How the brain slows down when we focus our gaze

Summary: A new study reveals that the brain can switch between slow and fast integration of information, allowing it to modulate the time scales on which it operates. The study also provides insight into how the structure of neural networks determines the rate at which information is integrated, which could have implications for future research on brain function and cognitive processes.

Source: Max Planck Institute

Alternating between slow and rapid integration of information, the brain can flexibly modulate the time scales on which it operates.

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

Their analysis of experimental data from the visual cortex and their computer simulations also explain how different time scales can arise and how they can change: the structure of neural networks determines how fast or slow information is integrated.

Different processes in the brain occur on different timescales: while sensory input can be processed in tens of milliseconds, decision-making or other complex cognitive processes may require the integration of information over several minutes. As a result, some areas of the brain are faster than others.

These intrinsic time scales are neither rigid nor invariable. However, until now, little was known about how they can adapt to different situations and tasks.

A team of researchers from Tübingen, Princeton, Stanford, Newcastle and Washington has now studied how the time scale of a brain area can vary during the execution of a task.

Specifically, they asked: when a subject focuses their visual attention or redirects it to a specific point in space, how does this alter the timescale of neural activity in the corresponding brain area?

To answer this, the researchers analyzed previously published data recorded from visual cortex V4 – the brain area involved in visual attention – from macaque monkeys during two different visual attention tasks.

For both tasks, the team observed that neural activity did not take place on a single timescale, but on at least two different timescales: a slow timescale and a fast timescale. Remarkably, the slow-paced time scale also changed during task performance: each time attention was directed to an area of ​​the visual field, the slow activity in the corresponding neuronal populations became even slower. Moreover, they observed that the slower the activity, the shorter the reaction times.

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

“A slow time scale means there is a stronger correlation between the current state of the brain and its state a moment ago. When neurons are busy with something, they better remember their own past activity , which implies a slower timescale.

Rich network structure enables flexible behavior

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

“We tested three different hypotheses with computer simulations,” says Anna Levina, assistant professor at Tübingen and doctoral student at Zeraati. advise.

“Do we see the different timescales just because some neurons work faster and some slower? Or, as a second option, could their different biophysical properties be responsible? Only our third guess turned out to be true: the answer lies not in the properties of individual neurons, but in the structure of the network.

Depending on how the neurons are connected to each other, different time scales arise: so-called cluster networks, for example, generate slow time scales.

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

“Two places in Paris are very well connected to each other, but it is much more difficult to get from a village in Burgundy to a beach in Portugal. At the same time, the air network can seem almost random. It is very difficult to reach a nearby town, but you can get almost anywhere without a lot of connecting flights.Networks that are more like airlines would not evolve over such long timescales as the road network.

The team was able to build networks that reproduced exactly in the computer simulation the time scales of the experimental data. The models also take into account the modulations observed in the time scales during the tasks: the efficiency of the interactions between neurons increases slightly, which in turn modifies the rhythm of the neuronal events.

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

About this neuroscience research news

Author: Press office
Source: Max Planck Institute
Contact: Press Office – Max Planck Institute
Picture: Image is in public domain

Original research: Free access.
“Intrinsic visual cortex timescales change with selective attention and reflect spatial connectivity” by Roxana Zeraati et al. Nature Communication

Abstract

Intrinsic visual cortex timescales change with selective attention and reflect spatial connectivity

Intrinsic time scales characterize the dynamics of endogenous fluctuations in neuronal activity. The variation of intrinsic time scales across the neocortex reflects the functional specialization of cortical areas, but less is known about how intrinsic time scales change during cognitive tasks.

We measured the intrinsic time scales of local spike activity in the V4 area columns in male monkeys performing spatial attention tasks. Current peak activity occurred on at least two distinct timescales, fast and slow. The slow time scale increased as monkeys cared about the location of receptive fields and correlated with reaction times.

By evaluating the predictions of multiple network models, we found that spatiotemporal correlations in V4 activity were best explained by the model in which multiple timescales arise from recurrent interactions shaped by spatially arranged connectivity, and the Attentional modulation of time scales results from an increase in the efficiency of recurrent activity. interactions.

Our results suggest that multiple timescales may arise from spatial connectivity in the visual cortex and change flexibly with cognitive state due to dynamic efficient interactions between neurons.