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.

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

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 w x ) φ i i

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

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, Herman et al. (2011) J Cogn Neurosci Herman, Lundqvist et al. (2013) Brain Research

21 Oscillatory dynamics in the model

Mesoscopic scales

22 Oscillatory phenomena

Synthetic LFP 60 50 40 30 20 Frequency (Hz) Frequency 10

2 3 4 5 6 7 8 Time (seconds)

Herman, Lundqvist et al. (2013) Brain Research Lundqvist, Herman et al. (2011) J Cogn Neurosci

22 Large-scale simulations

Hodgkin-Huxley

TOOLS

41 A holistic computational model of mammalian olfactory system

receptors and olfactory receptor cells

Buck and Axel, 1991

42 A holistic computational model of mammalian olfactory system

olfactory bulb

Benjaminsson, Herman et al.(2012) Buck and Axel, 1991

43 A holistic computational model of mammalian olfactory system

olfactory cortex

Benjaminsson, Herman et al.(2012) Buck and Axel, 1991

44 Abstract model vs spiking detailed model

Benjaminsson, Herman et al.(2012) Kaplan et al., 2014

45 Abstract model of mammalian olfaction

A wide spectrum of results

Benjaminsson, Herman et al.(2012)

46 How do computational models help?

• Integrate (and fit) experimental data • Describe neural systems – provide mechanistic understanding of the neural system • Make predictions about the system behavior in new conditions • Provide new ways to study brain diseases Marja-Leena • Provide principles to develop new technology (brain-like computing, neuromorphic systems, control for robots)

47 Future challenges for computational neuroscience

• Design of biologically realistic models that span over many levels of spatial organization and a wide range of temporal scales • The need for development of multi-scale interoperable simulation tools (e.g. MUSIC) • Further advancement of simulation technology allowing for interactive control with visualisation capabilities • Enforcing tighter links with biology – interactive and iterative process that deeply involves experimental work

48 Trends and future outlook – what do we need for that?

• more theory and simulations

• tighter connections with experimentalists (from genes to behaviour)

• computational power for large-scale massively parallel simulations

23 Trends and future outlook – what do we need for that?

• more theory and simulations

• tighter connections with experimentalists (from genes to behaviour)

• computational power for large-scale massively parallel simulations

• tools for simulations, analysis and visualisation

MOOSE

23 Thank you for attention

QUESTIONS ?