https://github.com/benlansdell/rdd

python code and jupyter notebooks to reproduce figures from our PLOS Computational Biology paper

https://github.com/benlansdell/rdd

Science Score: 23.0%

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Keywords

causal-inference computational-neuroscience economics leaky-integrate-and-fire-model neural-network
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python code and jupyter notebooks to reproduce figures from our PLOS Computational Biology paper

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causal-inference computational-neuroscience economics leaky-integrate-and-fire-model neural-network
Created almost 9 years ago · Last pushed over 3 years ago
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README.md

Neural spiking for causal inference and learning

Ben Lansdell and Konrad Kording 2023

Abstract: When a neuron is driven beyond its threshold, it spikes. The fact that it does not communicate its continuous membrane potential is usually seen as a computational liability. Here we show that this spiking mechanism allows neurons to produce an unbiased estimate of their causal influence, and a way of approximating gradient descent-based learning. Importantly, neither activity of upstream neurons, which act as confounders, nor downstream non-linearities bias the results. We show how spiking enables neurons to solve causal estimation problems and that local plasticity can approximate gradient descent using spike discontinuity learning.

python code and jupyter notebooks to reproduce figures from our PLOS Computational Biology paper (here).

See the text for more details.

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Owner

  • Name: Ben Lansdell
  • Login: benlansdell
  • Kind: user
  • Location: Santa Fe, NM
  • Company: Health stealth

Machine learning and applied mathematics | Former postdoc @KordingLab UPenn, PhD in applied mathematics @Fairhall-Lab UW

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