L8 computational neusci Flashcards by Stephanie Cheng

Does all scientific activity revolves around the creation, testing, refinement and debate of scientific models?

Yes

How well did you know this?

Not at all

Perfectly

What is Newtonian Mechanics?

Able to use a small number of very simple mathematical equations to describe the movement of planets with remarkable accuracy

How well did you know this?

Not at all

Perfectly

What is parsimony?

Decide what is important and ignore the rest

How well did you know this?

Not at all

Perfectly

What is explanatory?

Give good descriptions

How well did you know this?

Not at all

Perfectly

What is predictive power and falsifiability

Allow precise predictions

How well did you know this?

Not at all

Perfectly

Models of the brain have to strike the right balance between ______/______ and ______ of the explanations or predictions they make

simplicity; parsimony; accuracy

How well did you know this?

Not at all

Perfectly

What do compartmental models do?

stimulate individual neurons as lots (sometimes thousands) of connected tubelets and each has its equivalent circuit

How well did you know this?

Not at all

Perfectly

Purkinje cells:
Number of compartments
Number of ion channels
Number of different voltage-dependent channels distributed across
Number of synapses
What does it do?

4550;8021; 10; 3500; reproduces electrical current injection and synaptic responses

How well did you know this?

Not at all

Perfectly

Prof Markram‘s Blue Brain project tries to model parts of the neocortex by running vast _________ on massive super computers

compartmental models

How well did you know this?

Not at all

Perfectly

What did the Blue Brain project ignore?

make the model parsimonious by leaving out as much irrelevant detail as possible

How well did you know this?

Not at all

Perfectly

Why there is no method for trying to decide what details are relevant and which are not for Blue Brain project?

It is unclear what aspects of brain function the blue brain is suppose to model

How well did you know this?

Not at all

Perfectly

Did Blue Brain yield great return?

How well did you know this?

Not at all

Perfectly

Which of the following are spiking models/ non-spiking models (rate based)?:
Integrate and Fire
Linear threshold or linear-non linear neurons
linear neurons
the Izhikevich neurons

spiking model:
Integrate and Fire
the Izhikevich neurons

non-spiking models (rate based):
Linear threshold or linear-non linear neurons
linear neurons

How well did you know this?

Not at all

Perfectly

It is useful to think of neurons as ______

in a first order approximation

How well did you know this?

Not at all

Perfectly

How to compute neurons?

take all synaptic inputs (many of which may be zero most of the time) -> multiply each input by its ‘synaptic weight’ -> sum -> use a threshold criterion/ a linear/ sigmoidal function to decide how much output to send given the weighted sum of inputs

How well did you know this?

Not at all

Perfectly

What is the major property of biological neurons?

firing action potentials in response to synaptic input

How well did you know this?

Not at all

Perfectly

Essential features in simplified models of spiking neurons.

Action potential’s shape is same each time, so just treat the occurence of an AP as a binary 1
All EPSPs and IPSPs eventually get summed in the cell body, so no need to model dendrites

How well did you know this?

Not at all

Perfectly

Integrate-and-fire model 3 properties.

Point neuron; resistance+capacitance; synaptic input

How well did you know this?

Not at all

Perfectly

Which is simpler? Rate-based/ spiking neurons?

Rate-based

How well did you know this?

Not at all

Perfectly

Rate-based neuron models’ inputs are ______ & ______ to calculate the ‘activation’

weighted; summed

How well did you know this?

Not at all

Perfectly

Output in rate-based models could be _____/ _____/ ______/ ________

sigmoid; threshold; threshold linear function; linear function

How well did you know this?

Not at all

Perfectly

Computing units used in artificial neural network can be regarded as models of _________

biological neurons

How well did you know this?

Not at all

Perfectly

Activation function captures essence of a neuron’s spiking response using ____ & _____

Binary (0/1): neuron is silent/ firing - McCulloch-Pitts threshold logic unit
Analog: number represents neuron firing rate -piecewise linear/ sigmoid

How well did you know this?

Not at all

Perfectly

McCullough-Pitts Neurons is also known as.

Binary Threshold Neurons

How well did you know this?

Not at all

Perfectly

What is the simplest neuron model?

Binary threshold neuron

What do binary threshold neuron model receive?

Inputs form synapses that have positive/ negative weights (excitatory/ inhibitory synapses)

What is the activation of binary threshold neuron model?

Weighted sum of inputs

How does binary threshold neuron produce a binary output of 0/1?

Depends on whether the activation is greater than a given threshold

How is binary threshold neuron similar to biological neurons?

Summate synaptic currents in their dendrites and soma, and fire an action potential or not depending on whether there is depolarization

Binary threshold neurons are "_______"

Logic Gates

How to make it possible to make networks compute any function?

By setting synaptic weights on networks of binary threshold neurons appropriately

Linear and linear-nonlinear neurons weight and sum inputs the same way like _______ do to compute activation

binary threshold neurons

Linear and linear-nonlinear produce a _______ output (their firing rate) which is either just ______ to the activation (____) or is a ______, often ____, function of the activation

real valued; proportional; linear; non-linear; sigmoidal

Name 3 functions of the sigmoids

Logistic function Arctan function Auditory nerve fiber rate-level functions

Which model can model the input-output relationships of sensory neurons as 'weight matrix'?

Receptive field model

What is 'weight matrix'

It says what the synaptic weight of each given 'spatial' or 'auditory frequency channel' would be

Name 3 examples of receptive field models.

Center-surround receptive fields in retina or LGN Simple cell receptive fields in V1 Spectro-temporal receptive fields in auditory structures

What functions do the linear or LN models serve?

Predictive power and falsifiability

What do the linear or LN models make easy to do?

Easy to predict what the response to any arbitrary stimulus should be

What do the Difference of Gaussians (DoG) model of retinal ganglion cell receptive fields posit?

The weight of a spot of light on the retina as a function of distance from the receptive center is given by a broad inhibitory (negative, surround) bell curve and a sharp excitatory (positive, center) bell curve

The centre-surround structure of retinal ganglion cells turns them into "______"

spatial frequency filters

Larger RGC receptive fields are tuned to "________" structure in the visual scene, while smaller RFs are tuned to ______ structure

coarsely grained; fine-grained

What kind of filter does the Gabor Filter Model of V1 simple cells?

Linear filter

Binaural STRFs in A1 can predict ______ & _______

spatial tuning; how the neurons respond, as a function of time, to the sounds coming from right/left ear

Binaural STRFs in A1 is a hugely ______ model. Because _____

parsimonious; it takes all the complicated auditory brain stem away for simplification

Rosenblatt's Perceptron is the first "______", basically just a _________

artificial neural net; single binary threshold neuron

Rosenblatt's Perceptron is used as a "______"; (output goes high if inouts belong to class ___, low for class ______)

classifier; 1; 2

Rosenblatt's Perceptron's weight are set through ____ by thousands of examples: If the desired output for a given input set is high, then increase the weight of those inputs that were ___, but decrease those were ___

training; high; low

Rosenblatt's Perceptron is a form of _____ learning

associative

A ______ perceptron cannnot learn functions like 'exclusive or' but ______ perceptrons can in principle compute any arbitrary function

single layer; multilayer

Rosenblatt's Perceptron uses a ____ function to compute binary outputs. Multilayer perceptrons use _____ (____/____)

threshold; sigmoids; arctan; logistic

What does multilayer perceptrons mean?

Have one or more 'hidden layers' of artificial neurons between input and output

Weights are still set through ______ and ______, but errors during training stages must be '____' to adjust the weights of the hidden layer units

training; associative learning; backpropagated

Are there any input-output mapping that a sufficiently big multilayer perceptron cannot learn?

What is deep neural network?

multilayer perceptrons with more than one hidden layer

After training, neurons in subsequent layers may become sensitive to 'features' of ever greater _______ and ______

complexity; abstraction

In ThreeBlueOneBrown: first hidden layer: ______ | second hidden layer: _______

sensitive to little edges; sensitive to more complex shapes

In both _____neural nets, the features the hidden neurons are actually sensitive to, may be obscure.

real and artificial

Deep neural networks for image classification often have a hierarchy of layers sensitive to _________.

high level features

Deep neural networks for image classification is equivalent to various parts of the _______ and the_________ or _________.

ventral 'what' stream; dorsal 'where' stream; color areas of extrastriate visual areas

A _ layer RNN with only ____ hidden neurons trained on 4.4MB text file containing all the collected works of Shakespeare produced artificial Shakespeare.

3; 512

Multilayer perceptrons need to be trained on very ___ datasets, comprising tens of thousands to millions of ______, and for each example the ______, '_____' answer must be

large; examples; desired; correct

Multilayer perceptrons: The synaptic weights of the network are adjusted ______ to make the network output more similar to the _______________

gradually; correct answers in the training set

Multilayer perceptrons: "Errors" (differences between current and correct output) must "________" through the ___________

backpropagate; layers of the network

What is the criticism of multilayer perceptrons?

Real brains do not learn like this, they don't backpropagate/ teachers give us thousands of examples

What is unsupervised learning?

Learn types of things without 'teacher' input which tells them 'correct' answers

What will network under unsupervised learning discover?

natural 'clustering' of certain features

What way of learning is more biological?

unsupervised learning

What is reinforcement learning?

a network is not told what the output for a certain input should be, but it is simply 'rewarded' for 'good' outputs or 'punished' for 'bad' outputs

Reinforcement learning will try to predict which output (behaviour) lead to ________.

maximum reward and minimum punishment

What in the brain is thought to implement reinforcement learning?

Levels of the neurotransmitter dopamine representing 'reward prediction error'

Temporal difference gammon is a very modestly sized network of less than ____ artificial neurons

300

Temporal difference gammon can beat more human players at _______

backgammon

Neural network, whether biological/ artificial, compute by _______________

combining weighted inputs to calculate outputs

Networks must often be trained by _______

adjusting the weights to produce useful outputs

The trained weight matrix is a form of "________"

long term memory

Recurrent neural networks also have " ________" through activity running _______

short term memory; in loops

Subsequent layers of the network tend to become sensitive to _______________ of the inputs

increasingly higher order features