Brain simulation deals with creating a computer model of the brain. Such models can help in understanding diseases and reduce the need for animal experiments. The challenges in brain simulation are:
- Scale : The human brain contains about 86 billion neurons each with about 7000 connections. This pushes even the largest exascale computers to its limit. Exascale = quadrillion operations per second. Rat brains is the state of the art for now.
- Complexity : The exactly mimic a neuron and its molecular scale processes, each of these model neuron should have an unlimited set of parameters that need to be trained. Studies are ongoing to see what parts of these are important to achieve a better simulation and what parts can be left out.
- Speed : Learning and training in the brain occurs over years and the current technology limits us to run anything faster than real time. This puts a hard constraint on the depth to which we can train a model. This ability to model the speed and perhaps augment can open doors to better simulate the synapses.
- Integration : Our brain consists of different regions that handle different functions. To model this we need smaller models and combine them to achieve a brain wide function. This can lead to simulating aspects like consciousness and understanding.
Some interesting questions
- Would such simulations lead to generation of more human aspects like consciousness and imagination?
- Can such a model be used to augment the capabilities of the human brain?
- Can we transfer information between such models in a more intrinsic way?
The four biggest challenges in brain simulation