I am looking for good papers on the possibility that dendritic trees may compute derivatives of functions but haven’t found anything so far. Such computations may be very useful for both learning and closed-loop control. Are there papers I have overlooked?
You mean the temporal derivative of an input waveform? Or something else?
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I am interested in a general process for dendritic integration where the synaptic inputs are the function values f_i of a function of several variables and the output of the dendritic computation is the partial derivative of f_i with respect to one or more variables.
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In general, this may be a function learned by a network of neurons. So I think this definition of
@KordingLab might be a special case: https://twitter.com/KordingLab/status/1220101640432254976 … If spike trains can encode ordered pairs (x,f(x))...it should be possible. - 11 more replies
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I meant dA/dC where A is output activity of a neuron and C is a local channel or synapse and the neuron is nonlinear. Maybe I mistook the question.
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I don’t understand the question yet I think.
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