Resources: All

Submit a resource

Find a resource

  1. [Illinois]: Perturbative Reinforcement Learning to Develop Distributed Representations

    Tools | 10 Jul 2013 | Contributor(s): AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool trains three-layered networks of sigmoidal units to associate patterns.

  2. [Illinois]: Perturbative Reinforcement Learning Using Directed Drift

    Tools | 10 Jul 2013 | Contributor(s): AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool trains two-layered networks of sigmoidal units to associate patterns using a real-valued adaptation of the directed drift algorithm.

  3. [Illinois]: Posterior probabilities of hypothetical fish classes

    Tools | 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

  4. [Illinois]: Posterior probability of a target given input for two senses (Bayes')

    Tools | 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)

  5. [Illinois]: Posterior probability of a target given input for two senses (delta)

    Tools | 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)

  6. [Illinois]: Posterior probability of a target given single-sensory input (Bayes')

    Tools | 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)

  7. [Illinois]: Posterior target probability given single-sensory input (delta rule)

    Tools | 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 one modality (i.e., visual)

  8. [Illinois]: Predict Correct Set Up

    Tools | 15 Jul 2013 | Contributor(s): Bara Saadah, Nahil Sobh, AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool sets up a predictor-corrector model of target tracking

  9. [Illinois]: Predictor-corrector simulation of parabigeminal nucleus neural responses

    Tools | 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

  10. [Illinois]: Running Average

    Tools | 10 Jul 2013 | Contributor(s): Bara Saadah, Nahil Sobh, Jessica S Johnson, NanoBio Node

    This tool implements a running average of a noise series.

  11. [Illinois]: Sigmoidal unit training with the delta rule

    Tools | 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

  12. [Illinois]: Temporal Difference, Iterative Dynamic Programming, and Least Mean Squares

    Tools | 11 Jul 2013 | Contributor(s): Bara Saadah, Nahil Sobh, AbderRahman N Sobh, Jessica S Johnson

    This tool updates state values using the Temporal Difference Algorithm.

  13. [Illinois]: Two leaky integrators in series

    Tools | 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

  14. [Illinois]: Velocity storage and leakage

    Tools | 04 Jun 2013 | Contributor(s): Lisa L Sproat, Jessica S Johnson, NanoBio Node

    Implements the parallel-pathway and positive-feedback models of velocity storage, and the negative-feedback model of velocity leakage

  15. [Poster] An End-to-End Approach: Technology-Agnostic Compact Models for Solar Cells

    Presentation Materials | 25 May 2016 | Contributor(s): Xingshu Sun

  16. [Poster] Compact Modeling Of Electronic Biosensors

    Presentation Materials | 25 May 2016 | Contributor(s): Piyush Dak

  17. [Poster] Connecting Compact Models to Systems Performance Assessment - A Case Study of 32-bit Micro-processors at 5-nm Technology Node Enabled by NEEDS models

    Presentation Materials | 25 May 2016 | Contributor(s): Chi-Shuen Lee

  18. [Poster] MESH Nano-Oscillator: All Electrical Doubly Tunable Spintronic Oscillator

    Presentation Materials | 25 May 2016 | Contributor(s): Kerem Yunus Camsari

  19. [Poster] MIT Virtual Source GaNFET Model: RF Circuit Design and Experimental Verification

    Presentation Materials | 25 May 2016 | Contributor(s): Ujwal Radhakrishna

  20. [Poster] Multiphysics Modeling and Simulation in Berkeley MAPP

    Presentation Materials | 25 May 2016 | Contributor(s): Tianshi Wang