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AdamMarblestone's profile
Adam Marblestone
Adam Marblestone
Adam Marblestone
@AdamMarblestone

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Adam Marblestone

@AdamMarblestone

Technologist, Scientist

adammarblestone.org
Joined February 2009

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    1. Aidan Rocke‏ @bayesianbrain Jan 22
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      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?

      4 replies 3 retweets 6 likes
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    2. Aidan Rocke‏ @bayesianbrain Jan 22
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      cc: @YiotaPoirazi, @trabranco, @TonyZador, @KordingLab, @AdamMarblestone, @tyrell_turing, @jbimaknee

      2 replies 0 retweets 1 like
      Show this thread
    3. KordingLab  👨‍💻 🧠∇ 🔬 📈, 🏋️‍♂️ ⛷️ 🏂 🛹 🕺 ⛰️ ☕ 🦖‏ @KordingLab Jan 22
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      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!

      1 reply 0 retweets 3 likes
    4. Adam Marblestone‏ @AdamMarblestone Jan 22
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      Replying to @KordingLab @bayesianbrain and

      You mean the temporal derivative of an input waveform? Or something else?

      2 replies 0 retweets 0 likes
    5. Aidan Rocke‏ @bayesianbrain Jan 22
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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.

      1 reply 0 retweets 1 like
    6. Aidan Rocke‏ @bayesianbrain Jan 22
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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,

      KordingLab  👨‍💻 🧠∇ 🔬 📈, 🏋️‍♂️ ⛷️ 🏂 🛹 🕺 ⛰️ ☕ 🦖 @KordingLab
      Replying to @AdamMarblestone @bayesianbrain and 5 others
      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.
      1 reply 0 retweets 1 like
    7. Adam Marblestone‏ @AdamMarblestone Jan 22
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      Replying to @bayesianbrain @KordingLab and

      Still too abstract for me — need a concrete example say a fxn of 2 variables.

      2 replies 0 retweets 1 like
    8. Aidan Rocke‏ @bayesianbrain Jan 22
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      Replying to @AdamMarblestone @KordingLab and

      For concreteness, I think dendritic trees may compute the partial derivatives of functions using an algorithm similar to the Cauchy Integral Formula for derivatives. It can be implemented on any binary tree where a large number of local computations occur in parallel.

      1 reply 0 retweets 1 like
    9. Aidan Rocke‏ @bayesianbrain Jan 22
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      Replying to @bayesianbrain @AdamMarblestone and

      For a large number of functions(ex. polynomials, sigmoid, trigonometric) we have geometric convergence in error. I think any proposed algorithm must scale very well with the dimension of the input to the dendritic tree. Very fast convergence implies robustness.

      1 reply 0 retweets 0 likes
      Adam Marblestone‏ @AdamMarblestone Jan 22
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      Replying to @bayesianbrain @KordingLab and

      Like say I am a synapse, the jth in this tree. Function f(x,y). I have an associated x_j, y_j and my input at time t is taken as f(x_j, y_j)? Now what do you want the neuron spike output to represent? Or is what Konrad said the thing you want?

      2:48 PM - 22 Jan 2020
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      • POURCEL Guillaume KordingLab 👨‍💻🧠∇🔬📈,🏋️‍♂️⛷️🏂🛹🕺⛰️☕🦖 Aidan Rocke
      1 reply 0 retweets 3 likes
        1. New conversation
        2. Aidan Rocke‏ @bayesianbrain Jan 22
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          Replying to @AdamMarblestone @KordingLab and

          If we represent these inputs as a vector [f(x_1,y_1),...,f(x_n,y_n)] then the spike output may represent the dot product of [f(x_1,y_1),...,f(x_n,y_n)] and [f_1,...,f_n] where the f_i denote frequencies of different voltage oscillations in a dendritic tree.

          1 reply 0 retweets 0 likes
        3. Aidan Rocke‏ @bayesianbrain Jan 22
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          Replying to @bayesianbrain @AdamMarblestone and

          This formulation is not exact in the sense that it's not Contour Integration but the information I need is there. I think that by averaging these computations we can get a good stochastic estimate of a partial derivative with respect to any x_i or y_i.

          1 reply 0 retweets 0 likes
        4. 5 more replies

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