  To understand the learning process in brains, biologically plausible
algorithms have been explored by modeling the detailed neuron properties and
dynamics. On the other hand, simplified multi-layer models of neural networks
have shown great success on computational tasks such as image classification
and speech recognition. However, the computational models that can achieve good
accuracy for these learning applications are very different from the
bio-plausible models. This paper studies whether a bio-plausible model of a in
vitro living neural network can be used to perform machine learning tasks and
achieve good inference accuracy. A novel two-layer bio-hardware hybrid neural
network is proposed. The biological layer faithfully models variations of
synapses, neurons, and network sparsity in in vitro living neural networks. The
hardware layer is a computational fully-connected layer that tunes parameters
to optimize for accuracy. Several techniques are proposed to improve the
inference accuracy of the proposed hybrid neural network. For instance, an
adaptive pre-processing technique helps the proposed neural network to achieve
good learning accuracy for different living neural network sparsity. The
proposed hybrid neural network with realistic neuron parameters and variations
achieves a 98.3% testing accuracy for the handwritten digit recognition task on
the full MNIST dataset.
