The possibility of creating bio-mechanical brains is increasingly a reality. The experiment

Many are, and rightly so, impressed by what increasingly sophisticated artificial intelligences are achieving.

But what if living neural networks and artificial neural networks were directly integrated with each other?

Let’s start with a consideration: neurons are specialized cells, able to respond not only to electrical stimuli, but also to light, pressure, chemicals and magnetic fields; for this reason, as occurs in living organisms, they can be used to collect stimuli from the external environment and translate them into electrical impulses.

Furthermore, the ability of researchers to keep complex biological networks of neurons alive for times long enough to be able to exercise certain functions inside artificial bodies has already been demonstrated: living brain cells have already been used to make certain robots process information and navigate their environment on the basis of external stimuli processed by neurons.

Going one step further, a research group from the University of Illinois Urbana-Champaign presented fascinating work at the American Physical Society meeting held this month in Las Vegas.

The researchers cultured about 80,000 neurons derived from reprogrammed mouse stem cells on plates. For comparison, consider that the brain of an adult fruit fly contains about 200,000 neurons, and a human brain over 86 billion.

The plate cultivation of the neurons made it possible to obtain a two-dimensional biological network, which was positioned under an optical fiber and on an electrode grid; in this way, it was possible to stimulate the network itself with mixed sequences of light pulses and electrical signalsthen recording the electrical signals produced by the neuronal network in response thanks to the electrode grid.

All the apparatus was placed in a box the size of a palm, as can be seen in the figure accompanying this text, which in turn was placed in an incubator to keep the cells alive. The electrical signals produced by the neural network were sent to a normal computer chip and fed into a neural network, which was used to recognize specific electrical patterns produced by the biological network.

At this point, the entire apparatus was ready to answer a first, fundamental question: once a specific stimulus using light and electricity was sent to the network of biological neurons, the electrical response would be specificthat is, would the same response always be obtained by presenting the same stimulus?

The researchers then created 10 distinct sequences of electrical pulses and flashes of light, playing each one many times for a total of one hour.

After this one-hour “training”, the first important result was found: the neurons produced the same signals every time the same pattern was presented.

The chip, which managed the artificial neural network, just had to learn to distinguish those signals, classifying them as 10 different types – that is, grouping them according to their similarity. Artificial neural networks can often take a long time and many iterations to train, but the division of labor between biological neurons, capable of generating a precise electrical stimulus in response to the environmental conditions they “saw”, and artificial neurons has allowed researchers to greatly reduce the time and energy required for training.

Not only that: at the end of the training hour, the researchers let the neurons rest; then, they re-exposed them to each of 10 sequences of light and electricity.

Well, the biological network retained the memory, reproducing the same electrical patterns produced during training and thus feeding the artificial neural network used for their original recognition. In particular, to evaluate how well the device worked, they calculated a score commonly used to evaluate the recall and classification abilities of artificial intelligences, which ranges from 0 (worst possible case) to 1 (most efficient), obtaining for the device hybrid they designed 0.98.

At the moment, the device cannot compete with conventional neural networks, but here I would like to make a number of points.

First of all, the device will make it possible to evaluate whether the sensory memory of biological brains is really consistent in an architectural change of the synaptic connections between the different neurons, studying this parameter in response to stimulation and in connection with the increase in the discriminatory capacities of external stimuli and the permanence of this ability over time.

Secondly, the system is scalable: many plates can also be biologically connected to each other, letting the neurons of one culture contact those of another underlying one, resulting in complex three-dimensional architectures, comparable to those of cerebral organoids and the brains themselves. This will most likely allow to achieve performances that are still difficult to evaluate today.

Still, we have here the further demonstration of a path that is leading towards the creation of bio-mechanical brains which, remotely and in a potentially unlimited way, can control machines of all kinds, receiving stimuli from the environment in which these machines will operate and commanding the response to them.

In addition, the great savings in terms of data, time and energy needed to train networks of neurons compared to current artificial intelligences, also demonstrated in these first, simple experiments, opens up the prospect of a leap forward in the capabilities that we could call artificial systems “cognitive”, if not necessarily in the same computational capacities, based on heuristics different from those used up to now.

Finally, and this seems to me the most important element of all, we could be closer to experimentally addressing the problem of the organic/physical source of the mind, with all the ethical, philosophical and scientific consequences of the case.

The search goes very fast, but the social awareness of what is being done and its possible applications, for better or for worse, I believe is still too little widespread; this, it seems to me, is an example of the gap between science and participation in its results which, just yesterday, I was discussing on this page.