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[Illinois]: Posterior probability of a target given single-sensory input (Bayes')
28 Jun 2013 | | Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node
Computes the posterior probability of a target given sensory input of one modality (i.e., visual)
[Illinois]: Posterior probability of a target given input for two senses (Bayes')
Computes the posterior probability of a target given sensory input of two modalities (i.e., visual and auditory)
[Illinois]: Posterior probability of a target given input for two senses (delta)
Trains a single sigmoidal unit using the delta rule to estimate posterior target probability given sensory input of two modalities (i.e., visual and auditory)
[Illinois]: Posterior probabilities of hypothetical fish classes
Computes the posterior probabilities of each of three hypothetical fish classes using Bayes' rule
[Illinois]: Fish classification using back-propagation
Trains a three-layered network of sigmoidal units using back-propagation to classify fish according to their lengths
[Illinois]: Sigmoidal unit training with the delta rule
26 Jun 2013 | | Contributor(s):: Lisa Sproat, NanoBio Node, Jessica S Johnson
Uses the delta rule to train a single sigmoidal unit with feedback to simulate the responses of neurons in the parabigeminal nucleus
[Illinois]: Predictor-corrector simulation of parabigeminal nucleus neural responses
24 Jun 2013 | | Contributor(s):: Lisa Sproat, NanoBio Node, Jessica S Johnson
Implements a predictor-corrector simulation of the responses of neurons in the parabigeminal nucleus
[Illinois]: Midbrain dopamine neuron responses to temporal-difference learning
21 Jun 2013 | | Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node
Simulates the responses of midbrain dopamine neurons using temporal difference learning
Locust-flight central pattern generator
06 Jun 2013 | | Contributor(s):: Lisa L Sproat, NanoBio Node, Jessica S Johnson
Implements a linear version of Wilson's model of the locust-flight central pattern generator
[Illinois]: Two leaky integrators in series
31 May 2013 | | Contributor(s):: Lisa L Sproat, John Feser, Jessica S Johnson, NanoBio Node
Implements a model having two units (leaky integrators) in series, each with recurrent, excitatory self-connections allowing the units to exert positive feedback on themselves
Unit Cell Ranking Tasks
14 Oct 2012 | | Contributor(s):: Tanya Faltens
This set of ranking tasks is designed to help the learner work with and understand some of the features of common structures: planar densities, atomic densities, atomic packing factor and coordination numbers of different planes and unit cells. There are four pages-- be sure to click on...
Ranking Task Warm Up -- exponentials and natural logs
05 May 2012 | | Contributor(s):: Tanya Faltens
Class warm up activity to be done in pairs. Students evaluate each of 8 terms and rank them from lowest to highest value. Some terms may have equal value. I have found that this is surprisingly challenging for most of my students. Bonus points can be given to the first team to complete the task...
The Bohr Model of the Atom
The Bohr Model of the Atom
A model proposed by Niels Bohr to support his hypothesis about electrons in a hydrogen atom. This is a model of the atomic structure in which...
The Band Gap Energy and Material Classification Lesson
The Band Gap Energy
The band gap energy is the energy needed to break a bond in the crystal. When a bond is broken, the electron has absorbed enough energy to leave the valence band and...
Bulk-Charge Theory Lesson
Lesson Topic: Bulk-Charge Theory
Objective of Lesson: To understand the limitations of the two ID-VD-VG relationships, bulk-charge theory and square-law theory.
Reading Assignment: Section...
The Energy Band Model Lesson
The Energy Band Model
Throughout the book, the energy band model, or band diagram, is used to describe the behavior of electrons and holes to externally applied forces like...
Square-Law Theory Lesson
Lesson Topic: Square-Law Theory
Objective of Lesson: A little bit of exposure to the current-voltage models used for MOSFETs.
Reading Assignment: Section 17.2.2