Backpropagation is a leaky abstraction
Edit: hmm not sure why this comment is getting downvoted.<p>Backprop isn&#x27;t just a leaky abstraction. There&#x27;s not really any evidence yet that biological neural networks use anything like backprop. So its important that students be taught the low-level aspects of the current state of the art so that better architectures can be invented in the future.<p>(Note: at the very end of this comment, I am leaving a link to one hypothesis about how something like backprop may happen at the dendritic level.)<p>I&#x27;ve been doing a deep dive on ML and bio neurons in the last week or so to try and get up to speed. The feedforward networks with backprop that are doing such awesome work right now share almost nothing in common with actual neurons.<p>Just some differences: (neuron == bio, node == artificial)<p>Terms: Neuron resting potential is about -70mV. Action potential (threshold) is about -55mV.<p>1) Neurons are spikey and don&#x27;t have weights. A neuron sums all of its inputs to reach a threshold. When that threshold is reached, an electric current (action potential) is spread equally to all synaptic terminals. A node has a weight value for each input node that it multiplies by the value received from the input node. All of these values are then summed and passed to the connected nodes in the next layer.<p>2) There are multiple types of neurons, but for simplification there are 2 types that called &quot;excitatory&quot; and &quot;inhibatory&quot;. A neuron can <i>mostly</i> only emit &quot;positive&quot; or &quot;negative&quot; signals via the synapse. There are different neurotransmitters that either add or subtract to the voltage potential for each dendritic input. The total sums work together to cross the threshold voltage. I say mostly because there is some evidence for a minority of neurons to be able to release neurotransmitters from both groups.<p>3) Artificial networks are generally summation based with no inclusion of time. Bio networks use summation and summation over time together to determine if they should fire or not. If I recall correctly, a neuron can repeatedly fire about 300 times per second. A dendritic input can last for up to 1.5 milliseconds. So if a neuron gets enough positive inputs at the same time, or collects enough over time, it will fire.<p>I haven&#x27;t found any hypotheses or experiments that try to explain how reinforcement learning takes place in neurons yet in the absence of back propagation. Pretty sure that information is out there but I haven&#x27;t run across it.<p>Overall I think that there will be multiple engineering approaches to AI, just like there are to construction and flight. We understand how birds and bees fly, but we don&#x27;t build planes the same way.<p>Its important to remember than cognition is based in physics just like any other physical system. When the principles are well understood, there are multiple avenues to using them.<p>Here is a short collection of links that I&#x27;ve been finding helpful.<p><a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Neurotransmission" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Neurotransmission</a><p><a href="http:&#x2F;&#x2F;neuroscience.uth.tmc.edu&#x2F;s1&#x2F;introduction.html" rel="nofollow">http:&#x2F;&#x2F;neuroscience.uth.tmc.edu&#x2F;s1&#x2F;introduction.html</a><p><a href="https:&#x2F;&#x2F;www.quora.com&#x2F;Can-a-synapse-change-from-being-inhibitory-to-excitatory-What-is-the-mechanism-behind-it" rel="nofollow">https:&#x2F;&#x2F;www.quora.com&#x2F;Can-a-synapse-change-from-being-inhibi...</a><p><a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Dale&#x27;s_principle" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Dale&#x27;s_principle</a><p><a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Neural_backpropagation" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Neural_backpropagation</a>