  Driven by machine-learning tasks neural networks have demonstrated useful
capabilities as nonlinear hypothesis classifiers. The underlying technologies
performing the dot product multiplication, the summation, and the nonlinear
thresholding on the input data in electronics, however, are limited by the same
capacitive challenges known from electronic integrated circuits. The optical
domain, in contrast, provides low delay interconnectivity suitable for such
node distributed non Von Neumann architectures relying on dense node to node
communication. Thus, once the neural network's weights are set, the delay of
the network is just given by the time of flight of the photon, which is in the
picosecond range for photonic integrated circuits. However, the functionality
of memory for storing the trained weights does not exists in optics, thus
demanding a fresh look to explore synergies between photonics and electronics
in neural networks. Here we provide a roadmap to pave the way for emerging
hybridized photonic electronic neural networks by taking a detailed look into a
single node's perceptron, discussing how it can be realized in hybrid photonic
electronic heterogeneous technologies. We show that a set of materials exist
that exploit synergies with respect to a number of constrains including
electronic contacts, memory functionality, electrooptic modulation, optical
nonlinearity, and device packaging. We find that the material ITO, in
particular, could provide a viable path for both the perceptron weights and the
nonlinear activation function, while simultaneously being a foundry process
near material. We finally identify a number of challenges that, if solved,
could accelerate the adoption of such heterogeneous integration strategies of
emerging memory materials into integrated photonics platforms for real time
responsive neural networks.
