L25 - Computational Modelling Flashcards Preview

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Flashcards in L25 - Computational Modelling Deck (10)
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1
Q

What is a model?

A

Any description of a process is a model of that process – can be good or bad

2
Q

Can a model be verbal?

A

Yes – a description which does not specify a quantitative r/ship b/w elements, can be falsifiable

3
Q

All models must make wshat?

A

Testible predictions! or else it will be useless

4
Q

Why computer simulation?

A

Typical biological models use stereotyped stimuli and do not reveal the full response patterns involved in a behaviour

In many cases, the interactions between specific neurons controlling a behaviour are too difficult to determine

5
Q

Emergent properties

A

Properties of a system that cannot be predicted simply from the properties of its individual elements

  • E.g In NS, content addressable memory (e.g. know someone’s name hence we know someone’s face, sound of voice, location you met) is an emergent property of networks obeying Hebb’s rule (Discovered by Hopfield and Tank)
  • HOWEVER, content addressable memory may not always be correct as surrounds of memory overlap so you could get things mixed up
6
Q

Compartmental models

A
  • Subset of realistic models with maths descriptions
  • Has spatial relationships
  • Can activate one neuron at a time
  • Heavy comp requirements so not used for large networks
7
Q

Network Models

A

Depict the process in a network, but requires a degree of abstraction (simplification) as to the computational process of the network. It simplifies the properties of the neurons so that we may interpret data.
E.g. removal of voltage dependence

8
Q

Realistic models look at maths descriptions while abstract models?

A

–Assumptions made about specific components to obtain general rules for the system
–Can vary from completely top-down to various hybrids of realism and abstraction

9
Q

Example of Abstracted models - Integrate-and-fire neurons

A
  • Basis of most neural network models that try to simulate cognitive processes
  • Each neuron simply adds the synaptic inputs via some mathematical rule and if a threshold is exceeded an action potential is generated
  • The classic version is the McCulloch – Pitts neurons (originally an electronic model).
10
Q

Conclusions from model

A

Blocking AHP causes change in periods of activity but periods of quiescence do not change

•Accounts for effects of changing AHP on contractile activity of jejunum in presence of decanoic acid
•Requires feedback from contracting muscle and that synaptic transmission in ascending EXCITATORY pathway is fast EPSP, but is slow EPSP in descending INHIBITORY pathway
•Predicts that blocking serotonin will suppress motor activity
–Result supported by pharmacological analysis