<p>One way the biological plausibility of an artificial neural network could be assessed is to look at how much a neural network abstracts away from the behavior of real neurons.</p>

<p>For instance, it is common in psychology and machine learning to use a sigmoidal <a href="http://en.wikipedia.org/wiki/Activation_function">activation function</a> to determine the output of a node. If biological plausibility is a concern, one might prefer to use the <a href="http://en.wikipedia.org/wiki/Hodgkin%E2%80%93Huxley_model">Hodgkin-Huxley model</a>. (See also: <a href="http://en.wikipedia.org/wiki/Biological_neuron_model">other neuronal models</a>).</p>

<p>To many practitioners, biological plausibility is not a primary concern. In most ANNs, each node is not really believed to be implemented by a single neuron in the brain, and thus using a more realistic activation function does not necessarily make the network more biologically realistic.</p>

<p>Personally, I don't think looking at the behavioral accuracy of a biological neural network (compared  to an artificial neural network) says anything about the network's biological plausibility. Using more abstractions might produce more similar behavior due to less overfitting. Another concern is that the ANN could produce the same behavior as a BNN even if the actual computations involved are quite different.</p>

CC BY-SA 3.0
2014-12-27T21:45:01.460Z
2014-12-27T21:45:01.460Z
Jeff
<p>Due to my newness to the field, I can only talk about comparisons of biological plausibility when discussing the <a href="http://compneuro.uwaterloo.ca/research/nef.html" rel="nofollow noreferrer">Neural Engineering Framework</a> (NEF) and functional modeling. What is missing from this answer is a purely bottom-up modelling perspective in the same vein as the <a href="http://bluebrain.epfl.ch/" rel="nofollow noreferrer">Blue-Brain project</a>, but I'll leave that to another user.</p>

<p>One of the claims driving the push towards cognitive modelers using the NEF is the fact that it is more biologically plausible than back-prop ANNs. What is typically discussed is the neural mechanism for encoding, decoding and learning of the NEF has more scientific evidence claiming their plausibility than <a href="https://psychology.stackexchange.com/q/16269/4397">back-prop ANNs</a>.</p>

<p>From a more systematic biologically plausible perspective, the NEF is also used to build neural networks that use Semantic Pointer Architecture, which is also claimed to be more biologically plausible for a number of reasons. First of all, the number of neurons required for most models in significantly less than other proposed models (<a href="https://mindmodeling.org/cogsci2013/papers/0099/paper0099.pdf" rel="nofollow noreferrer">see Eric Crawford's work on knowledge representation</a>). Secondly, modifications to the network mimicking the effects of neurological disorders can be shown to have the same effect of those disorders (<a href="http://www.sciencedirect.com/science/article/pii/S0160289613001542" rel="nofollow noreferrer">see Dan Rasmussen's work on general intelligence</a>).</p>

<p>In terms of the bottom up perspective, the NEF could help here as well. The NEF is neuron-model agnostic, so it can be used with any of the neuron models you mentioned in the question. Hypothetically, it could be possible to create a similar network to one grown on a Neurochip and compare the various inputs and outputs as you suggested, but I've never heard of such a study being done.</p>

CC BY-SA 4.0
2014-12-26T14:19:01.220Z
2018-11-13T14:56:57.687Z
2018-11-13T14:56:57.687Z
Seanny123
<p>I am referring to the <a href="http://en.wikipedia.org/wiki/Computational_neuroscience">computational neuroscience</a> side of neural network research that focuses on biological accuracy.  I've read references to improving biological realism (using say <a href="http://en.wikipedia.org/wiki/Biological_neuron_model">spiking neurons</a> or <a href="http://en.wikipedia.org/wiki/Binding_neuron">binding neurons</a> of various types) by emulating known properties of real neurons (action potentials and what not).  However, I haven't been able to find any detail on how the effectiveness of such attempts is measured empirically.</p>

<p>For example, <a href="http://en.wikipedia.org/wiki/Cultured_neuronal_network">cultured neuronal networks</a> (such as a <a href="http://en.wikipedia.org/wiki/Neurochip">neurochip</a>) could be used as a benchmark: Create an ANN and a BNN with the same number and arrangement of neurons, give them the same inputs, and compare the results...  Has anything like that been attempted?  How is the biological accuracy of ANNs typically measured?</p>

CC BY-SA 3.0
2015-01-27T02:57:06.277Z
I like this question a lot. There seems to be a deeper underlying question of how is the accuracy (or the usefulness) of any mathematical (or computational) model to be measured? For that question, it might be important to notice that there are different [types of models](https://egtheory.wordpress.com/2013/09/08/mathematical-models/) with [different goals](https://egtheory.wordpress.com/2013/12/19/computational-models/), so a single answer might not exist; that might still be true even if restricted to ANNs.
Artem Kaznatcheev
CC BY-SA 3.0
2015-08-12T16:49:28.367Z
Take a look at Ted Berger's work on hippocampal prosthesis. It essentially involves recording signals from a patch of the brain and then processing them for use by another part of the brain. https://www.newscientist.com/article/dn3488-worlds-first-brain-prosthesis-revealed/
Justas
CC BY-SA 3.0
2015-08-12T19:04:40.307Z
Yes, thanks @Justas, that's an old article, I had read an update to it a while ago (http://www.popsci.com/technology/article/2011-06/artificial-memory-chip-rats-can-remember-and-forget-touch-button), and you can read more here: https://en.wikipedia.org/wiki/Hippocampal_prosthesis.  However, I haven't found any information about what type of ANN is used - do you have any idea?  It seems to be something very specialized.
Arnon Weinberg
CC BY-SA 3.0
2015-08-12T19:40:11.993Z
In the original paper (http://iopscience.iop.org/1741-2552/8/4/046017/pdf/1741-2552_8_4_046017.pdf), it describes the model as "A general, Volterra kernel-based strategy for modeling multi-input/multi-output (MIMO) nonlinear dynamics underlying spike train-to-spike train transformations between CA3 and CA1 was established to predict output patterns of CA1 firing pattern from input patterns of CA3 neural activity".
Justas
CC BY-SA 3.0
2015-08-12T19:40:16.990Z
Then in this reference (http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5424199&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5424199) it goes into all the technical details of how they built the MIMO.
Justas
CC BY-SA 3.0
2015-08-12T19:48:29.460Z
Thanks a lot @Justas, I'll check it out!  It looks like it isn't a learning/dynamic network, but a static system that emulates spike patterns, so not technically an ANN.
Arnon Weinberg
CC BY-SA 4.0
2022-05-31T21:25:22.167Z
An interesting experiment using an ANN to model a single biological neuron: https://twitter.com/DavidBeniaguev/status/1131890349578829825
Arnon Weinberg
<computational-modeling><neural-network>
How is the biological accuracy of ANNs typically measured?
Arnon Weinberg
# How is the biological accuracy of ANNs typically measured?

(Asked by: username_0 on 2014-12-26)

I am referring to the [computational neuroscience](http://en.wikipedia.org/wiki/Computational_neuroscience) side of neural network research that focuses on biological accuracy. I've read references to improving biological realism (using say [spiking neurons](http://en.wikipedia.org/wiki/Biological_neuron_model) or [binding neurons](http://en.wikipedia.org/wiki/Binding_neuron) of various types) by emulating known properties of real neurons (action potentials and what not). However, I haven't been able to find any detail on how the effectiveness of such attempts is measured empirically.

For example, [cultured neuronal networks](http://en.wikipedia.org/wiki/Cultured_neuronal_network) (such as a [neurochip](http://en.wikipedia.org/wiki/Neurochip)) could be used as a benchmark: Create an ANN and a BNN with the same number and arrangement of neurons, give them the same inputs, and compare the results... Has anything like that been attempted? How is the biological accuracy of ANNs typically measured?

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

> **Comments:**
> **username_1**: I like this question a lot. There seems to be a deeper underlying question of how is the accuracy (or the usefulness) of any mathematical (or computational) model to be measured? For that question, it might be important to notice that there are different [types of models](https://egtheory.wordpress.com/2013/09/08/mathematical-models/) with [different goals](https://egtheory.wordpress.com/2013/12/19/computational-models/), so a single answer might not exist; that might still be true even if restricted to ANNs.  
> **username_2**: Take a look at Ted Berger's work on hippocampal prosthesis. It essentially involves recording signals from a patch of the brain and then processing them for use by another part of the brain. https://www.newscientist.com/article/dn3488-worlds-first-brain-prosthesis-revealed/  
> **username_0**: Yes, thanks @username_2, that's an old article, I had read an update to it a while ago (http://www.popsci.com/technology/article/2011-06/artificial-memory-chip-rats-can-remember-and-forget-touch-button), and you can read more here: https://en.wikipedia.org/wiki/Hippocampal_prosthesis.  However, I haven't found any information about what type of ANN is used - do you have any idea?  It seems to be something very specialized.  
> **username_2**: In the original paper (http://iopscience.iop.org/1741-2552/8/4/046017/pdf/1741-2552_8_4_046017.pdf), it describes the model as "A general, Volterra kernel-based strategy for modeling multi-input/multi-output (MIMO) nonlinear dynamics underlying spike train-to-spike train transformations between CA3 and CA1 was established to predict output patterns of CA1 firing pattern from input patterns of CA3 neural activity".  
> **username_2**: Then in this reference (http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5424199&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5424199) it goes into all the technical details of how they built the MIMO.  
> **username_0**: Thanks a lot @username_2, I'll check it out!  It looks like it isn't a learning/dynamic network, but a static system that emulates spike patterns, so not technically an ANN.  
> **username_0**: An interesting experiment using an ANN to model a single biological neuron: https://twitter.com/DavidBeniaguev/status/1131890349578829825  

---

## 2 answers

### Answer by username_3 on 2014-12-27. Score: 7

One way the biological plausibility of an artificial neural network could be assessed is to look at how much a neural network abstracts away from the behavior of real neurons.

For instance, it is common in psychology and machine learning to use a sigmoidal [activation function](http://en.wikipedia.org/wiki/Activation_function) to determine the output of a node. If biological plausibility is a concern, one might prefer to use the [Hodgkin-Huxley model](http://en.wikipedia.org/wiki/Hodgkin%E2%80%93Huxley_model). (See also: [other neuronal models](http://en.wikipedia.org/wiki/Biological_neuron_model)).

To many practitioners, biological plausibility is not a primary concern. In most ANNs, each node is not really believed to be implemented by a single neuron in the brain, and thus using a more realistic activation function does not necessarily make the network more biologically realistic.

Personally, I don't think looking at the behavioral accuracy of a biological neural network (compared to an artificial neural network) says anything about the network's biological plausibility. Using more abstractions might produce more similar behavior due to less overfitting. Another concern is that the ANN could produce the same behavior as a BNN even if the actual computations involved are quite different.


---

### Answer by username_4 on 2014-12-26. Score: 6

Due to my newness to the field, I can only talk about comparisons of biological plausibility when discussing the [Neural Engineering Framework](http://compneuro.uwaterloo.ca/research/nef.html) (NEF) and functional modeling. What is missing from this answer is a purely bottom-up modelling perspective in the same vein as the [Blue-Brain project](http://bluebrain.epfl.ch/), but I'll leave that to another user.

One of the claims driving the push towards cognitive modelers using the NEF is the fact that it is more biologically plausible than back-prop ANNs. What is typically discussed is the neural mechanism for encoding, decoding and learning of the NEF has more scientific evidence claiming their plausibility than [back-prop ANNs](https://psychology.stackexchange.com/q/16269/4397).

From a more systematic biologically plausible perspective, the NEF is also used to build neural networks that use Semantic Pointer Architecture, which is also claimed to be more biologically plausible for a number of reasons. First of all, the number of neurons required for most models in significantly less than other proposed models ([see Eric Crawford's work on knowledge representation](https://mindmodeling.org/cogsci2013/papers/0099/paper0099.pdf)). Secondly, modifications to the network mimicking the effects of neurological disorders can be shown to have the same effect of those disorders ([see Dan Rasmussen's work on general intelligence](http://www.sciencedirect.com/science/article/pii/S0160289613001542)).

In terms of the bottom up perspective, the NEF could help here as well. The NEF is neuron-model agnostic, so it can be used with any of the neuron models you mentioned in the question. Hypothetically, it could be possible to create a similar network to one grown on a Neurochip and compare the various inputs and outputs as you suggested, but I've never heard of such a study being done.


---

