Body: <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>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-09-13T15:51:47.567Z
Comments.Text: @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.
Comments.UserDisplayName: noumenal
Tags: <deep-learning><artificial-neuron><neurons><biology>
Title: How does the degree of neuronal realism affect computing in a deep learning scenario?
UserDisplayName: noumenal
ThreadText: # 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
  
  