Computational Cognitive Neuroscience 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 line of best