGICAL DATA (DYNAMICS, ARCHITECTURE) Gerstner et al., Science 2012 ”bottom-up” 17 Modelling strategy – bridging levels EMERGING PHENOMENA, ”top-down” HIGHER-LEVEL FUNCTION / THEORY, GLOBAL / DYNAMICS FUNCTIONAL PRINCIPLES Synthetic LFP 60 50 40 30 20 Frequency (Hz) Frequency 10 NEURAL DETAIL, WEALTH OF NEURAL IMPLEMENTATION BIOLOGICAL DATA (DYNAMICS, ARCHITECTURE) 2 3 4 5 6 7 8 ”bottom-up” Time (seconds) 18 Modelling strategy – bridging levels EMERGING PHENOMENA, ”top-down” HIGHER-LEVEL FUNCTION / THEORY, GLOBAL / DYNAMICS FUNCTIONAL PRINCIPLES RESEARCH QUESTION SUITABLE STRATEGY (DATA, CONSTRAINTS) AND LEVEL OF DETAIL NEURAL DETAIL, WEALTH OF NEURAL IMPLEMENTATION BIOLOGICAL DATA (DYNAMICS, ARCHITECTURE) ”bottom-up” 19 Computational modelling approaches How can we go about modelling? David Marr’s theoretical approach Phenomenological models – Statistical models – mathematical description of description of a system in phenomena without handling terms of random variables constituent parts and their distributions (they Mechanistic models – can be mechanistic or description of a system in phenomenological) terms of its constituent parts 28 Computational mindset of David Marr Three levels of description: 1. Computational level – what does the system do? • What logic defines the nature of resulting mental 1945-1980 representations of incoming stimuli? 2. Algorithmic level – how does the system do it? • What processes are involved in building mental representations? How is input translate to output? 3. Implementation level – how is the system physically realised – implemented? • What is the neural hardware – substrate? 24 Typical modelling workflow 1. Defining the model: the research question and the research hypothesis determine the type of model, the model components, and the approach to solving the model. 2. Parameter fitting: complex high-dimensional models (biophysical) often have a huge parameter space that cannot be fully explored, instead parameters are fitted from the data, available data influences construction of the model. 3. Simulation: model is implemented in the suitable simulator(s), the obtained simulation results are analyzed and visualized. 4. Validation: the model is confronted with more experimental data, the model behaviour should correspond to the modelled biological system (at least qualitatively). 5. Prediction: good models have predictive power, when additionally perturbed they can show the behaviour of the system under the new conditions. 30 My general modelling philosophy abstract and conceptual functional models Model/simulate functional aspects cognitive phenomena, behavioural Develop or build effects on a theory constrain translate Implement neural anatomy, substrate signal recordings detailed models with neural dynamics 31 My general modelling philosophy Model/simulate functional aspects Recurrent associative memory Develop or build (Hopfield, 1982) on a theory Implement neural substrate Cortical attractor networks 32 Cortical attractor memory model example Cortical column Hopfield network Cortical attractor model Local RSNP Distant pyramidal Local basket cell Local pyramidal Hypercolumn with columns 20 From abstract to biologically detailed implementation • individual neurons • neural populations • cortical columns mapping (Mountcastle et al., 1955) to biology Hopfield recurrent neural network (the concept of a cell assembly, (horizontal connections in the cortical Hebb’s association) layer 2/3 implementing recurrency) 34 Biologically detailed cortical models HYPERCOLUMN MINICOLUMN Modular structure with hypercolumns consisting of minicolumnar units (distributed patterns) 35 Biologically detailed cortical models Attractor networks HYPERCOLUMN MINICOLUMN ~1.5 mm 36 Cortical attractor memory model Hopfield network Cortical mapping attractor model to biology Cortical patch 20 Cortical memory function completion 0 1 2 3 4 5 6 7 seconds bistability, competition Lundqvist,
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