Week 10: flashcards

Question 1

List of stuff we learned with models (7)

Answer 1

1. Spinal cord (HH)
2. WM (IF)
3. HD (Rate)
4. Hopfield network, memory matrices, spatial memory models (connecitonist neurons)
5. Learning rules (Hebbian, competitive, BCM)
6. Perceptron (supervised learning)
7. Reinforcement learning (Q-learning, TD learning, model-based)

Question 2

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

Answer 2

1. Computational level
2. Algorthmic level
3. Implmenation level

Question 3

At computational level to try and understand how a computer does an arithmetic

Answer 3

How do Arithmetic like a+b = c

Question 4

At algorithim to try and understand how a computer does an arithmetic (2)

Answer 4

like particularly way we cast the problem and solve it

000100101 + 00111101 = 01101101

Question 5

At implementation level to try and understand how a computer does an arithmetic (2)

Answer 5

How does it implement the solution in hardware like transistors on chips on computer

Question 6

David Marr originally proposed 4 levels but

Answer 6

levels 3 and 4 a commonly combined

Question 7

Marr wrote 3 extremely influential (still today)

Answer 7

papers on cortex, cerebellum, hippocampus and influential theory of vision

Question 8

Marr’s 3 levels can be used to classify

Answer 8

the models we learned in the model

Question 9

Marr’s 3 levels applied to lamprey locomotor
Lamprey locomotor network focuses on

Answer 9

how does the lamprey spinal cord control swimming (generating a travelling wave of muscle activation)

Question 10

Marr’s 3 levels applied to lamprey locomotor

Computational level problem

Answer 10

Has to generate rhythmic activity with a delay between spinal cord segments

Question 11

Marr’s 3 levels applied to lamprey locomotor

Algorithmic level

Answer 11

We use coupled osciliators to do this

Question 12

There is multiple realisability at algorthmic and implenetation level meaning (2)

Answer 12

More than one algortihm can exist for a given computaiton
More than one implementaiton can exist for a given algorithm

Question 13

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)

Answer 13

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)
*

Question 14

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)

Answer 14

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

Question 15

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)

Answer 15

• 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)

Question 16

How to decide the multiple ways to producing osciliations at implementation level for instance for lamprey locomotor network?

Answer 16

With experiments, record the ion channels, check if there is sAPH and which channel is responsible etc..

Question 17

When you model, what is considered implentation level (in real brain) is in part (2)

Answer 17

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

Question 18

Spike-frequency adapation mechanism recap (4)

Answer 18

1. Spike-frequency adapation interval without spikes becomes larger with time
2. Fewer inhbitatory APs arrive at contra-lateral side
3. Other side has time to be active and inhbiti previously active side
4. Escape from inhbition

Question 19

Spike-frequency adapation due to

Answer 19

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

Question 20

Example: Marr’s 3 levels applied to WM
Quesiton is

Answer 20

How does the brian generate/implement WM?

Question 21

Example Marr’s 3 levels to WM
Computational level

Answer 21

Maintain information in population of neurons on short time scales

Question 22

Example Marr’s 3 levels to WM
We can think of two algorithms

Answer 22

One with oscilaitions (e.g., Lisman-Idiart model) vs attractors (persistent activity = cell maintain own activity level via recurrent conenctions)

Question 23

Example Marr’s 3 levels to WM
Implenetation levels for osciliation based WM (3)

Answer 23

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

Question 24

Marr’s 3 levels of HD
Computational Level:

Answer 24

How does the brain generate head-direction?

Question 25

Marr's 3 levels of HD Algorithm level (2)

Answer 25

Had ring attractor =excit and inhbit connections violation of Dale's principles states single neuron has either excit or inhbitatory So this model good at algorithm but not implenetation as violates this principle

Question 26

Marr's 3 levels HD Different Implenetation level (2)

Answer 26

* Could have 3 rings separating excitatory and inhbitatory connections (Excit ring project to inhbi ring and project back) * OR 2 ring OR 1

Question 27

What do Marr's 3 levels mean for modelling? (3)

Answer 27

* a model does not capture all 3 levels to be useful! * We can have a model that captures the algorithmic level but stays agnostic with regard to implementation * We can have a model that provides a correct algorithmic account but is wrong with regards to mechanism at level of implementation

Question 28

The neuron is the (building blocks to create a causal mechanism) - (3)

Answer 28

* Basic structural unit of the brain * Basic functional unit of the brian * Neurons form networks, interact via synapses

Question 29

Neurons' casual mechanism (2)

Answer 29

* Chain of processing through neural networks * At which eventually lead to cognition and motor output

Question 30

If we want to define this concept then

Answer 30

neural mechanism is a structure of casually interacting elements specified in terms of constituents of the brain and their interactions

Question 31

If we want to make (mechanistic) models of brain function we need (2)

Answer 31

models, and connect them to casually interacting networks Be aware all mdoels are simplifcaitons

Question 32

A neural representation is

Answer 32

a specific way of coding information (e.g., location, direction)

Question 33

Example of neural representation

Answer 33

HD tuning curve, Place cell red dots, grid cell

Question 34

Neural computation is

Answer 34

mapping between neural representations

Question 35

Example of neural computation

Answer 35

The synaptic weights between BVC and PCs 'compute' PCs (output ) from BVC input

Question 36

Braitenberg Vehicles (3)

Answer 36

* Type of model * Not for predicitng measurement * Tools for thinking about the brain

Question 37

Braittenburg vehicles help make sense of

Answer 37

whole modelling enterprise

Question 38

A brain (or more abstractly a control mechanism) is essential for

Answer 38

gudiing "the vehicle" and produce adaptive behaviour

Question 39

Braitenburg was inspired by

Answer 39

Nobert Wiener (a key foundational influence on Cybernetics - study of goal-directed mechanism, formalising the notion of feedback

Question 40

Vehicle 1 which has a

Answer 40

Sensor directly conneected to the motor

Question 41

In Vehicle 1 properities (3)

Answer 41

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

Question 42

In Vehicle 1 linear relationship between

Answer 42

motor signal and sensor signal

Question 43

How might Vehicle 1 move? (3)

Answer 43

* 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

Question 44

How do we interpret motion of Vehicle 1 (2)

Answer 44

* Seem to dislike bright light * Afraid of being discovered and seeks darkness to remain hidden

Question 45

Vehicle 2 produced from

Answer 45

some mutation durign self-duplication or fusion two type 1 vehicles are joined

Question 46

Vehicle 2 can have - (3)

Answer 46

* Ipi-lateral connections (A) * Contra-lateral connections (B) * Both (c) = same as V1

Question 47

How vehicle 2 move? If vehicles 2A and 2B within the vincinitt of source (5)

Answer 47

V2a will avoid the source (the wheel closer to itis more strongly driven V2b will approach the source (the wheel closer to itreceives less drive than the other) V2a and V2b dislike sources (both hit them hard if coming a source straight on) But V2a is a ‘coward’ V2b is ‘aggressive’

Question 48

Vehicle 3 of adding

Answer 48

different 'neuro transmitter' => (implying) change the positive connection from sensory to a negative inhbitatory connection

Question 49

How does vehicle 3 move? (2)

Answer 49

V3a approaches the source and comes to rest near it - it ‘adores’ the source V3b comes to rest facing away from the sourceand any small perturbation (a flash of light) will lead it away – it’s an ‘explorer’

Question 50

In vehicle 3 the more motor signal obtained from vehicles

Answer 50

less sensory signal output you get

Question 51

Vehicle 3C which

Answer 51

combines 4 types of sensors

Question 52

What types of sensors does Vehicle 3C have? (4)

Answer 52

Light (+ uncrossed) Temperature (+ crossed) Oxygen (- crossed) Nutrient density (- uncrossed)

Question 53

What behaviour does vehicle 3C manifest? (3)

Answer 53

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

Question 54

May be tempted for vehicle 3C to ascribe the

Answer 54

values or goals

Question 55

Up to vehicles 1,2,3, 3C we had sensory of this kind: (4)

Answer 55

* 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)

Question 56

In Vehicle 4 we (3)

Answer 56

* observe a multitidue of interesting behaviour in agents like Vehicle 4a like complex trajectory (Vehicle 4a moving like figure 8 around light source * Especially combined with properities of vehicle 3c * As well as other motor dependencies

Question 57

We can add threshold to sensors in vehicle 4A which (4)

Answer 57

introduce sudden onsets of behaviour Some input from sensor but vehicle does not do anything but at a given intensity the vehicle moves in a given direction = either instantly at given voltage or slowly ramping up or slow increase, plateau then nothing if intensity gets intense They look like 'decisions' , if decisions being made we may be tempted that these vehicles have some form of "will" Or they need to integrate stimulus over time to get moving (need convincing)

Question 58

What does it all mean in Braitenburg vehicles? (6)

Answer 58

We have discovered that with very simple rules – by combining only a few – we can create vehicle with wonderfully complex behaviour. We understand their behaviour as a consequence of their simple internal workings But what if we didn’t know about their internal workings? Their behaviour still looks complex. We might be tempted to assume complex mechanism underneath! Braitenberg suggests called this: The law of uphill analysis and downhill invention (so downhill is building something then figure out what it can do, if you don't how it is made then hard to analysis = uphill analysis) Guessing from the outside is hard => When we analyse a mechanism from the outside we tend to overestimate its complexity Going through the internal workings and playing out behaviours (even if initially unexpected) is much easier.