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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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Donald Hebb's postulate only applies when two neurons are already connected. It seems you are asking more specifically, 'when two neurons are not already connected and they want to connect, how and why?' Correct? In this case, we do not know. StrangeLoop mentions it is due to the location of the neurons and the spreading of activation. Yes these might be ...

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has made it possible to have perfect input/output to the brain from a computer Perfect? Definitely not: the complexities of optogenetics of a single mm square of cortex, of a mouse lets say, are extremely complex. As Chuck mentions, many neurons/synapses may be activated by a single LASER and current technologies allow only a few different LASER ...

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Anology Taking the analogy and calculations directly, you are assuming that the fundamental computing unit of the brain is the neuron; we do not know if this is true. It could be a cortical column, a group of several neurons, the neuron, a dendritic branch (a fascinating review paper!), a synapse, receptors or neurotransmitter vesicles (how about glial ...

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