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Connectomics deep drive into challenge project

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Introduction to Connectomics

We have been asked to write a literature review on network reconstruction algorithms that can be applied  Predicting Connections from Calcium Channel Imaging for our programming Challenge project. As we go through the research I feel Like updating this on the blog will help a lot for the students who are interested in this research. As this is the 1st post I will cover the introduction to our Problem.
Connectomics is the production and study of connectomes (comprehensive maps of connections within an organism's nervous system, typically its brain or eye) . These structures are extremely complex. In Order to study Connectomics we should first know some hot topics such as Neural network and deep learning, Biological Neuron, Calcium Imaging Data analysis.

As It's introduction, We will walk you through all the aspects of Connectomics with simple explanations. First we go through  Neural Networks and Deep Learning

Neural networks : a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data

Deep learning : a powerful set of techniques for learning in neural networks.

Biological Neuron
A biological neuron model, also known as a spiking neuron model, is a mathematical description of the properties of certain cells in the nervous system that generate sharp electrical potentials across their cell membrane. Neuron receive input from other neurons at the synapses and if required conditions are satisfied, produce an output (a spike train) through the axon. 

Knowing the connections between neurons in the brain in key to understanding learning and memory. It's hard to trace them out physically even with powerful microscopes. 
Here we our introduce the problem and the solution for it.

Fluorescent calcium ions can be used to observe the firing of neurons (action potentials). It's observed that firing of neurons are exactly same as calcium ions. You can  view the simulation in the below video which shows the firing of Calcium ion. 


If we predict the connection by the firing of the Calcium ion we can  predict the connections between neurons using calcium fluorescence data. Our task is to  reconstruct the connectivity from the activity of neurons. 

Calcium Imaging Data Sets are available here. We have to come up with an algorithm to start testing with the “small” dataset involving a network of 100 neurons. Then we have to move on to the larger networks of 1000 neurons. E.g., normal-4-low noise.

 I will come up with a method to predict the connections and will say the implementation method in the next blog post. 









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