Changing Connectomes, MIT press

- connectomics is about graphs, different gradations of graphs, different mathematical properties, how graphs created during development
- can be graphs of neurons, voxels, regions

Characteristics of a graph: unit level

- node/edge betweenness - how frequently node/edge is a part of a shortest path

Characteristics of a graph: aggregate level

- connectedness =
`2 * E / (N * (N - 1))`

how many edges are there out of possible edges - average shortest path
- global efficiency = average of inverse of length of shortest path, if no edge and distance is infinite then it contributes 0
- average neihbourhood connectivity
- frequency of triples of different configurations. some configurations are more common that others in regions of bran and animals

thoughts: how about to try to use connectomics theory to understand CPU architecture? will it works well? what we are missing?

- clusters - connected a lot within

Major networks types

- random, random edges, not realistic
- scale-free..
- regular, high connectivity between neighbors
- small-world, high connectivity between neighbors, sometimes long connections
- modular, some connections within group and some between groups
- hierarchical, multi-level modular

- each neuron/node in graph have constant spacial position, which dictates which nodes it can connect to and how much it would cost, as well as physical properties of pressure, etc.
- physical Eluclidian distance between nodes is inversely proportional to probability of connectedness
- most of axons are straight lines

- different animals have different graphs of neural networks

fly

Graphs of brains available

- fly, drosophilia, full
- pigeon, some partial but good. more anatomically distributed than numans. two levels just as humans. has central core.
- macaque, rhesus monkey

pigeon

human

- patterns formation is complex on how they developed (like CNN layers)
- Alan Turing was working on CNN like patterns in 1952, guiding molecules called morphogenes lead to formation of patterns
- apoptosis - deletion of neurons and connections, occurs by programmed cell death or external factors (neuromorphic death)
- if non apoptosis happens brain can not develop, there are studies on mice
- layer formation — the more layer the more neurons and input. mice to human increased surface size 1000x but thickness only 2x

Plasticity

**structural**— how connections change. don’t change much after development.**functional**- connections get stronger or weaker. change a lot.

how connected? axons can be very long. how they are guided? many theories. establish first major pathways, then new paths follow them. use chemical signals to guide axons.

- patches of connected neurons on surface

Myelination

**10cm**is**100ms**for normal fiber and**10ms**for myelinated factor- degree influence synchronisation
- wide range of delays depending on brain system, hierarchy of delays and speeds

BCI

- stimulation works and can lead to lasting changes

Stimulation targets

- Kuramoto oscilators
- Proportional-Differentional Feedback control technique
- control theory

Software

- Lead-DBS - model of deep brain stimulation as population level
- VERTEX - model of electrical stimulation at neuronal level