Week 10: flashcards
List of stuff we learned with models (7)
- Spinal cord (HH)
- WM (IF)
- HD (Rate)
- Hopfield network, memory matrices, spatial memory models (connecitonist neurons)
- Learning rules (Hebbian, competitive, BCM)
- Perceptron (supervised learning)
- Reinforcement learning (Q-learning, TD learning, model-based)
Marr’s 3 levels
Example to introduce framework of Marr’s 3 levels is trying to understand a computer do arithmetic? (3) understand at 3 levels
- Computational level
- Algorthmic level
- Implmenation level
At computational level to try and understand how a computer does an arithmetic
How do Arithmetic like a+b = c
At algorithim to try and understand how a computer does an arithmetic (2)
like particularly way we cast the problem and solve it
000100101 + 00111101 = 01101101
At implementation level to try and understand how a computer does an arithmetic (2)
How does it implement the solution in hardware like transistors on chips on computer
David Marr originally proposed 4 levels but
levels 3 and 4 a commonly combined
Marr wrote 3 extremely influential (still today)
papers on cortex, cerebellum, hippocampus and influential theory of vision
Marr’s 3 levels can be used to classify
the models we learned in the model
Marr’s 3 levels applied to lamprey locomotor
Lamprey locomotor network focuses on
how does the lamprey spinal cord control swimming (generating a travelling wave of muscle activation)
Marr’s 3 levels applied to lamprey locomotor
Computational level problem
Has to generate rhythmic activity with a delay between spinal cord segments
Marr’s 3 levels applied to lamprey locomotor
Algorithmic level
We use coupled osciliators to do this
There is multiple realisability at algorthmic and implenetation level meaning (2)
More than one algortihm can exist for a given computaiton
More than one implementaiton can exist for a given algorithm
Marr’s 3 levels
Algorithm level = couples osilicators for lamprey locomotor
Example of ‘meaning’ flashcard (more than 2 algorithms for a given computation, more than one implementaiton can exist for a given algorithm)
Two ways of producing generating osciliations at algortthmic level (3)
Two ways of producing osciliations at algorithmic level
* Escape from inhbition (w3)
* Alternating we could have instrinic osciliations with bursting neurons (neurons fire a burst of spikes and shut down on their own)
*
Marr’s 3 levels
Algorithm level = couples osilicators for lamprey locomotor
Example of ‘meaning’ flashcard (more than 2 algorithms for a given computation, more than one implementaiton can exist for a given algorithm)
Two ways of producing generating osciliations at algortthmic level = how do we decide which one is a better model? (3)
With experiments! Cut the connection between hemi-segments in fictive locomotion experiment.
Are we still get osciliations? Are they still bursting?
If we do then might suggest we have individual bursting capability in hemi-segment
Marr’s 3 levels
Example of ‘meaning’ flashcard (more than 2 algorithms for a given computation, more than one implementaiton can exist for a given algorithm)
Example: multiple ways of generating osciliations at implementation level (4)
- Escape from inhbition
- Vary type of inhbition between two sides = Glycinergic or Gabaergic inhbition
- OR
- For instrinic osilications (if we chose that algorithm) we could have instrinic osciliations with bursting neurons, with ion channel config 1; instrinic osciliations with bursting neurons with ion channel config 2 (more than one congif of ionic channels can lead to bursting)
How to decide the multiple ways to producing osciliations at implementation level for instance for lamprey locomotor network?
With experiments, record the ion channels, check if there is sAPH and which channel is responsible etc..
When you model, what is considered implentation level (in real brain) is in part (2)
determined by your chosen model
You can not model ion channels with IF neurons but yu could (within the scope of the model) say something about the implementation level
Spike-frequency adapation mechanism recap (4)
- Spike-frequency adapation interval without spikes becomes larger with time
- Fewer inhbitatory APs arrive at contra-lateral side
- Other side has time to be active and inhbiti previously active side
- Escape from inhbition
Spike-frequency adapation due to
sAPH which is consequence of Ca+ flowing in each AP and Ca trigger activation of KCa which is hyerpolarising so harder for next spike to be emitted
Example: Marr’s 3 levels applied to WM
Quesiton is
How does the brian generate/implement WM?
Example Marr’s 3 levels to WM
Computational level
Maintain information in population of neurons on short time scales
Example Marr’s 3 levels to WM
We can think of two algorithms
One with oscilaitions (e.g., Lisman-Idiart model) vs attractors (persistent activity = cell maintain own activity level via recurrent conenctions)
Example Marr’s 3 levels to WM
Implenetation levels for osciliation based WM (3)
Idiart and Lisman: Specific osciliations where they come from, underpinning terms of ionic channels, ADP
OR
Various possible attractor network implenets = similar to ring attractor entworks
Vehicle 1 which has a
Sensor directly conneected to the motor
In Vehicle 1 properities (3)
The sensor drives the motor
The strength of the signal to the motor depend on the intensity of sensory signal
Say, the sensor senses the concentration of nutrients, or oxygen, or the intensity of light
In Vehicle 1 linear relationship between
motor signal and sensor signal
How might Vehicle 1 move? (3)
- Vehicle move faster when light intensity increases (fast)
- Speed up and slow down randomly according to sensory signal
- It might change direction due to external forces but not of its own volition
Vehicle 2 can have - (3)
- Ipi-lateral connections (A)
- Contra-lateral connections (B)
- Both (c) = same as V1
Vehicle 3 of adding
different ‘neuro transmitter’ => (implying) change the positive connection from sensory to a negative inhbitatory connection
What types of sensors does Vehicle 3C have? (4)
Light (+ uncrossed)
Temperature (+ crossed)
Oxygen (- crossed)
Nutrient density (- uncrossed)
What behaviour does vehicle 3C manifest? (3)
It dislikes heat (turns away from hot places)
It hates light bulbs and attacks them head on
It prefers well-oxygenated areas (slows down) and areas rich in nutrients (if both are low, it speeds up to get out of there
Up to vehicles 1,2,3, 3C we had sensory of this kind: (4)
- more/of X the faster/slower the wheels turn
- More sensor signal less motor signal
- More motor signal more sensory signal
- Oddly limiting so maybe more up to some point then less = vehicle 4A (introduce non-linear mapping)
