How the Brain Stores Information for Short Periods of Time

Freiburg biologist Dr. Aristides Arrenberg and his American colleagues studied mechanisms used by the brain to store information for a short period of time. The cells of several neural circuits store information by maintaining a persistent level of activity: A short-lived stimulus triggers the activity of neurons, and this activity is then maintained for several seconds. The mechanisms of this information storage have not yet been sufficiently described, although this phenomenon occurs in very many areas of the brain.The authors of the study, now published in the journal Nature Neuroscience, investigated the persistent activity in a hindbrain circuit responsible for eye movements in zebrafish larvae. This circuit, the so-called oculomotor system, gives the command for rapid eye movement by way of special nerve cells that produce a short-lived succession of action potentials. On the one hand, this "burst of fire" reaches the neurons responsible for movement in the eyes and triggers a "saccade," a rapid movement of the eye. On the other hand, it is also transmitted to a second cell population, the so-called neural integrator for eye movements, where the speed signal is integrated mathematically and a position signal is created. This signal is then transmitted to the motor neurons, thus producing -- in fish as well as in humans -- a stable eye position following the rapid eye movement. The neural integrator keeps up this signal for several seconds, until a new saccade is initiated.

The persistent activity in the neural integrator for eye positions is never perfect, as the eyes gradually drift back to their point of rest after a saccade. The authors thus had the possibility of measuring the dynamics of the system during spontaneous eye movements in the dark and testing the model without the measurements being distorted by saccade commands or visual feedback.

The authors discovered that, contrary to previous belief, the cells of the neural integrator for eye movements do not constitute a homogeneous population and that existing models for explaining persistent activity in the oculomotor system will have to be reconsidered. The scientists demonstrated that the integrator neurons do not posses a uniform dynamics and that the neurons are distributed in the hindbrain with the help of their integrator time constants.

These findings provide new evidence on the organization and functioning of circuits with persistent activity and suggest a potential explanation for their low susceptibility to failure. The study is an important milestone in the quest of network neuroscience to explain the functioning of local circuits and thus close the gap between the functioning of a single neuron and the production of behavior.

The First Real Brain-Like Computer Could be Made of the Same Material That Makes DVDs

Scientists in the UK and in California may have found a holy grail of brain-like computing--a material that can both simulate the behavior of neurons and run on very low power--in an abundant and familiar medium. The very same phase-changing material that allows us to record on DVDs could be used to build a low-power brain-like processor capable of learning and adapting without the need for extensive pre-programming.

The material is GST--so named for the materials it contains (germanium, antimony, and tellurium--and it possesses just the kind of phase-changing properties that researchers are looking for. Phase-change alloys like GST can exist in various phases, ranging from crystalline, ordered structures to chaotic, amorphous structures. Their states depends on various external factors, like heat or charge.

In DVDs, GST allows discs to be embedded with binary ones and zeros that can later be read by a laser. That’s fine for film screening purposes, but GST can actually exist in various degrees of phase change between completely crystalline and completely amorphous, and thus it can store information across a wider range of values--just like a neuron, which fires only when a build-up of incoming signals reaches a certain threshold.

The GST neurons/synapses developed by the University of Exeter and Stanford team also can adjust the strength of the synapses between them--a key characteristic of inter-neuron communication--because of its inherent ability to modify its electrical resistance. This allows the GST neurons to adjust the strength of the connections between them to signify the importance of incoming signals and prioritize signals flowing through a neural network.

All said, those two qualities--the ability to store information across a range of phase states, and a low-power, adjustable-strength synapse--make for a pretty nice electronic analog for a working brain. But the work is very preliminary, and it’s one thing to have a few working synthetic neurons in the lab, and quite another to have a working network of thousands or millions (much less hundreds of millions).

In other words, it’s pretty amazing that DVD tech can do these things, but a proper brain-like computer is still many years and several breakthroughs distant