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?
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@YiotaPoirazi,@trabranco,@TonyZador,@KordingLab,@AdamMarblestone,@tyrell_turing,@jbimaknee2 replies 0 retweets 1 likeShow this thread -
Replying to @bayesianbrain @YiotaPoirazi and
It's never been written about. It's very obvious and I have been talking anting to write that paper literally for 15 years. Would be great to write!
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Replying to @KordingLab @bayesianbrain and
You mean the temporal derivative of an input waveform? Or something else?
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Replying to @AdamMarblestone @KordingLab and
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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Replying to @bayesianbrain @AdamMarblestone and
Aidan Rocke Retweeted KordingLab 👨💻 🧠∇ 🔬 📈, 🏋️♂️ ⛷️ 🏂 🛹 🕺 ⛰️ ☕ 🦖
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.Aidan Rocke added,
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Replying to @bayesianbrain @KordingLab and
Still too abstract for me — need a concrete example say a fxn of 2 variables.
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Replying to @AdamMarblestone @bayesianbrain and
I am a synapse somewhere in a neuron. I participate locally in potential NMDA spikes. My influence on the soma may be shunted at times. etc. A spike has just happened. Can I know if it would have happened without me. Iwould the spike have been more likely if I had a larger weight
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This version makes sense.
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