CH 4 (2) - Neurons and the Brain

Question 1

What does the cortical homunculus represent?

Answer 1

Illustrates how much space the sensory representations of different parts of the body occupy
in the cerebral cortex.

Question 2

What does the Basal Ganglia do?

Answer 2

The basal ganglia are a group of nuclei (clusters of neurons) located beneath the cerebral cortex. The function of the basal ganglia is mainly related to the motor system where they are engaged in the planning, selection, execution and learning of actions.

Question 3

What is the hippocampus?

Answer 3

sea horse like shape. it is involved in the formation of episodic long-term memories.

Question 4

What is the function of the Amygdala?

Answer 4

group of nuclei related to the analysis of the emotional or motivational significance of stimuli.

Question 5

What are the hippocampus and the amygdala parts of?

Answer 5

The limbic system

Question 6

What three properties make the human brain stand out among other species?

Answer 6

• Neuron morphology
• Electrical properties of neurons
• Sequence processing

Question 7

Neuron morphology of human brain

Answer 7

The pyramidal neurons in the human cortex have the most elaborate and spine-rich dendritic trees.

Question 8

Electrical properties of neurons in the human brain

Answer 8

the pyramidal neurons in the human cortex have a very low membrane capacitance (storing of electric energy), which significantly enhances signal transmission.

Question 9

Sequence processing in the human brain

Answer 9

Compared to other species, humans have superior capabilities of learning sequences of stimuli

Question 10

When were neurons discovered?

Answer 10

in 19th century (1870s) by Golgi, Ramón and Cajal

Question 11

When was the birth of artificial neural networks?

Answer 11

1943 when a computational model of the nervous system was developed.

• Every cell has only two states ( (firing and resting)
• A cell only fires if its input crosses a defined threshold

-> Starting point of modern AI

Question 12

What is the perceptron and when was it developed?

Answer 12

1957: single neuron that can learn to classify input patterns.
- the neuron learns from falsely classifying samples by adapting its synaptic weights

Question 13

When was the AI Winter?

Answer 13

1969. The perceptron only works for simple data sets that are linearly separable (Xor problem cannot be solved).

Question 14

Why has the AI winter ended?

Answer 14

With the development of new neural network architectures and the back-propagation algorithm, which has become the basis of all modern neural networks architecture.

Question 15

What are biological neurons?

Answer 15

Cells with a highly intricate structure and complex electrophysiological dynamics

Question 16

What are analog neuron models?

Answer 16

real-valued (real numbers) output that can be interpreted as the neuron’s firing rate

( artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse.)

Question 17

spiking neural network

Answer 17

emits impulses that correspond to the spikes emitted by biological neurons

Question 18

Analog neuron model forumla

Answer 18

output = n(x) = A(w1x1 + … + wnxn + b)

Question 19

Common activation functions

Answer 19

Binary step function
Logistic function
tanh
Rectified Linear Unit (ReLu)
Exponential Linear Unit (ELU)
Gaussian

Question 20

What is information processing in biological neurons largely driven by?

Answer 20

ionic currents that accumulate in the soma (cell body)

Question 21

How can the flow of the currents in the soma be modeled?

Answer 21

functionally equivalent electrical circuits

Question 22

What is the Leaky Integrate-and-Fire Model?

Answer 22

first spiking neuron model that is still of relevance today. Comprises of a electric circuit composed of resistors and capacitors. It may be driven by a voltage or current source and these will produce different responses.

Question 23

Does the LIF neuron capture cell morphology?

Answer 23

No it is a point neuron model?

Question 24

More detailed spiking neuron models are derived from the

Answer 24

Hodgkin-Huxley model

Question 25

What are the 5 levels of modeling?

Answer 25

1. Detailed compartmental models
2. Reduced compartmental models
3. Single-compartment models
4. Cascade models
5. Block-box models

Question 26

What is the detailed compartmental model?

Answer 26

• captures the detailed neuron morphology based on anatomical reconstructions. The continuous geometrical structure of the neuron is discretised into compartments, each of which can be modeled as a point neuron.

Question 27

What is the Reduced compartmental model?

Answer 27

comprised of only a few dendritic compartments to decrease computational complexity and to increase analytical tractability.

Question 28

What is the single-compartment model?

Answer 28

ignores the morphology of the neuron. Detailed spiking point neuron models capture the flow of ion currents and spike generation.

Question 29

What is the cascade model?

Answer 29

purely functional model that only represents abstract computations but no biological detail.

Question 30

What is the black-box model?

Answer 30

Does not model any computational function at all but considers the neuron as an input/output system with defined statistical behavior.

Question 31

Are standard methods in neuroscience sufficient the way they are applied now?

Answer 31

Not really. If they are used on microprocessors they do not lead to a meaningful understanding of the processor in terms of information processing hierarchy. However, a hypothesis would be reasonably easy to test.

Question 32

What is whole brain modeling?

Answer 32

Executable hypothesis of the architecture and the functioning of the brain. Whole brain models integrate a large number of simulated neuron models in a coherent neural network model that captures to connectivity of biological brains to some degree.

Question 33

Many whole brain models are simulations of

Answer 33

the thalamocortical system

Question 34

Simplified engineering brain models are defined manually based on

Answer 34

rules and statistics identified in datasets