This is not speculation. This is a preview of the foundational, peer-reviewed science that makes Ankylotron possible. This page is for the engineers, the scientists, and the investors who want to see the work.
Traditional neural interfaces are monolithic, centralized, and lack spatial flexibility.
The platform leverages a distributed network of autonomous, sub-millimeter, wireless micro-implants—"neurograins"—that act as a wireless sensor network for the nervous system.
Our sophisticated, dual-protocol communication architecture demonstrates an application-aware engineering philosophy, recognizing that the requirements for "writing" signals (stimulation) and "reading" signals (recording) are fundamentally different.
For "writing." A synchronous, top-down protocol, ideal for precise, patterned stimulation and command-and-control.
For "reading." An event-driven, asynchronous protocol based on Code-Division Multiple Access (CDMA), elegantly optimized for large-scale recording of sparse neural spikes with high temporal fidelity and low power cost.
This deluge of data is useless without a decoder. Our data science pipeline validates the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs). These deep learning models can decode complex neural population activity in real-time, achieving a mean validation correlation of 0.72—significantly outperforming traditional linear filters.
Any long-term implant faces one great challenge: the Foreign Body Response (FBR). The body's immune system is evolved to attack any foreign object, forming a thick, dense, scar-like fibrous capsule that isolates the implant. This encapsulation leads to chronic inflammation, blocks sensor signals, and causes device failure. Traditional 'bio-inert' materials merely delay this; they don't solve it.
Our solution is a comprehensive, multi-faceted strategy. We do not just block the FBR; we actively manage the host-material interface.