Neuron Models and Stochastic Processes

Neuron Models and Stochastic Processes

Assessment

Interactive Video

Biology

University

Hard

Created by

Thomas White

FREE Resource

The lecture explores the concept of stochastic intensity in neuronal dynamics, focusing on inter spike interval distribution and survival functions. It discusses the application of renewal theory and the translation of continuous time escape rates into discrete time probabilities. The lecture also covers experimental applications and the intrinsic noise sources in neurons, providing a foundation for future studies on spike train likelihood and neuron model fitting.

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10 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What concept was introduced in the beginning of the lecture?

Deterministic signals

Membrane voltage

Neuron firing

Stochastic intensity

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does a deterministic signal give rise to in a neuron model?

Stochastic intensity

Membrane voltage

Escape rate

Survival probability

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the role of stochastic intensity in neuron models?

Determines membrane voltage

Calculates escape rates

Generates spikes

Resets potential

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the survival probability indicate in neuron models?

Interval distribution

Escape rate

Membrane voltage level

Likelihood of firing

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How is the interval distribution derived from survival probability?

By resetting potential

By calculating firing time

By integrating membrane voltage

By multiplying with escape rate

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What theory is used for constant input in neuron models?

Renewal theory

Survival theory

Stochastic theory

Deterministic theory

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What effect does time-dependent input have on neuron models?

Increases escape rate

Alters survival function

Changes membrane voltage

Affects firing probability

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