# A Computational Perspective on NeuroAI and Synthetic Biological Intelligence

arXiv: https://arxiv.org/abs/2509.23896 · HTML: https://mlsanity.com/paper/2509.23896/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [A discrete generative model of neuronal spiking activity on microelectrode arrays](https://mlsanity.com/paper/2609.23907/) (2026)

## Abstract (arXiv)

NeuroAI is an emerging field at the intersection of neuroscience and artificial intelligence, where insights from brain function guide the design of intelligent systems. A central area within this field is synthetic biological intelligence (SBI), which combines the adaptive learning properties of biological neural networks with engineered hardware and software. SBI systems provide a platform for modeling neural computation, developing biohybrid architectures, and enabling new forms of embodied intelligence. In this review, we organize the NeuroAI landscape into three interacting domains: hardware, software, and wetware. We outline computational frameworks that integrate biological and non-biological systems and highlight recent advances in organoid intelligence, neuromorphic computing, and neuro-symbolic learning. These developments collectively point toward a new class of systems that compute through interactions between living neural tissue and digital algorithms.