Connection and Question of Human Neurons with Retina

Question 1:- Identifying Synapses,
Reconstructing neurons involves tracing their branches, which are like the “wires” of the retina. This by itself is not enough for finding connectomes; we also need to identify synapses. This kind of image analysis will be accomplished through another game that will be introduced on this website in the near future. The identification of synapses will involve subtleties.
A neuron receives input from other neurons (typically many thousands). Inputs sum (approximately). Once input exceeds a critical level, the neuron discharges a spike - an electrical pulse that travels from the body, down the axon, to the next neuron(s) (or other receptors). This spiking event is also called depolarization, and is followed by a refractory period, during which the neuron is unable to fire.
The axon endings (Output Zone) almost touch the dendrites or cell body of the next neuron. Transmission of an electrical signal from one neuron to the next is effected by neurotransmittors, chemicals which are released from the first neuron and which bind to receptors in the second. This link is called a synapse. The extent to which the signal from one neuron is passed on to the next depends on many factors, e.g. the amount of neurotransmittor available, the number and arrangement of receptors, amount of neurotransmittor reabsorbed, etc.
The efficacy of a synapse can change as a result of experience, providing both memory and learning through long-term potentiation. One way this happens is through release of more neurotransmitter. Many other changes may also be involved.

Long-term Potentiation:

An enduring (>1 hour) increase in synaptic efficacy that results from high-frequency stimulation of an afferent (input) pathway 

Hebbs Postulate:

"When an axon of cell A... excites[s] cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells so that A's efficiency as one of the cells firing B is increased."

Bliss and Lomo discovered LTP in the hippocampus in 1973
Points to note about LTP:

• Synapses become more or less important over time (plasticity)
• LTP is based on experience
• LTP is based only on local information (Hebb's postulate).

Rules of Connection:- 

Playing either of the above games will produce information that will be valuable for understanding how the retina functions. How exactly will the information be used? To answer this question, we should confront the issue of variability. We expect that every retina will be wired somewhat differently. In that case, would mapping the connections in one retina tell us anything that is applicable to other retinas? We expect that retinal connectomes will obey invariant rules of connection, and it is these rules that really interest researchers. Many of the rules are expected to depend on neuronal cell types, i.e., of the form “Cell type A receives synapses from cell type B.” Some such rules are already known, but the vast majority remain undiscovered.
How can we extract such rules from a retinal connectome? A neuron’s cell type can be recognized from its distinctive shape, and hence from the 3D reconstructions that you will help create by playing the coloring game. The cataloguing of retinal cell types has not yet been completed, and your reconstructions will also contribute to this endeavor.

Relating Connections to Activity

Neurons of the retina respond to visual stimuli with electrical activity. Such neural signals eventually travel along the optic nerve from the eye to the brain. Therefore, if we want to understand the role played by the retina in vision, we must also measure the activity of retinal neurons. Furthermore, we should relate the connections of retinal neurons to their activity.
This can be done in two ways. First, before the researchers imaged retina neurons with serial electron microscopy, they used another method of imaging, two-photon microscopy, to measure the activity of the same neurons. Therefore it is possible to relate the activity of retinal neurons to their connectivity. Second, the cell type of a retinal neuron can be identified by its shape, and each cell type responds to visual stimulation in a distinctive manner. Therefore, we can relate the connections of retinal neurons in this dataset to the activity of neurons of the same cell types measured in other retinas.