<p>Neurons can be simulated using different models that vary in the degree of biophysical realism. When designing an artificial neuronal network, I am interested in the consequences of choosing a degree of neuronal realism.</p>

<p>In terms of computational performance, the FLOPS vary from integrate-and-fire to the Hodgkin–Huxley model (Izhikevich, 2004). However, properties, such as refraction, also vary with the choice of neuron.</p>

<ol>
<li><p>When selecting a neuronal model, what are consequences for the ANN other than performance? For example, would there be trade-offs in
terms of stability/plasticity?</p></li>
<li><p>Izhikevich investigated the performance question in 2004. What are
the current benchmarks (other measures, new models)?</p></li>
<li><p>How does selecting a neuron have consequences for scalability in terms of hardware for a deep learning network?</p></li>
<li><p>When is the McCulloch-Pitts neuron inappropriate?</p></li>
</ol>

<hr>

<p><strong>References</strong></p>

<p>Izhikevich, E. M. (2004). <em>Which model to use for cortical spiking neurons?</em> IEEE Transactions on Neural Networks, 15(5). <a href="https://www.izhikevich.org/publications/whichmod.pdf" rel="nofollow noreferrer">https://www.izhikevich.org/publications/whichmod.pdf</a> </p>

CC BY-SA 4.0
2018-09-13T15:51:47.567Z
@DuttaA I am new to the field, but from my brief literature review this seems to be a possibility: http://www.scholarpedia.org/article/Deep_Learning#Deep_Learning_with_Spiking_Neurons.3F Perhaps I should have asked more explicitly about the current state of the art of using spiking neurons for ANNs.
noumenal
<deep-learning><artificial-neuron><neurons><biology>
How does the degree of neuronal realism affect computing in a deep learning scenario?
noumenal
# How does the degree of neuronal realism affect computing in a deep learning scenario?

(Asked by: username_0 on 2018-09-13)

Neurons can be simulated using different models that vary in the degree of biophysical realism. When designing an artificial neuronal network, I am interested in the consequences of choosing a degree of neuronal realism.

In terms of computational performance, the FLOPS vary from integrate-and-fire to the Hodgkin–Huxley model (Izhikevich, 2004). However, properties, such as refraction, also vary with the choice of neuron.

1. When selecting a neuronal model, what are consequences for the ANN other than performance? For example, would there be trade-offs in
   terms of stability/plasticity?
2. Izhikevich investigated the performance question in 2004. What are
   the current benchmarks (other measures, new models)?
3. How does selecting a neuron have consequences for scalability in terms of hardware for a deep learning network?
4. When is the McCulloch-Pitts neuron inappropriate?

---

**References**

Izhikevich, E. M. (2004). *Which model to use for cortical spiking neurons?* IEEE Transactions on Neural Networks, 15(5). <https://www.izhikevich.org/publications/whichmod.pdf>

**Tags:** deep-learning, artificial-neuron, neurons, biology

> **Comments:**
> **username_0**: @DuttaA I am new to the field, but from my brief literature review this seems to be a possibility: http://www.scholarpedia.org/article/Deep_Learning#Deep_Learning_with_Spiking_Neurons.3F Perhaps I should have asked more explicitly about the current state of the art of using spiking neurons for ANNs.  

---

## 0 answers

