Brain from the Bottom Up: Spontaneous Birth of Synchrony in Small Neuronal Networks

More 13 July 2011: Brian Wang looks at the same research, with an emphasis on the hardware (electronic) aspect. It is fitting to look at both the neurons and the electronics, since the coming cybernetic biosingularity will be dependent upon both.

Human intelligence and consciousness are poorly understood, even by cognitive scientists, neuroscientists, and consciousness specialists. No one understands how to build a human intelligence from scratch, much less how to build a non-human intelligence capable of interacting with humans and the outside world on its own terms. But researchers at Tel Aviv University from the departments of Electrical Engineering and Physics, have taken a fascinating approach to building the basic components of brains: networks of biological neurons. Something wonderful happened when enough cultured neurons linked together in network: They spontaneously "synched up."

Background


Information processing in neuronal networks relies on the network's ability to generate temporal patterns of action potentials. Although the nature of neuronal network activity has been intensively investigated in the past several decades at the individual neuron level, the underlying principles of the collective network activity, such as the synchronization and coordination between neurons, are largely unknown. Here we focus on isolated neuronal clusters in culture and address the following simple, yet fundamental questions: What is the minimal number of cells needed to exhibit collective dynamics? What are the internal temporal characteristics of such dynamics and how do the temporal features of network activity alternate upon crossover from minimal networks to large networks?


Methodology/Principal Findings


We used network engineering techniques to induce self-organization of cultured networks into neuronal clusters of different sizes. We found that small clusters made of as few as 40 cells already exhibit spontaneous collective events characterized by innate synchronous network oscillations in the range of 25 to 100 Hz. The oscillation frequency of each network appeared to be independent of cluster size. The duration and rate of the network events scale with cluster size but converge to that of large uniform networks. Finally, the investigation of two coupled clusters revealed clear activity propagation with master/slave asymmetry.

Conclusions/Significance


The nature of the activity patterns observed in small networks, namely the consistent emergence of similar activity across networks of different size and morphology, suggests that neuronal clusters self-regulate their activity to sustain network bursts with internal oscillatory features. We therefore suggest that clusters of as few as tens of cells can serve as a minimal but sufficient functional network, capable of sustaining oscillatory activity. Interestingly, the frequencies of these oscillations are similar those observed in vivo. _PLoS

More papers by Mark Shein Idelson

Brain synchrony is an important topic of study, linked to consciousness, memory, learning, and normal function of general human brain activity. But synchronous oscillations are also programmed into the neurons themselves, at the smallest level of neuronal organisation. The challenge now, is to build "networks of networks", to discover the communications strategies which interconnected networks will evolve.

Contrast such a biological, bottom up approach with complex machine models of brain function such as the SpiNNaker project out of the University of Manchester, or the Human Brain Project (HBP) led by Henry Markram at Ecole Polytechnique de Lausanne.

Both of the above brain modeling approaches using computers, are based upon bottom-up theories of how brains work. The Lausanne project (HBP) is far more detailed -- going down to the ion channel level of neurons. The Manchester approach is impressive in its parallel computing ambitions, but it begins at the individual "neuronal spiking" level. SpiNNaker is more of a hybrid CompSci:Neurosci approach, than an actual model of the brain like the HBP.

Conventional artificial intelligence approaches do not mimic brain function closely, and are generally more "top-down" approaches, utilising conventional algorithmic concepts of mainstream computer science. Such approaches are doomed to failure before they even begin, as the last 70 years of conventional AI attempts continue to demonstrate.

In reality, brains must be grown. And new types of brains have to be evolved. Not necessarily from biological materials, but up until now the only working brains we know are biological. The first successful autonomous brains are likely to be evolved either from biological materials, or using ingenious abstractions of processes which emerge from biological mechanisms.

Al Fin cognitive scientists suggest that both the Lausanne approach and the Manchester approach are abstracted at the wrong level, if they wish to provide rapid paths to evolved intelligences. Creative human beings will have to discover the appropriate balance, but they will certainly be aided by computing systems in doing so. This is not gobbledygook nor is it AI-psychobabble. It is the genuine crux and pivot point of the problem.

What are the implications for the singularity? There will be no "uploading of consciousness" for the foreseeable future. The cyborg biosingularity is still on schedule for the decade between 2020 and 2030, if humans can avoid an extended Obama Dark Ages. The main question is how many of the cyborg components will be biological in origin, and how many will be non-biological (probably utilising nanotechnology).