EAISI AI Symposium

Regina Luttge and Bert de Vries

Computation in biological brain tissue consumes several orders of magnitude less power than silicon-based systems.

Motivated by this fact, this project aims to develop the world’s first hybrid neuro-in-silico Artificial Intelligence (AI) computer, introducing a fundamentally new paradigm of AI computing.

In this high-risk high-gain project, we will combine an in-silico Bayesian control agent (BCA) with neural tissue hosted by a microfluidic Brain-on-Chip (BoC) that together form a hybrid learning system capable of solving real-world AI problems.

Toward this paradigm, all computation and communication inside and between the BCA and BoC will be governed by the Free Energy Principle, which is both the leading neuroscientific theory for describing biological neuronal processes and supports a variational Bayesian machine learning interpretation.

We will start by developing a pure silicon-based BCA that learns to balance an inverted pendulum, implemented by free energy minimization on a factor graph.

Next, we will replace successively larger parts of the factor graph with biological neural circuits of a microfluidic multi-compartment BoC device. The biological network will be trained by electrical stimulation orchestrated by the synthetic Bayesian agent.

For the communication between these two units, we will design and realize a novel communication protocol making use of existing software being applied in readout and event sorting for Calcium imaging and multi-electrode array data, such as MEAViewer, CALIMA, NetCal and SpikeHunter.

By upscaling the number of replaced sub-circuits, we aim to provide a proof-of-concept and to lay the basis for ultra-low power hybrid brain- on-chip AI computing.

Bouke van Balen

Neurotechnologies and Artificial Intelligence (AI) are becoming more intertwined, and this raises unique ethical issues.

In this talk, I will highlight some ethical issues that surface when Brain Computer Interfaces (BCIs) are used as assistive communication technologies for people with Locked-In Syndrome (LIS). Brain Computer Interfaces are devices that can be controlled with brain activity. This can be an outcome for people with LIS, who are fully paralyzed yet cognitively intact, and suffer from the inability to express themselves.

With BCI-technology, people with LIS can control devices that allow them to communicate without the usual need for muscle control. Promising research is being done into a specific type of implanted BCI that directly translates neural activity associated with attempted speech into words on a screen or word pronunciation by a synthesized voice.

With this BCI, people with LIS could get a method of communication that approaches conventional speech. AI is an important part of this technology, as it is used to decode brain signals and predict attempted speech.

Whereas such use of AI leads to efficient and faster speech, it raises questions about the agency and ownership of BCI-utterances. It can for instance be ambiguous if users have sufficient control over their BCI-utterances, and if these utterances represent them.

As it is important to have control over if we say something, what we say, and how we say it, these ethical issues deserve attention.