Linear Algebra for Computation Neuroscience

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  1. Informations.
  2. Dialectic of Sedimentation and Innovation?
  3. Computational Neuroscience | The Center for Brains, Minds & Machines.

September 23, pm - pm NHB 1. A working model for the role of the cerebellum in drug addiction. Have we been ignoring the elephant in the room?


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Quantitative Methods for Neuroscience. Course content and aim This course will provide a broad introduction to basic mathematical and computational tools for a quantitative analysis of neural systems.

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GDC 6. WEL 2. Office hours Tu p. NHB 3.

CSE Introduction to Computational Neuroscience

Overfitting and cross-validation. PCA and applications.

The beauty I see in algebra: Margot Gerritsen at TEDxStanford

Intro to probability. PS7 , SpikeSortingData.

Algebra Linear for Computation Neuroscience

PS8 PS9 , linearneuron1. Basic components of a neural system: neurons, synapses, dendrities, receptive fields Hodgkin-Huxley model and its simulation on a computer Inter-neuron communication and the principle of synaptic learning Fundamentals of Neurobiology.


  1. Quantitative Methods for Neuroscience.
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  4. Computational Neuroscience;

Structure of the brain and brain regions Fundamental concepts of data and signal encoding and processing. Filtering, approximation and interpolation, clustering, principal component analysis Basic neural network architectures: pattern association networks, auto associative networks, feedforward networks, competitive networks, recurrent networks. Plasticity and Learning. Hebb rule, supervised learning, reinforced learning, error-correcting learning, unsupervised learning, competitive learning.