# Computational modelling Flashcards Preview

## Module 7 > Computational modelling > Flashcards

Flashcards in Computational modelling Deck (22)
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1
Q

What is computational about neuroscience?

A

> computer based modelling of neuronal functions at different levels
we try and understand the complex interactions in dynamic systems

2
Q

David Marr’s level of analysis:

A

1) What is the problem being solved?
or how is the brain trying to solve this problem?

2) Which algorithms can be used to solve it?
3) How are these implemented in neuronal systems? How is it being solved?

3
Q

is Marr’s level of analysis a linear model?

A

Although David Marr thought it to be - it is not

John O’Keefe figured out place cells without going through these stages

4
Q

‘Humans generalise effortlessly from limited data sets’

Which study proved this?

A

Level 1: how is the brain solving this problem? How is the brain generalise language?
Lany & Saffran (2010) – infants were ‘trained’ in an artificial language in which word categories were reliably distinguished using statistical cues

5
Q

Lany & Saffran Study illustrated:

A

–> Infants learn language patterns through statistical pairing
Infants attribute meanings of words based on statistical cues (during training)
They then categorised and generalised the pattern to include novel word pairings

6
Q

Using an algorithm (Level 2) to help us understand how to categorise

A

Bayesian Statistics
1) Prior Beliefs:
Knowledge of word categories
Generative model

2)Data
Empirical observations
We make assumptions based on prior beliefs then over time we collect data and alter these assumptions

7
Q

What is Bayesian Probability?

A

A theory in which evidence about the world is expressed in terms of ‘degrees of belief’

8
Q

How does Bayesian learning occur in the sensorimotor system?

A

Kording & Wolpert

In the most uncertain condition we are least likely to update and change, and maintain based on our prior belief

9
Q

Algorithm based on bayesian probability

A

Bayes Category Formation
Prior Belief + On-going Information
==> Effortless Categorisation

10
Q

Alogorithm for hierarchical learning:

A

Kemp & Tenenbaum
Their model brings structure learning methods closer to human abilities and may lead to a deeper computational understanding of cognitive development.

11
Q

Algorithm for bird songs:

A

Making a Generative model

->Different levels are distinguished in their temporal scales

12
Q

Step 3: Implementation

=>Dehaene-Lambertz et al. 2006

A

Sequential distribution reveal the language hierarchical processing scheme

13
Q

Which are of the brain was activated during presentation of sentences in preverbal infants?

A

Perisylvian Activation
- Adult like activation
>Dehaene-Lambertz et al. 2006

14
Q

What were the adult like responses in language processing in preverbal infants?
Dehaene-Lambertz et al. 2006

A
```Fastest Responses:
- Heschl’s gyrus
Slower responses (higher cortical areas; processing happens later)
- Posterior STG
- Temporal poles
- IFG (Broca)```
15
Q

When was Broca’s area activated in preverbal infants?

A

Activation in Broca after repetition of sentence

Broca Activated before babbling: activity in this region is not the consequence of sophisticated motor learning but, on the contrary, that this region may drive, through interactions with the perceptual system, the learning of the complex motor sequences required for future speech production.

16
Q

Step 3: Implementation

study by: Iglesias et al. 2013

A

Hierarchical signals of Prediction Error are processed in neuroanatomically (and physiologically) distinct regions

17
Q

Where are ‘low level’ prediction errors processed?

= Iglesias et al. 2013

A

low-level PEs about visual stimulus outcome => widespread activity in visual and supramodal areas + midbrain

18
Q

Where are ‘high level’ (abstract) prediction errors processed?
= Iglesias et al. 2013

A

high-level, abstract, PEs about stimulus probabilities in the basal forebrain.

19
Q

Step 3: Implementation

study by: Emberson et al. 2015

A

Top Down Modulation in the infant Brain - does it occur?
Is expectation-related feedback an inherent property of the cortex (i.e., operational early in development) or does expectation-related feedback develop with extensive experience and neural maturation

20
Q

What did Emberson et al. 2015 study tell us about ‘top down modulation’ in the infant brain?

A

The occipital cortex of 6-month-old infants exhibits the signature of expectation-based feedback
- this region does not respond to auditory stimuli if they are not predictive of a visual event.

These findings suggest that the young infant’s brain is already capable of some rudimentary form of expectation-based feedback

21
Q

Computational Neuroscience has helped us understand:

A

1) Top down processing in infancy
we do not learn about the world only in a bottom-up manner
We need experience with the world (bottom-up) + top-down in synchrony

2) Hierarchical learning that you experience aids your learning – learning complexity requires hierarchical interactions
3) Development is regionally heterogeneous

22
Q

Dynamic causal modelling

Cooray et al. 2016

A

MMN - cortical network develops in adolescence up to adulthood

== computational analysis can extract relevant information from neuronal measurements