# Optoelectronic Intelligence

arXiv: https://arxiv.org/abs/2010.08690 · HTML: https://mlsanity.com/paper/2010.08690/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [An active dendritic tree can mitigate fan-in limitations in superconducting neurons](https://mlsanity.com/paper/2107.05777/) (2021)
- [Demonstration of Superconducting Optoelectronic Single-Photon Synapses](https://mlsanity.com/paper/2204.09665/) (2022)
- [Prospective Messaging: Learning in Networks with Communication Delays](https://mlsanity.com/paper/2407.05494/) (2024)
- [Analog-Friendly Predictive Coding without Activation Derivatives](https://mlsanity.com/paper/2609.32350/) (2026)

## Abstract (arXiv)

To design and construct hardware for general intelligence, we must consider principles of both neuroscience and very-large-scale integration. For large neural systems capable of general intelligence, the attributes of photonics for communication and electronics for computation are complementary and interdependent. Using light for communication enables high fan-out as well as low-latency signaling across large systems with no traffic-dependent bottlenecks. For computation, the inherent nonlinearities, high speed, and low power consumption of Josephson circuits are conducive to complex neural functions. Operation at 4,K enables the use of single-photon detectors and silicon light sources, two features that lead to efficiency and economical scalability. Here I sketch a concept for optoelectronic hardware, beginning with synaptic circuits, continuing through wafer-scale integration, and extending to systems interconnected with fiber-optic white matter, potentially at the scale of the human brain and beyond.