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To clear this up, there is a distinction between phase difference and phase synchronization. Phase synchronization = is the phase difference constant with time? One crude way to measure phase synchronization would be to partition long signals $x(t)$ and $y(t)$ into many disjoint windows, compute the phase difference in each window using either Method 1 or ...


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Methods 1 and 2 are matematically equivalent, but I have experienced that method 2 is computationally more efficient for high sampling rates and large ranges of delay (notice that method 2 does not require a loop).


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The Neurological Engineering Framework does not explicitly state a mechanism for memory. There is no "hard-drive" in the brain for easy retrieval and access. Rather, memory is captured in the connection weights between neural populations and the dynamics of the network. In the Hierarchical Reinforcement Learning example, linked to in the previous question, ...


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To review how neurons encode information, please check out these class notes review encoding. In those notes, you'll notice the intercept $J_{bias}$ and the maximum firing rate $\alpha$ are randomly selected when encoding functions in large populations of neurons. These variations can account for heterogeneity in attributes of neurons.


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At this point in time, the difference in neurotransmitter types affect the synaptic time constant (i.e. the filter on the incoming spike train) between neurons in Nengo. See Neural Engineering p.112 and these notes (see the section called "Biologically plausible filter") from a course covering the book. Hypothetically, it would be possible to implement a ...


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If you don't have a strong background in circuits & signals, I highly recommend: Circuits, Signals, and Systems for Bioengineers: A MATLAB-Based Introduction (Biomedical Engineering) by John Semmlow (Mar 21, 2005). You can also grab some Schaum's Outlines along the way if you want added practice. And if you enjoy the circuits section, grab an ...


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Signals and System by Oppenheim (and others) was developed while he was teaching 6.003 at MIT. Similarly Foundations of Analog and Digital Electronic Circuits by Agarwal (and others) was developed while he was teaching 6.002 at MIT. Circuits, Signals and Systems by Siebert was written while he was teaching at MIT. Siebert was before my time, but I believe ...


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Neural networks constitute one (very important) level of organization that is modelled computationally in brain research. Computational neuroscience attempts to make these as biologically realistic as possible, often creating models that operate at multiple levels, such as having the neural networks exhibit electrochemical dynamics - something that is ...


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First, a definition, which is basically what you said but refers to elements in the domain of the function: "A learning algorithm has high variance for a particular input x if it predicts different output values when trained on different training sets." So, in order to have zero variance, the machine/NN must output the exact same value for x across training ...



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