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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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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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