Neuron Models

Question 1

McCulloch-Pitts model - Analog Neurons

Answer 1

Have real-valued output that can be interpreted as the neuron’s firing rate. Simple linear layer: A(wT*x +b)- Activation functions

Question 2

Spiking Neurons

Answer 2

Emit impulses that correspond to the
spikes emitted by biological neurons.

Question 3

The Leaky Integrate-and-Fire Model - Spiking Neurons

Answer 3

Information processing in biological
neurons is largely driven by ionic currents
that are accumulated in the soma
(Zellkörper)

• The flow of these currents can be modeled
  with functionally equivalent electrical
  circuits (ionic currents are represented by
  electrical currents)
• The first spiking neuron model that is still
  of high relevance today is the leaky
  integrate and fire (LIF) model
• LIF neurons are comprised of a simple RC
  circuit that is driven by an external current

Question 4

Levels of Modeling

Answer 4

Detailed compartmental models
Reduced compartmental models
Single-compartment models
Cascade models
Black-box models

Question 5

Detailed compartmental models

Answer 5

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

Question 6

Reduced compartmental models

Answer 6

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

Question 7

Single-compartment models

Answer 7

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

Question 8

Cascade models

Answer 8

are purely functional models that only represent abstract computations but no biological detail.

Question 9

Black-box models

Answer 9

do not model any computational function at all but consider the neuron as an input/output system with defined
statistic behavior