A reservoir of timescales emerges in recurrent circuits with heterogeneous neural assemblies

Consistent Hierarchies of Single-Neuron Timescales in Mice, Macaques, and Humans

The intrinsic timescales of single neurons are thought to be hierarchically organized across the cortex, but whether hierarchical variation in timescales is a general brain organizing principle across mammalian species remains unclear. Here, we took a cross-species approach and estimated neuronal timescales of thousands of single neurons recorded across frontal cortex, amygdala, and hippocampus in mice, monkeys, and humans of both sexes using a task-agnostic method. We identify largely consistent hierarchies of timescales in frontal and limbic regions across species: hippocampus had the shortest timescale whereas anterior cingulate cortex had the longest. Within this scheme, variability across species was found, most notably in amygdala and orbitofrontal cortex. We show that variation in timescales is not simply related to differences in spiking statistics nor the result of cytoarchitectonic features such as cortical granularity. Thus, hierarchically organized timescales are a consistent organizing principle across species and appear to be related to a combination of intrinsic and extrinsic factors.

Sleep Modulates Neural Timescales and Spatiotemporal Integration in the Human Cortex

Spontaneous neural dynamics manifest across multiple temporal and spatial scales, which are thought to be intrinsic to brain areas and exhibit hierarchical organization across the cortex. In wake, a hierarchy of timescales is thought to naturally emerge from microstructural properties, gene expression, and recurrent connections. A fundamental question is timescales' organization and changes in sleep, where physiological needs are different. Here, we describe two measures of neural timescales, obtained from broadband activity and gamma power, which display complementary properties. We leveraged intracranial electroencephalography in 106 human epilepsy patients (48 females) to characterize timescale changes from wake to sleep across the cortical hierarchy. We show that both broadband and gamma timescales are globally longer in sleep than in wake. While broadband timescales increase along the sensorimotor-association axis, gamma ones decrease. During sleep, slow waves can explain the increase of broadband and gamma timescales, but only broadband ones show a positive association with slow-wave density across the cortex. Finally, we characterize spatial correlations and their relationship with timescales as a proxy for spatiotemporal integration, finding high integration at long distances in wake for broadband and at short distances in sleep for gamma timescales. Our results suggest that mesoscopic neural populations possess different timescales that are shaped by anatomy and are modulated by the sleep/wake cycle.

Ramping dynamics in the frontal cortex unfold over multiple timescales during motor planning

Plans are formulated and refined throughout the period leading up to their execution, ensuring that the appropriate behaviors are enacted at the appropriate times. Although existing evidence suggests that memory circuits convey the passage of time through diverse neuronal responses, it remains unclear whether the neural circuits involved in planning exhibit analogous temporal dynamics. Using publicly available data, we analyzed how activity in the mouse frontal motor cortex evolves during motor planning. Individual neurons exhibited diverse ramping activity throughout a delay interval that preceded a planned movement. The collective activity of these neurons was useful for making temporal predictions that became increasingly precise as the movement time approached. This temporal diversity gave rise to a spectrum of encoding patterns, ranging from stable to dynamic representations of the upcoming movement. Our results indicate that ramping activity unfolds over multiple timescales during motor planning, suggesting a shared mechanism in the brain for processing temporal information related to both memories from the past and plans for the future. NEW & NOTEWORTHY Neuronal responses in the cortex are diverse, but the nature and functional consequences of this diversity remain ambiguous. We identified a specific pattern of temporal heterogeneity in the mouse frontal motor cortex, whereby the firing of different neurons ramps up at varying speeds before the execution of a movement. Our decoding analyses reveal that this heterogeneity in ramping dynamics enables precise and reliable encoding of movement plans and time across various timescales.

Hierarchical gradients of multiple timescales in the mammalian forebrain

Many anatomical and physiological features of cortical circuits, ranging from the biophysical properties of synapses to the connectivity patterns among different neuron types, exhibit consistent variation along the hierarchical axis from sensory to association areas. Notably, the temporal correlation of neural activity at rest, known as the intrinsic timescale, increases systematically along this hierarchy in both primates and rodents, analogous to the increasing scale and complexity of spatial receptive fields. However, how the timescales for task-related activity vary across brain regions and whether their hierarchical organization appears consistently across different mammalian species remain unexplored. Here, we show that both the intrinsic timescale and those of task-related activity follow a similar hierarchical gradient in the cortices of monkeys, rats, and mice. We also found that these timescales covary similarly in both the cortex and basal ganglia, whereas the timescales of thalamic activity are shorter than cortical timescales and do not conform to the hierarchical order predicted by their cortical projections. These results suggest that the hierarchical gradient of cortical timescales might represent a universal feature of intracortical circuits in the mammalian brain.

Consistent hierarchies of single-neuron timescales in mice, macaques and humans

The intrinsic timescales of single neurons are thought to be hierarchically organized across the cortex. This conclusion, however, is primarily based on analyses of neural responses from macaques. Whether hierarchical variation in timescales is a general brain organizing principle across mammals remains unclear. Here we took a cross-species approach and estimated neuronal timescales of thousands of single neurons recorded across multiple areas in mice, monkeys, and humans using a task-agnostic method. We identify largely consistent hierarchies of timescales in frontal and limbic regions across species: hippocampus had the shortest timescale whereas anterior cingulate cortex had the longest. Within this scheme, variability across species was found, most notably in amygdala and orbitofrontal cortex. We show that variation in timescales is not simply related to differences in spiking statistics nor the result of cytoarchitectonic features such as cortical granularity. Thus, hierarchically organized timescales are a consistent organizing principle across species and appear to be related to a combination of intrinsic and extrinsic factors.

Generalized Dynamical Mean Field Theory for Non-Gaussian Interactions

We present a generalized dynamical mean field theory for studying the effects of non-Gaussian quenched noise in a general set of dynamical systems. We apply the framework to the generalized Lotka-Volterra equations, a central model in theoretical ecology, where species interactions are fixed over time and heterogeneous. Our results show that the new mean field equations have solutions that depend on all cumulants of the distribution of species interactions. We obtain an analytic solution when the interaction couplings are α-stable distributed and find a relationship between the abundance distribution of species and the statistics of microscopic interactions. In the case of sparse interactions, which we investigate analytically, we establish a simple relationship between the distribution of interactions and the one of population densities.

Ramping cells in the rodent medial prefrontal cortex encode time to past and future events via real Laplace transform

In interval reproduction tasks, animals must remember the event starting the interval and anticipate the time of the planned response to terminate the interval. The interval reproduction task thus allows for studying both memory for the past and anticipation of the future. We analyzed previously published recordings from the rodent medial prefrontal cortex [J. Henke et al., eLife10, e71612 (2021)] during an interval reproduction task and identified two cell groups by modeling their temporal receptive fields using hierarchical Bayesian models. The firing in the "past cells" group peaked at the start of the interval and relaxed exponentially back to baseline. The firing in the "future cells" group increased exponentially and peaked right before the planned action at the end of the interval. Contrary to the previous assumption that timing information in the brain has one or two time scales for a given interval, we found strong evidence for a continuous distribution of the exponential rate constants for both past and future cell populations. The real Laplace transformation of time predicts exponential firing with a continuous distribution of rate constants across the population. Therefore, the firing pattern of the past cells can be identified with the Laplace transform of time since the past event while the firing pattern of the future cells can be identified with the Laplace transform of time until the planned future event.

Searching for long timescales without fine tuning

Animal behavior occurs on timescales much longer than the response times of individual neurons. In many cases, it is plausible that these long timescales emerge from the recurrent dynamics of electrical activity in networks of neurons. In linear models, timescales are set by the eigenvalues of a dynamical matrix whose elements measure the strengths of synaptic connections between neurons. It is not clear to what extent these matrix elements need to be tuned to generate long timescales; in some cases, one needs not just a single long timescale but a whole range. Starting from the simplest case of random symmetric connections, we combine maximum entropy and random matrix theory methods to construct ensembles of networks, exploring the constraints required for long timescales to become generic. We argue that a single long timescale can emerge generically from realistic constraints, but a full spectrum of slow modes requires more tuning. Langevin dynamics that generates patterns of synaptic connections drawn from these ensembles involves a combination of Hebbian learning and activity-dependent synaptic scaling.

Ramping Dynamics in the Frontal Cortex Unfold Over Multiple Timescales During Motor Planning

Plans are formulated and refined over the period leading to their execution, ensuring that the appropriate behavior is enacted at just the right time. While existing evidence suggests that memory circuits convey the passage of time through diverse neuronal responses, it remains unclear whether the neural circuits involved in planning behavior exhibit analogous temporal dynamics. Using publicly available data, we analyzed how activity in the frontal motor cortex evolves during motor planning. Individual neurons exhibited diverse ramping activity throughout a delay interval that preceded a planned movement. The collective activity of these neurons was useful for making temporal predictions that became increasingly precise as the movement time approached. This temporal diversity gave rise to a spectrum of encoding patterns, ranging from stable to dynamic representations of the upcoming movement. Our results indicate that neural activity unfolds over multiple timescales during motor planning, suggesting a shared mechanism in the brain for processing temporal information related to both past memories and future plans.