Introduction to Neuroinformatics - kth.se · PDF file– poor understanding of neural...

KTH ROYAL INSTITUTE OF TECHNOLOGY

Pawel Herman

Department of Computational Science and Technology,

School of Computer Science and Communication

KTH Royal Institute of Technology, Sweden

Introduction to Neuroinformatics Importance of Modelling and Simulations

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/

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Marja-Leena Line, INCF

Where are we today?

• collecting more data, building more advanced models, performing more complex analyses

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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

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Where are we today?

What could we do to advance the brain science?

Progress is not satisfactory and the needs are immense, so are the implications of

the advancement of brain science

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Where are we today?

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.

Progress is not satisfactory and the needs are immense, so are the implications of

the advancement of brain science

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What is neuroinformatics?

Neuroinformatics is the mean to connect neuroscience, medical science, information technology and computer science.

NEUROSCIENCE

COMPUTER SCIENCEINFORMATION TECHNOLOGY (IT)

NEUROINFORMATICS

NEUROMEDICINE

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What is neuroinformatics?

Neuroinformatics is the mean to connect neuroscience, medical science, information technology and computer science.

NEUROSCIENCE

COMPUTER SCIENCEINFORMATION TECHNOLOGY (IT)

NEUROINFORMATICS

NEUROMEDICINE

NEUROSCIENCE eSCIENCE

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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.

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ICT-based brain research – fundamental goals


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“Data tsunami”

“We are drowning in information but starved for knowledge” John Naisbitt

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Data Age ̶ Multiomic Neuroscience Data

From sub-cellular resolution to whole brain resolution Sean Hill, INCF

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• organise and integrate multi-level data


gather existing data across scales and levels,

identify missing data

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Sean Hill, INCF


• organise and integrate multi-level data


gather existing data across scales and levels,


from genes to behaviour

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Integrative and data-oriented neuroinformatics

NEUROINFORMATICS

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Sean Hill, INCF

organise and integrate multi-level data

Focus for neuroinformatics – data, theory

gather existing data at multiple levels,


devise new approaches to data analysis

develop tools for storing, visualising

and sharing information

build databases, brain atlases

build models, simulate, develop theories

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Computational neuroscience and neuroinformatics

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Marja-Leena Linne, INCF

Modelling – towards integrative neuroscience

What is the purpose of computational modelling?

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adapted by A Kumar

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.

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adapted by A Kumar

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.

)()( tIRtudtdu

m +−=τsubthreshold activity in the

integrate-and-fire (IF) model

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An example of a single neuron model – HH formalism

• Various levels of mathematical description – • zooming in (on details) vs. zooming out (abstraction)

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An example of a single neuron model – rate unit


y = φ(Σ wi xi) φ y

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An example of a single neuron model – rate unit


y = φ(Σ wi xi) φ

time

y

time

y

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Modelling strategy – bridging levels

THEORY, GLOBAL / FUNCTIONAL PRINCIPLES

NEURAL IMPLEMENTATION

(DYNAMICS, ARCHITECTURE)

”top-down”

NEURAL DETAIL, WEALTH OF BIOLOGICAL DATA

EMERGING PHENOMENA, HIGHER-LEVEL FUNCTION /

DYNAMICS

”bottom-up” Gerstner et al., Science 2012

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”top-down”

Synthetic LFP

Fre

qu

ency

(H

z)

10

20

30

40

50

60

2 3 4 5 6 7 8Time (seconds)



DYNAMICS

”bottom-up”

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”top-down”



DYNAMICS

”bottom-up”

SUITABLE STRATEGY AND LEVEL OF DETAIL

RESEARCH QUESTION (DATA, CONSTRAINTS)

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Computational modelling approaches

How can we go about modelling?

Phenomenological models – mathematical description of phenomena without handling constituent parts

Mechanistic models – description of a system in terms of its constituent parts

Statistical models – description of a system in terms of random variables and their distributions (they can be mechanistic or phenomenological)

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David Marr’s theoretical approach

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 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?

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1945-1980

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.

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My general modelling philosophy

Develop or build on a theory

Model/simulate functional aspects

Implement neural substrate

abstract and conceptual functional models

detailed models with neural dynamics

translate constrain

cognitive phenomena, behavioural

effects

anatomy, signal

recordings

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My general modelling philosophy

Develop or build on a theory

Recurrent associative memory (Hopfield, 1982)

Cortical attractor networks

Model/simulate functional aspects

Implement neural substrate

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Cortical attractor memory model example

Local basket cell

Local pyramidal

Local RSNP

Distant pyramidal

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Cortical column

Hopfield network

Hypercolumn with columns

Cortical attractor

model

From abstract to biologically detailed implementation

Hopfield recurrent neural network

mapping to biology

• individual neurons

• neural populations

• cortical columns (Mountcastle et al., 1955)

(the concept of a cell assembly, Hebb’s association)

(horizontal connections in the cortical layer 2/3 implementing recurrency)

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Biologically detailed cortical models

MINICOLUMN

HYPERCOLUMN

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Modular structure with hypercolumns consisting of minicolumnar units (distributed patterns)

Biologically detailed cortical models

MINICOLUMN

HYPERCOLUMN

~1.5 mm

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Attractor networks

Cortical attractor memory model

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Hopfield network

Cortical patch

Cortical attractor

model

mapping to biology

Cortical memory function

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

0 1 2 3 4 5 6 7seconds

completion

bistability, competition

Oscillatory dynamics in the model

Mesoscopic scales

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Oscillatory phenomena

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

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Synthetic LFP

Freq

uenc

y (H

z)

10

20

30

40

50

60

2 3 4 5 6 7 8Time (seconds)

Large-scale simulations

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Hodgkin-Huxley

TOOLS

A holistic computational model of mammalian olfactory system

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receptors and olfactory receptor cells

Buck and Axel, 1991

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olfactory bulb


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

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Buck and Axel, 1991

olfactory cortex


Benjaminsson, Herman et al.(2012)

Abstract model vs spiking detailed model

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

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A wide spectrum of results

Abstract model of mammalian olfaction

Benjaminsson, Herman et al.(2012)

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

• Provide principles to develop new technology (brain-like computing, neuromorphic systems, control for robots)

Marja-Leena

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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

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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

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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

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Thank you for attention

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QUESTIONS ?

Introduction to Neuroinformatics - kth.se · PDF file– poor understanding of neural...

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