Cerebellar Model Flashcards
What is the main theory of motor learning?
Marr-Albus Framework
What does the Marr-Albus framework try to do?
Explain how the cerebellum is important for motor control
What are the levels in the Marr-Albus framework?
- Computational level (task analysis)
- Algorithmic level (method)
- Implementational level
What is the computational level?
Complex information processing problems need to be modelled at more than one level - the most abstract is the computational level
–> What problem is the cerebellum trying to solve?
Framework associated with David Marr
What is associated with the computational level?
Task analysis
Explain task analysis
Start with a simple task - target shown to a pp
Target appears on the retina 10 degrees to the right
Natural thing is to move the retina to look at the target
To do this we need to know how big a command to send to the muscles
Eventually, we need to know how big a command to send to the muscles wherever the target is located
How are motor commands learnt?
Algorithmic level
For the cerebellum - precise motor commands are learnt using supervised learning
What is supervised learning?
Supervised learning is characterized by teaching signals that can report whether or not expectations match outcomes (i.e. a yes or no signal)
Is supervised learning a plausible explanation?
Yes - as adults we make accurate movements ‘naturally’
But babies spend an enormous amount of time learning about movements
How can the correct value of motor command be learnt?
Use an error signal
What is the learning rule?
If movement is too small, increase the command
If too big, reduce the command
If accurate, do nothing
How can the cerebellum carry out the learning rule?
- Parallel fibres signal the location of the target
- When a parallel fibre fires, so does its target Purkinje cell (the motor command)
- How much the Purkinje cell fires depends on the weight of the synapse between the parallal fibre and the Purkinje cell
- Weight of synapse adjusted by climbing fibre signal
- Increases weight if movement too small, decreases if movement too big
- Eventually learn how big a command to send to the muscles wherever the target is located
What is the function approximation method?
Function approximation helps finding the value of an action when similar circumstances occur
This method learns to approximate the function that relates motor command to retinal position
What is the function approximation method an example of?
Supervised learning - the error signal indicates which direction to alter the command
Is this a plausible method (function approximation)?
Explains why there might be so many granule cells
A lot of factors can affect the size needed for an accurate motor command - need for constant practice
So a lot of information is needed to get the movement just right
Also explains why the climbing fibre input is so unusual - low frequency (error signals should not drive output), very reliable, all synapses between granule cells and Purkinje cells affected - just what is wanted from an error signal
What is not explained using the function approximation method?
No mention of what granule cells actually do to mossy fibre inputs - ‘expansion recoding’ not explained
Learning rule is in fact slightly more complicated
- If PF signal positively correlated with CF signal, decrease weight
- If PF signal negatively correlated with CF signal, increase weight
What is the decorrelation rule?
Learning ceases when no correlation between any PF input and the CF signal
Also known as Least Mean Square, delta learning rule
How does the learning rule apply to NMR conditioning?
The initial movement is too small - in fact no blink at all
The error signal (US) therefore needs to increase movement size
Because of the circuitry, this means the US should teach Purkinje cells to fire LESS
Describe the cerebellum and NMR conditioned reflex pathway
Information about the conditioned stimulus arrives at the cerebellum via mossy fibres
Information about the unconditioned stimulus arrives at the cerebellum via climbing fibres
Light/sound –> pontine nuclei –> mossy fibres –> cerebellum
Error signal = sent to inferior olive –> climbing fibres –> cerebellum
Cerebellum –> Cerebellar nucleus efferents –> Red nucleus
Explain the cerebellum before conditioning
Error signal is the US i.e., air puff to eye, sent by the inferior olive
CS is the tone, via the mossy fibres
CR is produced by a change in Purkinje cell firing
CS produces no change in firing rate of the Purkinje cells
CS produces no change in cerebellar nuclei firing rate so no conditioned response
Explain the cerebellum during conditioning
CS tone decreases PC firing rate and increases the cerebellar nuclei firing rate creating the conditioned response
Parallel fibre CS is now paired with climbing fibre US
This pairing produces long term depression (LTD) in the excitatory synapses between parallel fibres and Purkinje cells
Makes functional sense - CS predicts being hit in the eye i.e., ERROR - do it less
This is a synaptic process, which the model can connects to function
Following the Marr-Albus framework, what is the predicted site of plasticity?
Synapses between granule cells and Purkinje cells
How does the cerebellar chip apply to NMR conditioning?
Same basic circuit used for eyeblink conditioning as for movements in general
Thus, a cerebellar model used to explain role in motor control should also apply to classical conditioning
