CompNeuro_HodgkinHuxley Dr. Cian O’Donnell cian.odonnell@bristol.ac.uk The Hodgkin-Huxley model COMS30127: Computational Neuroscience mailto:cian.odonnell@bristol.ac.uk?subject= Questions you may have that we will answer today • “What is the Hodgkin-Huxley model and why do I need to know about it?” • “Who were Hodgkin and Huxley?” • “What does the model consist of?” • “What does it do?” • […]
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CS233 Lab 2 Handout “Beware of bugs in the above code; I have only proved it correct, not tried it.” – Donald E. Knuth Learning Objectives 1. Combinational logic design. 2. Using bitwise logical and shifting operations in a high-level language like C++. Work that needs to be handed in These files need to be
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06_Dropout_and_maxout-checkpoint Dropout and maxout¶ In this lab we will explore the methods of dropout, a regularisation method which stochastically drops out activations from the model during training, and maxout, another non-linear transformation that can be used in multiple layer models. This is based on material covered in the fifth lecture slides. Exercise 1: Implementing a
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CS124 Lecture 5 Spring 2011 Minimum Spanning Trees A tree is an undirected graph which is connected and acyclic. It is easy to show that if graph G(V,E) that satisfies any two of the following properties also satisfies the third, and is therefore a tree: • G(V,E) is connected • G(V,E) is acyclic • |E|
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MLP Courseworks 3 & 4 Due: 2017-02-16 (cw3); 2017-03-16 (cw4) Machine Learning Practical: Courseworks 3 & 4 Release date Friday 27 January 2017 Due dates 1. Baseline experiments (Coursework 3) – 16:00 Thursday 16th February 2017 2. Advanced experiments (Coursework 4) – 16:00 Tuesday 21st March 2017 (deadline extended) 1 Introduction Courseworks 3 & 4
MLP Coursework 1 Due: 27 October 2016 Machine Learning Practical: Coursework 1 Release date: Monday 10th October 2016 Due date: 16:00 Thursday 27th October 2016 Introduction This coursework is concerned with training multi-layer networks to address the MNIST digit classification problem. It builds on the material covered in the first three lab notebooks and the
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Distributed Systems COMP90015 2017 SM1 Project 1 – EZShare Resource Sharing Network Introduction In Project 1 we will build a resource sharing network that consists of servers, which can communicate with each other, and clients which can communicate with the servers. The system will be called EZShare. In typical usage, each user that wants to
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11_Radiosity COMP3421 Radiosity, Splines, Colour The lighting equation we looked at earlier only handled direct lighting from sources: We added an ambient fudge term to account for all other light in the scene. Without this term, surfaces not facing a light source are black. Recap: Global Lighting Global lighting In reality, the light falling on
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07_Autoencoders Autoencoders¶ In this notebook we will explore autoencoder models. These are models in which the inputs are encoded to some intermediate representation before this representation is then decoded to try to reconstruct the inputs. They are example of a model which uses an unsupervised training method and are both interesting as a model in
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CM10228 Coursework 1 Dungeon of Doom – Part 2 February 10, 2017 1 Introduction Due Date: 17.00 on 3rd March 2017 Overall, your mark in Programming 2 is composed of, 1. 50% coursework, 2. 50% exam. The coursework component (part 1. above) is made up of three exercises (CW1, CW2 and CW3) each of which
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