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305 Flashcards

1
Q

What does the Artificial Neural Networks constitute

A

A part of computer science based on neuroscience ideas

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2
Q

What is biological excitation

A

Internal mechanism

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3
Q

What are the external neurons that enable the propagation

A

External mechanism

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4
Q

What are propagated by a neuron

A

Spikes

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5
Q

Describe a neuron

A

Able to propagate signals over large distances
Propagate information by generating electrical pulses (action potentials or spikes) that can travel down nerve fibres.
Specialised for generating electrical signals in response to chemical/other inputs and transmitting them to other cells
Represent and transmit information by firing a sequence of spikes in various temporal patterns

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6
Q

What is the electrical potential between in most living cells

A

Interiors and (exterior) environment

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7
Q

What is one of the factors determining the energy barriers encountered by charged substances (ions) entering/leaving the cell

A

The membrane potential

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8
Q

What are ion channels

A

Proteins within the cell membrane with the central pore through which ions can cross the membrane

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9
Q

What is the function of the cell membrane

A

It acts as a barrier for ions

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10
Q

What elements are ions predominantly made of

A

Sodium
Potassium
Calcium
Chloride

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11
Q

How do Ion channels control the flow of ions

A

By opening and closing in response to voltage changes and both internal/external signals

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12
Q

What causes the response of a large amplitude electrical wave

A

A significantly large perturbation, above a threshold in intensity and duration

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13
Q

Describe wave propagation in biological excitation

A

Travels with uniform velocity
Excitation/transmission is all or none- strength does not vary
Excitation is followed by the absolute refractory period, an unexcitable period of definite duration

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14
Q

What is the function of dendrites

A

Receive inputs from many other neurons through synaptic connections

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15
Q

What is the soma

A

The cell body

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16
Q

What is the function of the axon

A

Carries signals from the neuron to other neurons/effectors

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17
Q

What is the name for the tips of axon branches

A

Boutons/nerve terminals

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18
Q

What is a synapse

A

The location of interaction between a terminal and the cell

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19
Q

When is a neurotransmitter released

A

When a spike arrives from the presynaptic neuron

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20
Q

Describe trans-synaptic stimulation

A

Neurotransmitters cross the synaptic cleft and bind to receptors on the dendrite spine
Excitatory synapses on the cortical pyramid form on dendrite spines or axon

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21
Q

Describe no physiological continuity from neuron to neuron

A

When an impulse (perturbation) reaches a
synapse, it does not necessarily stimulate the
following neuron

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22
Q

What does trans-synaptic stimulation of a neuron require

A

Either:
Temporal summation- a repetition of impulses in time at the same synapse
or
Spatial summation- the simultaneous arrival of impulses at a sufficient number of adjacent synapses to make the density of excitation high enough

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23
Q

What is inhibition

A

The opposite effect of excitation, renders the element less excitable to other stimuli. Can occur due to the arrival of an impulse at synapses

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24
Q

What is the top trace

A

A recording from an intercellular electrode connected to the soma of the neurone.

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25
Q

What is the middle trace

A

A simulated extracellular recording. Action potentials appear roughly equal positive and negative potential fluctuations with an amplitude of 0.1V, 1000x smaller than intracellularly recorded action potential

26
Q

What is the bottom trace

A

A recording from an intracellular electrode connected to the axon some distance away from the soma.

27
Q

Describe neuron to neuron propagation

A

Spikes, but not subthreshold potentials, propagate regeneratively down the axons

28
Q

What is the weight of connection

A

The connection strength, assigned some value w, that describes the importance of a connection

29
Q

What represents the dendrites in the abstract neuron model

A

Inputs a(0) to a(n)

30
Q

What represents the soma in the abstract neuron model

A

The neuron body, represented by a circle

31
Q

What represents the axon in the abstract neuron model

A

Output X

32
Q

What represents the excitation potential threshold in the abstract neuron model

A

The threshold

33
Q

What represents the importance of presynaptic neurons in the abstract neuron model

A

Weights of input connections

34
Q

When is the abstract neuron excited

A

When the weighted sum is above the threshold 0

35
Q

What did McColloch and Pitts demonstrate

A

Because of the all or none character of nervous activity, neural events and the relations among them can be treated by means of the propositional logic

36
Q

How was the neuron modelled by McColloch and Pitts

A

As a discrete time input
With excitatory and inhibitory connections and an excitation threshold

37
Q

Describe discretization

A

Comparable to the refractory period
No zeno executions

38
Q

Describe fixed time step size

A

The impulse travels with a nearly uniform velocity in a biological neural system

39
Q

What is the value of w for an excitatory connection

A

+1

40
Q

What is the value of w for an inhibitory connection

A

-1

41
Q

What is the function of a register cell

A

To retain the input for one period elapsing between 2 instances

42
Q

What is the excitation threshold in a McCulloch-Pitts neuron equivalent to in a biological neuron

A

Potential threshold

43
Q

What prevents excitation of an MP neuron

A

Activity of a single inhibitory input (input via a connection with negative weight)

44
Q

What is S ^(t-1)

A

Instant state of the neuron

45
Q

What is each stage of the MP Neuron Computation Algorithm

A

Check inputs from all inhibitory connections
If they are not all 0, Xt = 0, else:
Calculate the instant state of the neuron
If S^(t-1) < threshold Xt = 0
else (S^(t-1) >= threshold): Xt = 1

46
Q

What is linear separability

A

There exists a line/plane such that all inputs which produce a 1 for the function lie on one side of the line/plane and all inputs which produce a 0 are lie on the other side of the line/plane

47
Q

Can each linearly separable function be represented by a single MP neuron (Complete)

A

No

48
Q

Define learning

A

To change in response to experience

49
Q

What is the ANN learning rule

A

The rule how to adjust the weights of connections to get desirable output

50
Q

What was Hebb’s thesis

A

Cells that fire together, wire together

51
Q

What is the simple formulation of Hebb’s rule

A

Increase weight of connection at every next instant in the way

52
Q

When is there an excitatory input according to Hebb’s rule

A

When input is not equal to 0

53
Q

When is the neuron fired according to Hebb’s rule

A

When output is not equal to 0

54
Q

Describe how to normalise inputs for Oja’s rule

A

Square route the sum of all the inputs
-root(w1 + w2 +… Wn)
Set weights to 1/result multiplied by the previous weight
Continue until max change in weight <= convergence criteria

55
Q

Describe unsupervised learning

A

A type of machine learning where the algorithm is not provided with any pre-assigned labels or scores for the training data.
As a result any unsupervised learning algorithms must first self-discover any naturally occurring patterns in the training data set

56
Q

Describe clustering

A

An unsupervised network that can group similar sets of input patterns into clusters predicated on a predetermined set of criteria relating to the components of the data

57
Q

When can clustering be achieved

A

When we extend the single neuron to the network with multiple outputs

58
Q

Describe competitive learning

A

We consider a one layer neural network with multiple outputs
A single output neuron of a network competes among all the outputs to have its weight updated, whereas in Hebbian learning several outputs can be simultaneously updated

59
Q

What is competitive learning suited for

A

Discovering statistically important features that may be used to classify sets of input patterns

60
Q

Describe a Self Organising Map

A

Used to produce a low dimensional (typically 2D) representation of a higher dimensional data set, while preserving the topological structure of the data.
Can be used for clustering or visualisation

61
Q
A

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