<commit_before>---
layout: post
date:  2016-11-03 10:00:00
title: The search for biologically plausible neural computation: The conventional approach
summary: Introduces biological neurons and how they can compute simple functions. 
author: Dmitri "Mitya" Chklovskii
visible: True
---

Inventors of the original artificial neural networks (NNs) derived their inspiration from biology. 
However, as artificial NNs progressed, their design was less guided by neuroscience facts. 
Meanwhile, progress in neuroscience has altered our conceptual understanding of neurons. 
Consequently, we believe that many successful artificial NNs resemble natural NNs only
superficially violating fundamental constraints imposed by biological hardware. 

The wide gap between the artificial and natural NN designs raises intriguing questions: What 
algorithms underlie natural NNs? Can insights from biology help build better artificial 
NNs? 

This is the first of a series of posts aimed at explaining recent progress made
by my collaborators and myself towards biologically plausible NNs. Such networks can 
serve both as models of natural NNs and as general 
purpose artificial NNs. We have found that respecting biological constraints 
actually helps development of artificial NNs by guiding design decisions.

In this post, I cover the background material, going back several decades. 
I sketch a biological neuron, introduce primary biological constraints, and discuss 
the conventional approach to deriving artificial NNs. I will show that while <commit_msg>Update 2016-11-03-MityaNN1.md<commit_after>---
layout: post
date:  2016-11-03 10:00:00
title: The search for biologically plausible neural computation: The conventional approach
author: Dmitri "Mitya" Chklovskii
visible: True
---

Inventors of the original artificial neural networks (NNs) derived their inspiration from biology. 
However, as artificial NNs progressed, their design was less guided by neuroscience facts. 
Meanwhile, progress in neuroscience has altered our conceptual understanding of neurons. 
Consequently, we believe that many successful artificial NNs resemble natural NNs only
superficially violating fundamental constraints imposed by biological hardware. 

The wide gap between the artificial and natural NN designs raises intriguing questions: What 
algorithms underlie natural NNs? Can insights from biology help build better artificial 
NNs? 

This is the first of a series of posts aimed at explaining recent progress made
by my collaborators and myself towards biologically plausible NNs. Such networks can 
serve both as models of natural NNs and as general 
purpose artificial NNs. We have found that respecting biological constraints 
actually helps development of artificial NNs by guiding design decisions.

In this post, I cover the background material, going back several decades. 
I sketch a biological neuron, introduce primary biological constraints, and discuss 
the conventional approach to deriving artificial NNs. I will show that while 
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