Week 1.1 - Introduction Flashcards

Introduction: The Computing Brain

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1
Q

Name 5 disciplines within Neuroscience and describe their focus.

A
  • Neurobiology - focuses on the biological components of the neuron, and analyzes its bits and pieces in all its glorious biological variability, such as ion channels, neurotransmitters, neuroanatomy.
  • Computational neuroscience — in broad outlines — denotes the use of computers for simulating neurons and neuronal networks, i.e., it’s dynamics. Computational neuroscience is concerned with reproducing essential dynamical phenomena of neurons.
  • Theoretical neuroscience - uses statistics and math to quantify and describe neuronal dynamics.
  • Cognitive Neuroscience - tackles the problems of cognitive function: ways in which neurons enable organisms to deal with the environment.
  • Neurodynamics. A branch of mathematical physics that attempts to explain the spiking behavior of neurons from a physical as well as dynamical perspective.
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2
Q

What are some of the main differences between Computational Neuroscience and Neural Computation?

A

Neural Computation

  • Focuses on reproducing neural function via simplified single neurons and abstracted networks.
  • Often disregards biological detail; high level of abstraction.

Computational Neuroscience

  • Focus on biophysics, the spikes or activity of the neuron and its biological components.
  • Uses computers to simulate neurons and neural networks (its dynamics).
  • Attempts to reproduce/explain dynamical emergent properties of biological neural networks.
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3
Q

What is a good model?

A

It depends on the question that you want to answer. Because based on that question, you will look for the appropriate level of abstraction.

But in general, these are the desired properties:

a model should be…

  • simple
  • accurate
  • representative
  • explanatory
  • predictive

*Note that these properties are contradictory. There is a trade-off between complexity (i.e. biologically implausibility and efficiency).

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4
Q

What is a pattern?

A

Some kind of reoccurring structure; statistical regularities in the world.

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5
Q

What are Artificial Neural Networks?

A

ANN’s are computing systems that are inspired by how neurons in the brain work. Most often they abstract biological complexity to emulate cognitive function.

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6
Q

What is a unit of measurement of capacitance (C )?

A

Farad, F

Also: C = Charge/Voltage

Therefore: 1 farad = 1 coulomb/ 1 volt

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7
Q

What is the unit of measurement of charge (Q)?

A

Coulomb, C

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8
Q

What is the unit of current (I )?

A

Ampere (A)

Also: I = Charge/Time

Therefore: 1 Ampere = 1 Coulomb / 1 Second

Or as a current density 𝜇𝐴/𝑐𝑚2

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9
Q

What is the unit of voltage (V)?

A

Volt, (V)

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10
Q

What is the unit of conductance (g)?

A

Siemens, (S)

Conductance is the inverse of resistance (R) which is measured in ohms Ω. Thus, conductance equals 1/Ω (or Ω-1 which is referred to as omh).

Or as a density 𝑚𝑆/𝑐𝑚2

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11
Q

What is the unit of measurement of m, n, and h activation variables?

A

This is a trick question:

m, n, h variables are dimensionless quantities that vary between 0 and 1

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12
Q

What is the unit of measurement of R, the ideal gas constant?

A

Joules per Kelvin per Mole

R = J * mol-1 * K-1

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13
Q

What is the unit of measurement of T, temperature?

A

Kelvin

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14
Q

What is the unit of measurement of F, Faraday’s constant?

A

Coulombs per Mole

C*mol-1

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15
Q

What is capacitance?

A

Capacitance can be defined as the ratio between the change in electric charge in a system over the change in electric potential.

C=Q/V

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16
Q

Define an electric current.

A

An electric current is the movement of charged particles through an electrical conductor.

I = Q/t

17
Q

Define Faraday’s constant.

A

Faraday’s constant is the electric charge carried by one mole of electrons.

F = 96,485.3365(21) C/mol

18
Q

What is the unit of measurement of resistance R?

A

Ohm

or because R = V/I, Volt/Ampere

19
Q

What is the difference between ‘emulation’ and ‘simulation’?

A

Emulate: Reproducing the function of a system. A model ‘emulates’ behavioral function, as in, ‘this model emulates the decision making behavior of mice’

Simulate: Use a computational model to reproduce the physical behavior of a substrate, as in ‘this model simulates the membrane potential’.