Cmptatina Cgnitive Nerscience Lab计算认知神经科学实验室.ppt

Computational Cognitive Neuroscience Lab Today: Model Learning * Computational Cognitive Neuroscience Lab Today: Homework is due Friday, Feb 17 Chapter 4 homework is shorter than the last one! Undergrads omit 4.4, 4.5, 4.7c, 4.7d * Hebbian Learning “Neurons that fire together, wire together” Correlations between sending and receiving activity strengthens the connection between them “Don’t fire together, unwire” Anti-correlation between sending and receiving activity weakens the connection * LTP/D via NMDA receptors NMDA receptors allow calcium to enter the (postsynaptic) cell NMDA are blocked by Mg+ ions, which are cast off when the membrane potential increases Glutamate (excitatory) binds to unblocked NMDA receptor, causes structural change that allows Ca++ to pass through * Calcium and Synapses Calcium initiates multiple chemical pathways, dependent on the level of calcium Low Ca++ long term depression (LTD) High Ca++ long term potentiation (LTP) LTP/D effects: new postsynaptic receptors, incresed dendritic spine size, or increased presynaptc release processes (via retrograde messenger) * Fixing Hebbian learning Hebbian learning results in infinite weights! Oja’s normalization (savg_corr) When to learn? Conditional PCA--learn only when you see something interesting A single unit hogs everything? kWTA and Contrast enhancement -- specialization * Principal Components Analysis (PCA) Principal, as in primary, not principle, as in some idea PCA seeks a linear combination of variables such that maximum variance is extracted from the variables. It then removes this variance and seeks a second linear combination which explains the maximum proportion of the remaining variance, and so on until you run out of variance. * PCA continued This is like linear regression, except you take the whole collection of variables (vector) and correlate it with itself to make a matrix. This is kind of like linear regression, where a whole collection of variables is regressed on itself The