
Soft, Biocompatible Optoelectronic Interfaces to the Brain
08 Jun 2017   Contributor(s):: John A. Rogers
In this talk, we will describe foundational concepts in physics and materials science for these types of technologies, in 1D, 2D and 3D architectures. Examples in system level demonstrations include experiments on freely moving animals with ‘cellularscale’, injectable optofluidic...

[Illinois] MCB 529 BRH Reproductive Rhythms
07 Jan 2014   Contributor(s):: Megan Mahoney, Martha U. Gillette

[Illinois] MCB 529 BRH Biological Rhythms in Health and Disease
30 Dec 2013   Contributor(s):: Martha U. Gillette
Our major research thrusts are to understand: 1) signals that engage the circadian clockwork in the brain, 2) subcellular microenvironments that shape neuronal dendrites in development and repair, and 3) emergent behaviors of integrated neuronal systems.

[Illinois] MCB 529 BRH Nocturnal and Diurnal Adaptations
30 Dec 2013   Contributor(s):: Rhanor Gillette, Martha U. Gillette

[Illinois] MCB 529 BRH Circadian Control of Liver Function
30 Dec 2013   Contributor(s):: Shelley Tischkau, Martha U. Gillette

[Illinois] MCB 529 BRH Drugs of Abuse and Circadian Rhythms
30 Dec 2013   Contributor(s):: Joshua M Gulley, Martha U. Gillette

[Illinois] MCB 493 Lecture 9: Probability Estimation and Supervised Learning
30 Oct 2013   Contributor(s):: Thomas J. Anastasio
Supervised learning algorithms can train neural units and networks to estimate probabilities and simulate the responses of neurons to multisensory stimulation.

[Illinois] MCB 493 Lecture 14: Future Directions in Neural Systems Modeling
30 Oct 2013   Contributor(s):: Thomas J. Anastasio
In the future, neural systems models will become increasingly complex and will span levels from molecular interactions within neurons to interactions between networks

[Illinois] MCB 493 Neural Systems Modeling
29 Oct 2013   Contributor(s):: Thomas J. Anastasio
The purpose of this independent study is to give students handson experience in using computers to model neural systems. A neural system is a system of interconnected neural elements, or units. Students will use existing computer programs which will simulate real neural systems. They will...

[Illinois] MCB 493 Lecture 11: TemporalDifference Learning and Reward Prediction
29 Oct 2013   Contributor(s):: Thomas J. Anastasio
Temporaldifference learning can train neural networks to estimate the future value of a current state and simulate the responses of neurons involved in reward processing.

[Illinois]: Posterior target probability given singlesensory input (delta rule)
02 Jul 2013   Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node
Trains a single sigmoidal unit using the delta rule to estimate posterior target probability given sensory input of one modality (i.e., visual)

[Illinois]: Posterior probability of a target given singlesensory 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')
28 Jun 2013   Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node
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)
28 Jun 2013   Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node
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
28 Jun 2013   Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node
Computes the posterior probabilities of each of three hypothetical fish classes using Bayes' rule

[Illinois]: Fish classification using backpropagation
28 Jun 2013   Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node
Trains a threelayered network of sigmoidal units using backpropagation 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]: Predictorcorrector simulation of parabigeminal nucleus neural responses
24 Jun 2013   Contributor(s):: Lisa Sproat, NanoBio Node, Jessica S Johnson
Implements a predictorcorrector simulation of the responses of neurons in the parabigeminal nucleus

[Illinois]: Midbrain dopamine neuron responses to temporaldifference learning
21 Jun 2013   Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node
Simulates the responses of midbrain dopamine neurons using temporal difference learning

[Illinois]: Velocity storage and leakage
04 Jun 2013   Contributor(s):: Lisa L Sproat, Jessica S Johnson, NanoBio Node
Implements the parallelpathway and positivefeedback models of velocity storage, and the negativefeedback model of velocity leakage