Computational Neuroscience
Andy Philippides
Centre for Computational Neuroscience and
Robotics (CCNR) COGS/BIOLS
andrewop@cogs.susx.ac.uk
Spring 2003
Teaching
 2 hours per week: Monday 9.15-11.05
 Nominally 1 hour lecture, 1 hour
seminar, but may vary
 Office hour: Friday12.30-1.30, BIOLS
room 3D10
 Lecture notes available online soon
Assessment:Seminar presentation 25%
• 30 mins presentation (25 mins + 5 mins questions)
• Papers chosen from the reading list. Papers of your own
choosing may be acceptable but MUST be ratified by me
• Presentations start in week 4 (order decided by subject)
• Present the ideas of the paper clearly
• Give your opinion on the strengths and weaknesses of the
ideas/research
• Be prepared to answer questions on the paper
• A little reading around the subject will usually be required
for a good presentation
• A printout of any resources used must be given to me for
marking and external assessment
Assessment: Project 75%
• Programming project to implement a computational
neuroscience model
• Due in Wed 23rd April 12 noon
• Approx 3500 words
• Some suggested projects but you can choose your own
subject to ratification by me
• More details in week 5
Course summary
Computational Neuroscience aims to understand the
mechanisms underlying brain function by building
quantitative models.
The course is intended to introduce the basic concepts of
this area and give details of some of the standard models
and approaches
Biological background will be given together with the
model though some basic knowledge is assumed
Some mathematics will be required as the majority (all?)
of the models are mathematical, but I will attempt to keep
it to a minimum
Don’t Panic about the Maths!
• You only need to know enough to understand
what is going on not how to do it
• Maths is necessarily abstract and may not be
clear at first, but this is to be expected
• It may take time (several viewings) to
understand things so be patient
Caveat
Computational neuroscience is a huge topic
and this is a short course
The lectures will not cover everything
Will try to cover the basics of a subject and
give pointers to more advanced topics/areas
of interest
Course Structure 1
1. Course introduction: What is computational
neuroscience, why is it needed, levels of modelling,
neural signalling
2/3. Single neuron models (start small and work up):
Basics of neural signalling, membrane equation, cable
theory, action potentials, Hodgkin-Huxley model,
beyond HH model
4. Networks of neurons: what neuron models are used,
how they are connected, oscillations in networks of
neurons, map formation
Course Structure 2
5. Learning: modelling synaptic learning, how
learning shapes neural networks
6. Spiking networks: Models of spiking neurons and
issues of spike timing and coincidence detection
7. Gaseous neurotransmission: mathematical models
of diffusion, more abstract models of diffusion
8. Systems level neuroscience. Examples of higher
level models (if we have enough time…)
Further reading
Purves, et al eds. Neuroscience. Sinauer, 97 (many neuroscience texts
at various levels: pick one that’s right for you)
Abbott LF & Dayan P: Theoretical Neuroscience.
MIT Press, 2001
Computational Neuroscience. Realistic Modeling for Experimentalists
By Erik De Schutter. CRC Press, 2000
Koch C & Segev I. Methods in neuronal modeling: from ions to
networks. MIT Press, 1998
Spikes, Rieke et al, 1996
Many others: see http://home.earthlink.net/~perlewitz/books.html
What is computational neuroscience?
• Mathematical modelling: the construction of quantitative
models to understand observable phenomena. Explaining
phenomena in terms of underlying mechanisms.
• Computational modelling: Modelling what the brain does in
terms of computations. Crudely, trying to understand the
brain as a computing device: a rather newer idea (eg Info
theory etc)
• Relation to AI/ALife: These try to understand computing
devices in general and how best to solve computational
problems. CN studies a particular computing device and
how it solves problems. Sometimes uses same tools (eg
neural nets) but one must be careful. Also biology provides
great deal of inspiration for Alife techniques
Why do we need models?
• When we have enough data about the brain, won't we
understand how it works? Analogy with astronomy.
• Common misuderstanding: Modelling is a form of
hypothesis testing.
• Force one to make assumptions explicit. Can only get
so far with hypotheses expressed in intuitive terms.
E.g. ``visual experience affects visual development''.
• Enables many ``virtual'' experiments to be done, can
pinpoint the one that is most crucial.
• Can lead to unexpected predictions.
• Often much quicker/easier to try out ideas eg lesioning
studies in silica rather than in vitro/vivo so can guide
potential experiments
What makes a good model?
• However, its easy to make a bad Alifey model
• Good to have close contact with
neuroscientists
• (Maybe) “Model should not only replicate
existing data but must also make predictions
about the biological system” [Editor of
biological journal] (??! Discuss)
• Roger Quinn: cockroach models normally
only informative when they don’t work
Levels of modelling
• Many different types of models: continuum from very
realistic to very abstract. All models must make
simplifications to be useful.
• E.g. model of single neuron.
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Binary threshold unit
Continuous unit
Integrate-and-fire (continuous in time)
Spiking
A few compartments
Many compartments
Individual channels
Detailed model of channel dynamics
etc
Which to use?
• Depends on purpose of the model! Different
types appropriate for different sorts of
questions. Should become clearer as we learn
about particular types of models in the course.
• Eg: Top-down vs bottom up models.
– Top-down: Start with idea about abstract task /
problem, figure out good way to solve it, see if
that's what the nervous system does.
– Bottom-up: Look closely at nervous system, try
and figure out what its doing, derive the task /
problem from there.
Models can be categorised in other ways:
• Biological perspective (Molecular, neuronal etc)
• Marr (82):
– Computational (what computation is to be performed, in
terms of optimality, modularity etc),
– algorithmic (looks at nature of computations performed)
– implementational (how algorithm is implemented)
• Abbot and Dayan (2001)
– Mechanistic: based on known anatomy and physiology
– Descriptive: summarizes large amounts of experimental
data
– Interpretive: explores behavioural and cognitive
significance of nervous system function