Encoding and Decoding Flashcards

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1
Q

What is neural encoding?

A

The process of converting input into a neural response.

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2
Q

What is neural decoding?

A

To retrieve/obtain information from neuronal activity.

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3
Q

What is population decoding?

A

Population decoding refers to the process of interpreting the combined activity of a group of neurons to determine the information they represent, such as a stimulus or a movement.

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4
Q

Why is population decoding important in understanding neural representation?

A

Population decoding is crucial because individual neurons can be noisy or ambiguous. By decoding the activity of many neurons together, the brain (or an algorithm) can achieve more accurate and robust interpretations of complex stimuli or motor commands.

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5
Q

What’s the objective of optimal linear decoding?

A

To find the optimal weight matrix W that minimizes the decoding error.

For more information about the mathematical formulation https://chatgpt.com/share/9b7dcc8b-943c-438e-bab1-1e1937cb16ce

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6
Q

Name a four examples of features

A

Features are specific aspects of an input that help distinguish it.

For instance: shapes (e.g.’ovalness’), orientations (e.g.’slantedness’), colour, smell (‘sweetness’), distance (‘far-ness’), rhythm, brightness , texture (‘roughness’), temperature (‘warmness’), frequency (‘shriek’), loudness, …

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7
Q

What is a feature space?

A

The number of dimensions is often equal to the number of measurable features of the object.

Complex features can be combinations of lower-level features, and so the feature space is ‘combinatorial’ in nature.

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8
Q

What are the advantages of population coding?

A
  1. Reduction of uncertainty due to neuronal variability.
  2. Ability to represent a number of stimulus attributes simultaneously.
  3. Driving a large population of downstream neurons.
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9
Q

This equation shows how a neuronal population (4 neurons) of the cricket hair cell encodes wind direction.

  • f(s) is the firing frequency of neuron ‘a’ in response to a certain stimulus ‘s’
  • rmax is the maximum firing rate of the neuron
  • v is the wind direction
  • ca is the preferred direction of a neuron’s tuning curve

When is the activity of the neuron maximal?

A

When the velocity of the wind is aligned with the neuron’s preferred direction.

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10
Q

How can we estimate wind direction using the activity of four neurons of a cricket?

A

Through population decoding.

We take the activity of the neurons to indicate their alignment according to their preferred directions. We obtain the population vector by summing the activations of four individual vectors.

In this equation, r/rmax denotes the spike count rate of a given neuron over its maximum firing rate (modulation).

Unit vector Ca denotes the preferred direction of the wind for a given neuron.

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11
Q

The graph below shows behavioral and electrophysiological data from a random dot motion discrimination task performed by a monkey.

What does the graph tell you about neuronal decoding accuracy?

Random dot motion discrimination: https://youtu.be/Cx5Ax68Slvk?si=zAmdfdlcRaAmPqHi

A

The discriminations made by a monkey as a function of coherence of random dot movement is similar to the discriminations that an ideal observer could make given the neuronal responses.

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12
Q

What is an overcomplete basis?

A

An overcomplete basis is when we use more dimensions (neurons) than there are dimensions to encode (features).

To encode a point in 2 dimensions, one needs two (linearly independent) coordinate axes (such as the Descartes plane x,y).

When multiple neurons encode a lower-dimensional stimulus we have an overcomplete basis. An overcomplete basis leads to better decoding accuracy.

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13
Q

What are the 5 main assumptions of the McCullogh-Pitts neuron?

Original publication, 1943.

A
  1. The activity of a neuron is all or none.
  2. A certain fixed number of synapses must be excited in order to excite a neuron at any time, and this number is independent of the previous activity of the neuron.
  3. The activity of any inhibitory synapse absolutely prevents the excitation of the neuron at any time.
  4. Weights do not change with time.
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