There have been major advances in culturing neurons (and associated brain cells), and integrating them within electronic circuits. There’s an excellent review on Frontiers in Science. There are two possible aims for this work: understanding neural circuitry better (with many clinical applications), and integrating neural circuitry into artificial intelligence systems (because real brains are much better at certain types of everyday tasks).
Back in the ealy 2000’s I worked (with Nhamo Mtetwa) in this area on an EPSRC project including Glasgow University (Prof Adam Curtis, Prof Chris Wilkinson, Brad Dworak) and Edinburgh University (Prof Alan Murray, Dr Mike Hunter, Dr Nikki Macleod). At that time Stirling was involved in data analysis, intended for data from multi-electrode array (MEA) based cultures of early rat neurons, . But culturing them proved almost impossible for us, and at Stirling we worked on data from the Potter/Wagenaar lab at Georgia Tech. I gave a talk about the area in Georgia Tech in April 2003. But a great deal has changed since then: we were too early (or perhaps just not inventive enough!) to the game.
Possibly the biggest change is in the source of the neural culture. The use of induced pluripotent stem cells (iPSC) from human skin samples, and the building of 3-D brain organoids (small cultures of neural and associated cells) means that one need not be sacrificing newborn rats, and secondly that the cultures are (at least in a sense) made from human-like neural cells. This, and improvements in the size and flexibility of electrodes (and faster processing of their signals) means that such cultures can much more reliably be built and instrumented. But how should this work be continued? There is a long discussion in the Frontiers paper, itself part of a larger discussion on Frontiers, including a very good discussion of ethics issues. Should we be seeking better understanding of the brain, improving our ability to deal with clinical issues (mental health and physical damage to neural tissue), and/or incorporating these organoids into AI systems?
It is clear that better understanding of brains (and more generally nerve tissue) has clinical applications. It may raise philosophical issues as well: once we understand the connection between consciousness, awareness, cognition and neural tissue, we may need to re-jig our ideas of what makes an addictive personality, or of makes people criminal, quite apart from the possibility of re-creating these in synthesised neural cultures. But that is (probably) a little further down the road.
Incorporating organoids into AI systems has attractions. While real brains run at much lower speeds, they are highly parallel and very energy efficient, more than compensating for this. However, neural tissue needs to be kept at a constant temperature and perfused with nutrients and water. While microfluidics have advanced a great deal, I reckon that these disadvantages may make putting such systems into consumer equipment unattractive. In addition, such systems have a limited lifespan compared to the (essentially infinite) lifespan of semiconductor based electronics. Further, there are still issues related to the longevity of the electronics/tissue connection. On the other hand, in the 1980’s I never expected to see hand-held devices with 64 bit processors and many gigabytes of memory, so one can never be sure!
Lastly, I want to return to the attempts we made more than 20 years ago. It seems to me that I have been trying to make scientific/technological advancements too early. Without iPSCs, without good microfluidics, creating and keeping neural cultures alive was very difficult, making instrumenting them just too difficult (at least for us). And before that, I was working on a binaural hearing aid that attempted to find the sound sources, and allow the user to select the one of interest using an iPad-like interface – but in the year 2000. Too early. Right now many researchers are refining Transformer-like AI systems, jumping on a fast-rolling bandwagon. Too late. Getting the timing just right is the really difficult trick!
Lestheprof's Blog 