<p>I don't know much about this, but here goes anyway.</p>

<p>I heard that the reason backprop isnt biologically plausible is that it requires global control/coordination for the propagation of the gradients. (verification of this would be nice...)</p>

<p>The <a href="https://arxiv.org/abs/1608.05343" rel="nofollow noreferrer">decoupled neural interface</a> seems to solve this problem by making gradient propagation local (using a finite difference approximation to break the dependency). So could be biologically plausible, while keeping much of the flavour of backprop.</p>

<p>Also, in my mind the bi-directional argument isnt much of an issue. As you can just have another neuron/path doing the backward step/propagation?</p>

CC BY-SA 3.0
2017-01-08T07:57:38.007Z
Right. The "another neuron/path doing the backward step/propagation" is exactly what those papers I cited in my original question are attempting to do. However, I'm really not clear on how well they're doing that.
Seanny123
CC BY-SA 3.0
2017-01-08T08:52:01.753Z
Oh yea. That reminds me of another [paper](https://arxiv.org/abs/1412.7525). You can use autoencoders to approximate the backward propagation required.
Alexander Telfar
CC BY-SA 3.0
2017-01-08T09:49:30.623Z
I'll add that paper to the list. I'm reading it now, but I'm having a hard time understanding it completely.
Seanny123
CC BY-SA 3.0
2017-01-07T21:32:36.190Z
2017-01-07T21:32:36.190Z
Alexander Telfar
<p>While a backpropagating action potential can presumably cause changes in the weight of the presynaptic connections, there is no simple mechanism for an error signal to propagate through multiple layers of neurons, as in the computer backpropagation algorithm. However, simple linear topologies have shown that effective computation is possible through signal backpropagation in this biological sense.</p>

<p>From wikipedia
<a href="https://en.wikipedia.org/wiki/Neural_backpropagation" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Neural_backpropagation</a></p>

<p>It is a matter of time and more precise research. I am sure that this is the main clue</p>

CC BY-SA 4.0
2018-07-11T01:54:52.097Z
2018-07-11T01:54:52.097Z
hjoab
<h1>Biological Plausibility of Back-Prop</h1>

<p>No, the algorithm of back-prop (BP) isn't biologically plausible. However, there are other means which involve propagating the error through multiple layers of neurons in a feed-forward network which are biologically plausible. But before we evaluate these substitutions, let's review why back-prop isn't biologically plausible [1]:</p>

<h2>1. Weight Transport Problem</h2>

<p>BP uses the same weights for forward-pass and backward error propagation. But synapses are uni-directional.</p>

<h2>2. Derivative Transport Problem</h2>

<p>The derivative of each neuron is used to modulate the error signal in BP. Additionally, the derivative is propagated through each layer. How this derivative calculated by neurons and then propagated, is unclear.</p>

<h2>3. Linear Feedback Problem</h2>

<p>The BP feedback path is linear, but neurons aren't linear.</p>

<h2>4. Spiking Problem</h2>

<p>Neurons spike. BP is defined for rates.</p>

<h2>5. Timing Problem</h2>

<p>The BP error signal is expected to propagate instantaneously. Calculations and signal transmission in the brain do not happen instantaneously.</p>

<h2>6. Target Problem</h2>

<p>Most applications of BP rely on many examples with given labels, but where do those labels come from?</p>

<h1>Replacements for Back-Prop</h1>

<p>Most replacements for back-prop solve a subset of these problems, however none of them see to solve all of them. In "<a href="https://uwspace.uwaterloo.ca/handle/10012/12819" rel="noreferrer">Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition</a>", a method is constructed using <a href="https://psychology.stackexchange.com/questions/tagged/nef">the NEF</a> that solves all problems except for the Target Problem. This means there are no replacements for BP that work for all cases where BP is applied, which is to be expected given this is what usually happens when biological plausibility is emphasized.</p>

<h1>Beyond Back-Prop</h1>

<p>However, it should be noted that there is more than back-propagation through fully-connected networks involved in Deep Learning. Specifically, the are a number of different architectures used, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).</p>

<ul>
<li>CNNs aren't biologically plausible due to their reliance on weights being exactly equal across multiple locations.</li>
<li>RNNs rely on Back-Propagation Through Time (BPTT), of which there is currently no biologically plausible analogue [2].</li>
</ul>

<p>This confusion between Deep Learning, neural network architectures and back-prop is reason that Yann LeCunn is trying to get people to adopt the term "Differentiable Computing" instead of "Deep Learning". At which point, the question must shift from "Is Deep Learning biologically plausible?" into "What aspects of Differentiable Computing are biologically plausible?" Which is much harder to answer, but at least we have a better question than when we started!</p>

<p>[1] Summarized from Eric Hunsberger's PhD Thesis "<a href="https://uwspace.uwaterloo.ca/handle/10012/12819" rel="noreferrer">Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition</a>"</p>

<p>[2] That being said, there are other means of learning (supposedly) biologically plausible recurrent neural network weights (<a href="https://psychology.stackexchange.com/q/17393/4397">FORCE training</a>, Conceptors), however they don't resemble BPTT in any way. The discussion of these methods is outside the scope of this question.</p>

CC BY-SA 4.0
2018-05-21T00:30:40.563Z
2018-11-13T14:52:26.543Z
2018-11-13T14:52:26.543Z
Seanny123
<p>One of the common criticisms of Deep Learning is it's training algorithms, back-propagation of error (back-prop), has no <a href="https://psychology.stackexchange.com/q/8923/4397">biologically plausible</a> implementation, despite evidence of something like it occurring in the brain. The default implementation is considered biologically implausible due to it's reliance on bi-directional synapses. However, there's been a lot of publications published showing implementations that should be plausible. Specifically, from <a href="http://biorxiv.org/content/early/2016/08/22/058545" rel="nofollow noreferrer">section 2.2.2 of the review &quot;Towards an integration of deep learning and neuroscience&quot;</a> and some discussion I've had:</p>
<ol>
<li><a href="http://www.nature.com/articles/ncomms13276" rel="nofollow noreferrer">Random synaptic feedback weights support error backpropagation for
deep learning</a></li>
<li><a href="https://arxiv.org/abs/1609.01596" rel="nofollow noreferrer">Direct Feedback Alignment</a> (related to the
previous random weights idea) and it's inclusion in the <a href="https://fzenke.net/index.php/2017/02/19/learning-in-multi-layer-spiking-neural-networks/" rel="nofollow noreferrer">Superspike algorithm</a></li>
<li><a href="https://arxiv.org/abs/1610.00161" rel="nofollow noreferrer">Deep learning with segregated
dendrites</a></li>
<li><a href="https://arxiv.org/abs/1502.04156" rel="nofollow noreferrer">Spike timing dependent plasticity (STDP) with
iterative inference and target propagation</a> which is based off <a href="https://arxiv.org/abs/1412.7525" rel="nofollow noreferrer">Difference Target Propagation</a></li>
<li>Kickback from &quot;<a href="https://arxiv.org/abs/1411.6191" rel="nofollow noreferrer">Kickback cuts Backprop's red-tape: Biologically plausible credit assignment in neural networks</a>&quot;</li>
<li>Generalized Recirculation from &quot;<a href="http://psych.colorado.edu/%7Eoreilly/papers/oreilly96_generec_nc.pdf" rel="nofollow noreferrer">Biologically Plausible Error-driven Learning using Local Activation Differences: The Generalized Recirculation Algorithm</a>&quot;</li>
<li>Contrastive Hebbian Learning from &quot;<a href="https://www.ncbi.nlm.nih.gov/pubmed/12590814" rel="nofollow noreferrer">Equivalence of backpropagation and contrastive Hebbian learning in a layered network</a>&quot;</li>
<li>Complex neurons from &quot;<a href="https://link.springer.com/article/10.1023/A:1013776130161" rel="nofollow noreferrer">Supervised and unsupervised learning with two sites of synaptic integration</a>&quot;</li>
<li>Using an attention mechanism from &quot;<a href="https://arxiv.org/abs/1811.01768" rel="nofollow noreferrer">A Biologically Plausible Learning Rule for Deep Learning in the Brain</a>&quot;</li>
<li>Layer-specific target from &quot;<a href="https://arxiv.org/abs/2006.06438" rel="nofollow noreferrer">GAIT-prop: A biologically plausible learning rulederived from backpropagation of error</a>&quot;</li>
</ol>
<p>Are any of these implementable in spiking neurons, able to scale to more than two layers and have been shown to work on normal Deep Learning benchmarks such as MNIST or ImageNet?</p>

CC BY-SA 3.0
2016-11-23T13:51:12.423Z
The strength of DL is in its predictive power, not in faithful reimplementation of the biological prototype. It is not clear how relevant is "biological implementability" backprop.
sds
CC BY-SA 3.0
2017-04-14T01:15:34.330Z
related https://biology.stackexchange.com/q/54147/460
Memming
CC BY-SA 4.0
2024-11-10T20:31:23.943Z
Just noting that [neural backpropagation](https://en.wikipedia.org/wiki/Neural_backpropagation) does appear to be biologically plausible, but this should not be mistaken for what deep learning does.
Arnon Weinberg
<theoretical-neuroscience><computational-modeling><neural-network>
Is back-prop biologically plausible?
Seanny123
# Is back-prop biologically plausible?

(Asked by: username_0 on 2016-11-21)

One of the common criticisms of Deep Learning is it's training algorithms, back-propagation of error (back-prop), has no [biologically plausible](https://psychology.stackexchange.com/q/8923/4397) implementation, despite evidence of something like it occurring in the brain. The default implementation is considered biologically implausible due to it's reliance on bi-directional synapses. However, there's been a lot of publications published showing implementations that should be plausible. Specifically, from [section 2.2.2 of the review "Towards an integration of deep learning and neuroscience"](http://biorxiv.org/content/early/2016/08/22/058545) and some discussion I've had:

1. [Random synaptic feedback weights support error backpropagation for
   deep learning](http://www.nature.com/articles/ncomms13276)
2. [Direct Feedback Alignment](https://arxiv.org/abs/1609.01596) (related to the
   previous random weights idea) and it's inclusion in the [Superspike algorithm](https://fzenke.net/index.php/2017/02/19/learning-in-multi-layer-spiking-neural-networks/)
3. [Deep learning with segregated
   dendrites](https://arxiv.org/abs/1610.00161)
4. [Spike timing dependent plasticity (STDP) with
   iterative inference and target propagation](https://arxiv.org/abs/1502.04156) which is based off [Difference Target Propagation](https://arxiv.org/abs/1412.7525)
5. Kickback from "[Kickback cuts Backprop's red-tape: Biologically plausible credit assignment in neural networks](https://arxiv.org/abs/1411.6191)"
6. Generalized Recirculation from "[Biologically Plausible Error-driven Learning using Local Activation Differences: The Generalized Recirculation Algorithm](http://psych.colorado.edu/%7Eoreilly/papers/oreilly96_generec_nc.pdf)"
7. Contrastive Hebbian Learning from "[Equivalence of backpropagation and contrastive Hebbian learning in a layered network](https://www.ncbi.nlm.nih.gov/pubmed/12590814)"
8. Complex neurons from "[Supervised and unsupervised learning with two sites of synaptic integration](https://link.springer.com/article/10.1023/A:1013776130161)"
9. Using an attention mechanism from "[A Biologically Plausible Learning Rule for Deep Learning in the Brain](https://arxiv.org/abs/1811.01768)"
10. Layer-specific target from "[GAIT-prop: A biologically plausible learning rulederived from backpropagation of error](https://arxiv.org/abs/2006.06438)"

Are any of these implementable in spiking neurons, able to scale to more than two layers and have been shown to work on normal Deep Learning benchmarks such as MNIST or ImageNet?

**Tags:** theoretical-neuroscience, computational-modeling, neural-network

> **Comments:**
> **username_1**: The strength of DL is in its predictive power, not in faithful reimplementation of the biological prototype. It is not clear how relevant is "biological implementability" backprop.  
> **username_2**: related https://biology.stackexchange.com/q/54147/460  
> **username_3**: Just noting that [neural backpropagation](https://en.wikipedia.org/wiki/Neural_backpropagation) does appear to be biologically plausible, but this should not be mistaken for what deep learning does.  

---

## 3 answers

### Answer by username_0 on 2018-05-21. Score: 11 | Accepted answer

# Biological Plausibility of Back-Prop

No, the algorithm of back-prop (BP) isn't biologically plausible. However, there are other means which involve propagating the error through multiple layers of neurons in a feed-forward network which are biologically plausible. But before we evaluate these substitutions, let's review why back-prop isn't biologically plausible [1]:

## 1. Weight Transport Problem

BP uses the same weights for forward-pass and backward error propagation. But synapses are uni-directional.

## 2. Derivative Transport Problem

The derivative of each neuron is used to modulate the error signal in BP. Additionally, the derivative is propagated through each layer. How this derivative calculated by neurons and then propagated, is unclear.

## 3. Linear Feedback Problem

The BP feedback path is linear, but neurons aren't linear.

## 4. Spiking Problem

Neurons spike. BP is defined for rates.

## 5. Timing Problem

The BP error signal is expected to propagate instantaneously. Calculations and signal transmission in the brain do not happen instantaneously.

## 6. Target Problem

Most applications of BP rely on many examples with given labels, but where do those labels come from?

# Replacements for Back-Prop

Most replacements for back-prop solve a subset of these problems, however none of them see to solve all of them. In "[Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition](https://uwspace.uwaterloo.ca/handle/10012/12819)", a method is constructed using [the NEF](https://psychology.stackexchange.com/questions/tagged/nef) that solves all problems except for the Target Problem. This means there are no replacements for BP that work for all cases where BP is applied, which is to be expected given this is what usually happens when biological plausibility is emphasized.

# Beyond Back-Prop

However, it should be noted that there is more than back-propagation through fully-connected networks involved in Deep Learning. Specifically, the are a number of different architectures used, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

* CNNs aren't biologically plausible due to their reliance on weights being exactly equal across multiple locations.
* RNNs rely on Back-Propagation Through Time (BPTT), of which there is currently no biologically plausible analogue [2].

This confusion between Deep Learning, neural network architectures and back-prop is reason that Yann LeCunn is trying to get people to adopt the term "Differentiable Computing" instead of "Deep Learning". At which point, the question must shift from "Is Deep Learning biologically plausible?" into "What aspects of Differentiable Computing are biologically plausible?" Which is much harder to answer, but at least we have a better question than when we started!

[1] Summarized from Eric Hunsberger's PhD Thesis "[Spiking Deep Neural Networks: Engineered and Biological Approaches to Object Recognition](https://uwspace.uwaterloo.ca/handle/10012/12819)"

[2] That being said, there are other means of learning (supposedly) biologically plausible recurrent neural network weights ([FORCE training](https://psychology.stackexchange.com/q/17393/4397), Conceptors), however they don't resemble BPTT in any way. The discussion of these methods is outside the scope of this question.


---

### Answer by username_4 on 2017-01-07. Score: 3

I don't know much about this, but here goes anyway.

I heard that the reason backprop isnt biologically plausible is that it requires global control/coordination for the propagation of the gradients. (verification of this would be nice...)

The [decoupled neural interface](https://arxiv.org/abs/1608.05343) seems to solve this problem by making gradient propagation local (using a finite difference approximation to break the dependency). So could be biologically plausible, while keeping much of the flavour of backprop.

Also, in my mind the bi-directional argument isnt much of an issue. As you can just have another neuron/path doing the backward step/propagation?

> **Comments:**
> **username_0**: Right. The "another neuron/path doing the backward step/propagation" is exactly what those papers I cited in my original question are attempting to do. However, I'm really not clear on how well they're doing that.  
> **username_4**: Oh yea. That reminds me of another [paper](https://arxiv.org/abs/1412.7525). You can use autoencoders to approximate the backward propagation required.  
> **username_0**: I'll add that paper to the list. I'm reading it now, but I'm having a hard time understanding it completely.  

---

### Answer by username_5 on 2018-07-11. Score: 0

While a backpropagating action potential can presumably cause changes in the weight of the presynaptic connections, there is no simple mechanism for an error signal to propagate through multiple layers of neurons, as in the computer backpropagation algorithm. However, simple linear topologies have shown that effective computation is possible through signal backpropagation in this biological sense.

From wikipedia
<https://en.wikipedia.org/wiki/Neural_backpropagation>

It is a matter of time and more precise research. I am sure that this is the main clue


---

