Multiple Choice Test Flashcards
Univariate analysis:
Which of the following does not reflects spiking activity?
- Single-unit activity (SUA)
- Low frequency components of the EFP (extracellular field potentials)
- High frequency components of the EFP
Low frequency components of the EFP (extracellular field potentials)
Univariate analysis:
What needs to be taken into account when modelling the BOLD-signal?
- The shape of the haemodynamic response function
- Slow fluctuations in the signal
- The effects of the experimental conditions
- All of the above
All of the above
Univariate analysis:
What is the hemodynamic response?
- A way for the neuron to cool itself
- A way for the neuron to rapidly get rid of waste matter
- A way for the neuron to inhibit ‘firing’
- A way to get more oxygen and energy to the neuron
A way to get more oxygen and energy to the neuron
Multivariate analysis:
What is a classifier in multivariate analysis of fMRI?
- A number between 0-1 that specifies the level of activation in a voxel
- An algorithm used to separate brain activation according to category
- A linear model that predicts the amount of neuronal activity in a group of voxels
- The voxel with the most influence on model performance.
An algorithm used to separate brain activation according to category
Multivariate analysis:
Which of the following statements regarding multivariate analysis is false?
- Multivariate analysis measures the difference in activation patterns of voxels
- Encoding models can be used to infer stimuli from brain activation in multivariate analysis
- Assuming different brain regions are the same is a pitfall of multivariate analysis
- Multivariate analysis measures if activation is significant on a per-voxel basis
Multivariate analysis measures if activation is significant on a per-voxel basis
Multivariate analysis:
What is the Curse of Dimensionality and why is it a problem for multivariate fMRI analysis?
- A high number of voxels and low number of samples leads to false positives
- Warping of voxel space leads to false negatives
- A genetic deformity of brain regions leads to false inferences
- The natural layering of the cortex leads to difficulties in fMRI measurements
A high number of voxels and low number of samples leads to false positives
Rhythms of the brain:
What characterises the bottom-up approach?
- It is the same approach as Allan Turing had on human intelligence <3
- It is to move from theory to data
- To investigate the brain with focus on making theory based on data
- To replicate cognitive behaviour
To investigate the brain with focus on making theory based on data
Rhythms of the brain:
Which statement is true about brain organization? Neuron are….
- arranged in local clusters with fewer long distance connections
- connected to all other neurons
- all randomly connected across the brain to a fraction of all neurons
- arranged in local clusters with random long range connections
Arranged in local clusters with fewer long distance connections
Rhythms of the brain:
Inhibitory feedback happens when….
- the excited neuron reduces the activity of its neighbors
- The dominant source of input to an interneuron is different from the population of cells which is targeted by the interneuron.
- a distant neuron excites the interneuron and this interneuron is then inhibiting several other neurons.
- the neuron inhibits all other neurons in the brain.
A distant neuron excites the interneuron and this interneuron is then inhibiting several other neurons.
Rhythms in the Brain II
Slow brain oscillations
- Cannot propagate
- Can propagate over small areas
- Can propagate over larger areas
- Can propagate everywhere
Can propagate over larger areas
Rhythms in the Brain II
The presence of pink 1/f noise indicates
- Oscillations at different scales are temporally linked
- Oscillations at different scales are independent
- Oscillations at high frequencies are less costly
- Oscillations at high frequencies are more costly
Oscillations at different scales are temporally linked
Rhythms in the Brain II
Are neurons integrate and fire systems?
- Definitely yes, like we see in neural networks
- Definitely no, brains are not like neural networks
- it was the prevailing model until the 1980s, but no longer
- It has been the prevailing model since the 1980s
It was the prevailing model until the 1980s, but no longer
Convolutional neural networks
What exactly is meant by “shared weights” in convolutional neural networks?
- Per feature map, the same weights are applied to each local receptive field
- All feature maps in the network have the same weights
- All feature maps in a layer have the same weights
- You must initialize all weights with the same value
Per feature map, the same weights are applied to each local receptive field
Convolutional neural networks
Are convolutional neural networks designed to closely parallel biological vision system?
- Yes, CNNs provide framework for fully biological brain-computational models
- No, but the activity of CNNs can be related to activity of the visual system
- No, only recurrent NNs are completely parallel to primate visual system
- Yes, CNNs have proven to be 90% similar to biological brains
No, but the activity of CNNs can be related to activity of the visual system
Convolutional neural networks
How do you pass the output of a conv. layer to a fully connected layer?
- Flatten it
- Multiply every input pixel by the total number of shared weights
- Calculate the gradient and subtract it from the loss
- import numpy
Flatten it
