summary: A new study reveals that the brain can switch between slow and fast integration of information, allowing it to adjust the schedules on which it operates. The study also provides insight into how the architecture of neural networks determines the speed at which information is integrated, which may have implications for future research on brain function and cognitive processes.

source: Max Planck Institute

Switching between slow and rapid integration of information, the brain can flexibly adjust the schedules on which it operates.

This is the result of a new study by an international team of researchers, now published in the journal Nature Communications.

Their analysis of experimental data from the visual cortex and computer simulations also provides an explanation for how different time scales arise and how they can be changed: the structure of neural networks determines how quickly or slowly information is integrated.

Different processes occur in the brain on different time scales: while sensory input can be processed within tens of milliseconds, decision-making or other complex cognitive processes may require the consolidation of information over several minutes. In contrast, some areas of the brain are faster than others.

These intrinsic time scales are not rigid and fixed. However, until now little was known about how they adapt to different situations and tasks.

A team of researchers from Tübingen, Princeton, Stanford, Newcastle, and Washington investigated how the timeline of a brain region varies during task execution.

Specifically, they asked: When a person focuses or redirects their visual attention to a specific point in space, how does that change the time scale of neural activity in the corresponding brain region?

To answer this, the researchers analyzed previously published data recorded from visual cortex V4—the brain region involved in visual attention—of macaques during two different visual attention tasks.

For both tasks, the team observed that neural activity unfolded not on one time scale, but at least on two different scales: a slow and a fast time scale. Remarkably, the slow-paced time scale also changed during task execution: the more attention was directed at a region in the visual field, the slower the activity in corresponding neuronal groups became slower. Furthermore, they note that the slower the activity, the shorter the reaction times.

“This may sound counterintuitive, but it’s actually quite plausible,” comments Roxana Zarati, a researcher at the University of Tübingen and the Max Planck Institute for Biological Cybernetics.

“A slower timescale means there is a stronger relationship between the current state of the brain and its state a moment ago. When neurons are interested in something, they remember their previous activity better, and that means the timescale is slower.”

Rich network architecture enables flexible behaviour

The researchers wondered how a network of neurons could create these different time scales.

“We tested three different hypotheses using computer simulations,” says Anna Levina, associate professor at Tübingen and Ph.D. to Zerati. advisor.

“Do we see different time scales simply because some neurons run faster and others slower? Or, as a second option, could their different biophysical properties be responsible? Only our third guess proved correct: the answer lies not in the properties of individual neurons, but in the structure of the network “.

This shows the outline of the heads
These intrinsic time scales are not rigid and fixed. The image is in the public domain

Depending on how neurons communicate with each other, different time scales arise: so-called cluster networks, for example, generate slow time scales.

“You can compare an aggregate network to a European road system,” explains Levina, who led the project with colleague Tatiana Engel of Princeton.

“Any two places in Paris are well connected to each other, but it is very difficult to get from a village in Burgundy to a beach in Portugal. At the same time, the airline network can seem almost random. It is very difficult to get to a nearby city, but you can go anywhere.” Almost without many connecting flights, airline-like networks will not develop as much on long time scales as the road network.”

The team was able to create networks that replicated in a computer simulation on time scales from the experimental data. The models also explain the observed modulations in time scales during the tasks: the effectiveness of interactions between neurons increases slightly, which in turn changes the frequency of neuronal events.

The findings could change our view of the brain: “Our experimental observations combined with the computational model provide a basis for studying the association between network structure, functional brain dynamics, and plastic behavior,” the publication concludes.

About this research in Neuroscience News

author: press office
source: Max Planck Institute
communication: Press Office – Max Planck Institute
picture: The image is in the public domain

Original search: open access.
“Intrinsic temporal scales in the visual cortex change with selective attention and reflect spatial connectivity” by Roxana Zeraati et al. Nature Communications

a summary

Intrinsic temporal scales in the visual cortex change with selective attention and reflect spatial connectivity

Intrinsic time scales characterize the dynamics of subjective fluctuations in neural activity. The variability of intrinsic time scales across the neocortex reflects the functional specialization of cortical regions, but little is known about how intrinsic time scales change during cognitive tasks.

We measured intrinsic time scales of local beat activity within the columns of area V4 in male monkeys performing spatial attention tasks. Continuous upwelling activity was detected across at least two different time scales, fast and slow. The slower time scale increased when the monkeys attended to the site of the receptive fields and was associated with reaction times.

By evaluating the predictions of several network models, we found that spatiotemporal correlations in V4 activity were best explained by the model in which multiple temporal scales arise from repetitive interactions formed through spatially ordered connectivity, and intentional modification of temporal scales results in increased efficacy. Repetition. interactions.

Our results indicate that multiple temporal scales may arise from spatial connectivity in the visual cortex and change flexibly with cognitive state due to dynamic efferent interactions between neurons.

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