Towards using large-scale, cross-brain neuronal recordings to identify the brain’s internal signals
Neural activity is often analyzed with respect to external referents, such as the onset of a sensory stimulus or an overt motor action. Simultaneous recordings allow referencing neurons’ activity to each other and thus detecting signals that are internal to the organism. Further, multi-region simultaneous recordings allow observing how these internal signals are coordinated across the brain. Following this logic in rats performing a perceptual decision-making task, we recorded simultaneously from thousands of neurons across up to 20 brain regions at once. Here we report two internal signals which we found to profoundly shape decision-related neural dynamics and brain states. First, we decoded the continuously evolving decision state separately from each region, and found surprisingly large magnitude co-fluctuations in these measures. Dimensionality analysis showed these to be dominated by a single state variable, suggesting that only a single decision-making computation, not multiple parallel computations, are being carried out during the analyzed period. Second, we found that the precise time the subject commits to a decision – a covert event that we decoded from large-scale neural activity in primary motor cortex – was accompanied by a coordinated change, across the brain, from a decision formation to a post-commitment state. The two states differ substantially in their choice-predictive neural dynamics and in their inter-region correlations. Therefore, knowing the time of this state change on single trials is needed to correctly parse fundamentally different phases of decision-making. Overall, our data suggest that internally-referenced signals and state changes, not timelocked to external events but detectable through simultaneous recordings, are major features of neural activity during cognition. VVTNS 2026 Opening Lecture. Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-01-07. Recording duration: 00:42:45.
Multiscale modeling of brain states, from spiking networks to the whole brain
Modeling brain mechanisms is often confined to a given scale, such as single-cell models, network models or whole-brain models, and it is often difficult to relate these models. Here, we show an approach to build models across scales, starting from the level of circuits to the whole brain. The key is the design of accurate population models derived from biophysical models of networks of excitatory and inhibitory neurons, using mean-field techniques. Such population models can be later integrated as units in large-scale networks defining entire brain areas or the whole brain. We illustrate this approach by the simulation of asynchronous and slow-wave states, from circuits to the whole brain. At the mesoscale (millimeters), these models account for travelling activity waves in cortex, and at the macroscale (centimeters), the models reproduce the synchrony of slow waves and their responsiveness to external stimuli. This approach can also be used to evaluate the impact of sub-cellular parameters, such as receptor types or membrane conductances, on the emergent behavior at the whole-brain level. This is illustrated with simulations of the effect of anesthetics. The program codes are open source and run in open-access platforms (such as EBRAINS).