Principles of Computational Modelling in Neuroscience

Transcription

1 Principles of Computational Modelling in Neuroscience David Willshaw Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh Edinburgh, Scotland 1

2 Perspectives of High Power Computing in Neurosciences Large projects involving massive computing efforts are being designed and discussed intensively in different fields of research, including some to model higher functions of the human brain. 1. What relevance has computer modelling in answering biological questions? 2. How does massive modelling relate to other approaches of brain research? 3. What can be expected from massive modelling? 2

3 Where I started out: Neural network modelling of associative memory Willshaw, Buneman & Longuet-Higgins (Nature, 1969) Our high performance computer: Elliott 4130 with 32K words of memory 3

4 Our recent text book on how to do computational modelling in neuroscience (2011) Computer modelling or Computational modelling? 4

5 1. What relevance has computer modelling in answering biological questions? 2. How does massive modelling relate to other approaches of brain research? 3. What can be expected from massive modelling? 5

6 What is computational modelling? Theories of the natural world lead to predictions Informal models based on theories: verbal reasoning, diagrams More formal models eg, models of biologically grounded elements specified in the language of mathematics. Quite often this ends up with calculations of numerical solutions for quantities computer simulation Computer modelling computational modelling 6

7 Why do computational modelling? As an aid to reasoning, not to replace reasoning Removes ambiguity from theories and makes them logically consistent Models that are wrong are still useful Use of computer technology enables theories involving with a large number of elements to be investigated Computational modelling can help to do the right experiment 7

8 Why do computational modelling? As an aid to reasoning, not to replace reasoning Removes ambiguity from theories and makes them logically consistent Models that are wrong are still useful Use of computer technology enables theories involving with a large number of elements to be investigated Computational modelling can help to do the right experiment 8

9 Frog visual system

10 Sperry (1963): axons/cells match up according to the labels they carry If map-making employs molecular labels, label flexibility is required

11 Christoph von der Malsburg and I took up an idea originally due to Shin-Ho Chung (1972) - to give a contrary view to Roger Sperry: We abolished the idea of specific dedicated targets for each axon Axons make contact through recognising their neighbours in a way mediated by electrical activity How patterned neural connections can be set up by self-organisation Willshaw & von der Malsburg, B, Proc. Roy. Soc. B (1976) At the time our view was not popular although now it is 11

12 Neural activity model Formulated as a simple model of neurons and connections...and activity-driven synaptic plasticity

13 Today it is thought that both neural activity and molecular recognition are involved Theories for the formation of nerve connections can be tested in mice for which the genome is known Genes that are thought to be determining developmental mechanisms can be manipulated Their effects on connectivity can be observed and compared with the model predictions Computers can be also used for sophisticated analysis 13

14 Simulated on high performance computer with 1MB memory Results validate theory Our contribution was to take the idea of activity-based neighbourmatching and show how it could work Modelling as an aid to reasoning

15 Mechanisms for the formation of topographic projections in the vertebrate visual system Slide from Uwe Drescher

16 Maps in mouse superior colliculus (Cang et al, J. Neurosci, 2008) The colour-coded noisy X, Y coordinates of each field position maximally exciting each of pixels within a 2 mm X 2 mm area of brain including colliculus Fourier- based intrinsic imaging - at each pixel the component at the scanning frequency is extracted L M I S Wild type R N C 16 T

17 Maps in genetically modified animals WT abnormal R C L M 17

18 Maps in genetically modified animals WT abnormal To develop a model that reproduces these activity patterns needs some hypotheses and analysis! R C L M 18

19 The Hodgkin Huxley (HH) model for the propagation of the nerve impulse Membrane conductance is voltage dependent. They solved numerically a set of biologically grounded equations describing the voltage-dependent changes in postulated sodium and potassium channels. A quantitative description of membrane current and its application to conduction and excitation in nerve. Hodgkin & Huxley, J Physiol (1952) Alan Hodgkin Andrew Huxley

20 The Hodgkin Huxley (HH) model for the propagation of the nerve impulse Membrane conductance is voltage dependent. They solved numerically a set of biologically grounded equations describing the voltage-dependent changes in postulated sodium and potassium channels. A quantitative description of membrane current and its application to conduction and excitation in nerve. Hodgkin & Huxley, J Physiol (1952) Alan Hodgkin Andrew Huxley

21 Computational analysis of subthalamic nucleus and lenticular fasciculus activation during therapeutic deep brain stimulation (Miocinovic et al., J.Neurophysiol., 2006) Uses our compartmental model (46 free parameters) of the STN projection neuron (Gillies & Willshaw, 2006) 21

22 Computational analysis of subthalamic nucleus and lenticular fasciculus activation during therapeutic deep brain stimulation (Miocinovic et al., J.Neurophysiol, 2006) Uses our compartmental model (46 free parameters) of the STN projection neuron (Gillies & Willshaw, 2006) 22

23 m CNS 10 cm Systems 1 cm Maps 1 cm Networks 100 µm Neurons µm Synapses A Molecules

24 Issues in modelling the nervous system 1. Formulating the question - a clear one is needed! 2. Getting good data contradictory, reproducibility 3. Deciding on the type of model at what level or levels? 4. Formulating the model at what level of detail; modelling space? 5. Matching the model structure to the computing infrastructure 6. Developing the optimisation procedure many parameter values to optimise; resource implications, uniqueness, local optima

25 7. Solving the model and addressing the question Need to discover the optimal values for a large number of parameters - thousands for multilevel models Is the solution unique? What do we know that we didn t know before? Having a model that fits the data is only the start Does the model generalise -eg, in a single neuron model do activity patterns respond appropriately to stimuli that it wasn t trained on? 25

26 1. What relevance has computer modelling in answering biological questions? Computer modelling aids reasoning and does not replace it Computer models can be wrong (Ito/Marr) It has been successful on projects with clearly defined goals Limitations in computing resource may be a problem [but in Edinburgh, with lots of HPCs, Neuroinformatics DTC faculty and students tend not to use them] 26

27 Perspectives of High Power Computing in Neurosciences 1. What relevance has computer modelling in answering biological questions? 2. How does massive modelling relate to other approaches of brain research? 3. What can be expected from massive modelling? 27

28 2. How does massive modelling relate to other approaches of brain research? Computer (rather than massive) modelling is an essential component of the neuroscientist s repertoire Computing power is not the bottleneck in neuroscience Massive (=High Performance) computer modelling has the potential disadvantage of the lack of portability of the tools developed The primary driver of neuroscience is the development of new experimental technologies such as optogenetics 28

29 Perspectives of High Power Computing in Neurosciences 1. What relevance has computer modelling in answering biological questions? 2. How does massive modelling relate to other approaches of brain research? 3. What can be expected from massive modelling? 29

30 Projects requiring lots of computing resource Human Genome Project to find the sequences of base pairs in DNA single data type a clear goal and knowledge of when to stop Weather forecasting many types of data informing a single set of equations a clear question and knowledge of when to stop Econometric modelling a clear question (at least!) Human Brain Project 30

31 Human Brain Project The goal of the Human Brain Project (HBP) is to integrate neuroscience and neural data from around the world into unifying computer models of the brain, to simulate the behaviour of these models, to develop applications for medicine and future computing, and to make these capabilities available to the scientific community (EU FET Conference November 2011) Resource implications A proposal for EU funding on a massive scale Requires large contributions from participating countries 31

32 There are many conflicting reports about what HBP will do The Human Brain Project is conceived as an extension of the Blue Brain Project, (2005--) So I looked at the 18 papers published by BBP since (>1MEuros/paper...) 32

33 2006 The first phase of BBP will be to replicate in software a column of the neocortex with 10,000 morphologically complex neurons with unprecedented detail for high-speed simulations ( As a first step the project succeeded in simulating a rat cortical column... A facility [has been developed] that can create realistic models of one of the brain s essential building blocks.the process is entirely data driven and essentially automatically executed on the supercomputer. From current Blue Brain Web site, January

34 The du Vaucanson duck 34

35 35

36 BB computer modelling - infrastructure 1. Parallel implementation of single cell compartmental model Development of the NEURON simulator to work in parallel Framework to integrate different simulators Load balancing - distribution of nerve cell segments over different processors in a HPC 2. Structural studies Reconstructing single cell morphologies from traced profiles - not yet automatic Calculating interconnectivity patterns from cell morphologies 36

37 BBP computer modelling - Databases 3. Ion Channel Data Base Channelpedia ( Frontiers in Neuroinformatics, 2012) stores annotated information about 187 ion channels For 45 of these, existing published data has been used to find the parameters for the best fitting Hodgkin-Huxley model. Just one paper per channel How good is the fit? Link to other web sites (ModelDB, pharmacological)? 37

38 BBP computer modelling - simulation studies 4. Parameter optimisation for single neuron models with up to 22 free parameters uses multi-objective genetic algorithm (burns up cycles!) comparing models on real and gold standard data variable performance Examined choice of training/test stimuli 5. Combined experimental and modelling work: extraction of principles of interconnectivity leading to a stochastic view of neural activity and interconnectivity the average neuron are averages adequate? 38

39 Questions/Issues 39

40 1. Searching for the right parameter values is like searching for a speck of dust in a galaxy <Need a graphic> 40

41 1. Searching for the right parameter values is like searching for a speck of dust in a galaxy Parameter number can be reduced by simplifying But this requires detailed work on models at many different levels to justify simplifications testing on many different types of data <Need a graphic> 41

42 2. Multilevel models are needed - how to construct them? BBP standard model is of single cells and networks of single cells Hodgkin Huxley Compartmental Network 42

43 2. Multilevel Models are needed with components at different levels and with different amounts of detail; experimental data input into all levels 43

44 Open Source Computational Neuroscience Simulators: Emergent (PDP+) BRIAN NEST GENESIS MOOSE neuroconstruct NEURON PSICS NETMORPH TOPOGRAPHICA CX3D CNS Systems Maps Networks Neurons Synapses Molecules

45 Why are BBP s tools not more widely used or discussed? The BBP BrainBuilder and BrainSimulator aren t generally used or available Expert evaluation of the Blue Brain Project (2011): Why are BBP s tools not more widely used or discussed? It is important to make use of the database of connectivity available to other scientists Perhaps the specific HPC hardware is an impediment to collaboration? The larger the project the more likely it is to fail, especially with ill-defined goals [cp. attempts to computerise England & Wales National Health Service] 45

46 3. An alternative, evolutionary, distributed scheme: many projects, each with clear goals? 46

47 4. The goal of the Human Brain Project We d all like to understand the brain But how do we know when we have done this? A clear goal is needed 47

48 NOTES 48