Building an artificial neural network with neurons

Abstract

Artificial neural networks are based on mathematical models of biological networks, but it is not clear how similar these two networks are. We have recently demonstrated that we can mechanically manipulate single neurons and create functioning synapses. Here, we build on this discovery and investigate the feasibility and time scales to build an artificial neural network with biological neurons. To achieve this, we characterized the dynamics and forces when pulling functional axonal neurites using a micromanipulation technique with maximum speeds about 300 times faster than the average natural growth rate of 0.0017μm/s. We find that the maximum force required to initiate and extend the neurites is about 1nN. The dynamics of the mechanical extension of the neurite is well described by many elastic springs and viscous dashpots in series. Interestingly, we find that the transport networks, specifically the actin network, lags behind the mechanically pulled structure. These insights could potentially open a new avenue to facilitate and encourage neuronal regrowth not relying on chemical queues. The extracted mechanical parameters and timescales characterize the neurite growth. We predict that it should be possible to use a magnetic trap to wire an artificial network such as a multi-layer perceptron in 17 hours. Once wired, we believe the biological neural network could be trained to process a hand-written digit using artificial neural network concepts applied to biological systems. We show how one could test the stability and robustness of this network by axotomizing (i.e. cutting) specific axons and reconnecting them using mechanical manipulation.