Thomas S. J. Burger, Rodolphe Sepulchre · q-bio.NC, cs.SY, eess.SY
Computational models 96%
Abstract
Neuronal excitability is commonly modeled using different principles at the cellular and population scales, making it difficult to study how microscopic mechanisms shape macroscopic activity. We introduce a spatiotemporal excitatory-inhibitory (EI) model that unifies these scales through a common mixed-feedback architecture. The model retains the conductance-based circuit structure of Hodgkin-Huxley models and describes intrinsic and synaptic currents through memductances whose temporal and spatial voltage dependence encodes dynamics at the relevant scale. In its minimal form, temporal excitability arises from fast excitatory feedback opposed by slower inhibitory feedback, while spatial excitability arises from short-range excitation opposed by longer-range inhibition, recovering the core lateral-inhibition motif of Amari neural fields. We show that combining these mechanisms gives rise to population excitability in strongly heterogeneous populations. In particular, the model generates population spikes that are localized in both space and time and possess population-level scales distinct from those of individual neuronal spikes, despite substantial cell-to-cell parameter variability. We then show that both intrinsic temporal excitability and spatial synaptic feedback are required for coherent mean-field events and robust spatiotemporal population rhythms. Finally, selective modulation of memductances in a subset of the population tunes the frequency of the population rhythm, illustrating how changes at the cellular scale can shape macroscopic dynamics. By separating circuit architecture from modeling scale, the proposed framework provides a physically interpretable approach to analyzing and designing neuronal excitability across scales.
Shakleina, P., Guy, A. T., Trombetta, E. M. · neuroscience
Computational models 95%
Abstract
Hippocampal sharp-wave ripple (SWR) events occur during post-experience rest and sleep and play a significant role in memory consolidation. Recent ex vivo studies have found orexin-driven neuromodulatory effects on SWR parameters and proposed that they can influence memory consolidation. Additionally, behavioral studies demonstrate contradictory results regarding orexinergic effects on memory acquisition depending on task structure and intervention time. This inconsistency, along with the diffuse spread of orexin receptors (OXR) in the CA1 and CA3 hippocampus, challenges theoretical interpretation of orexinergic effects on memory. To reconcile this problem, we propose a rate-based model linking orexinergic tone-dependent SWR statistics to memory performance. The model combines an encoding module whose gain scales with the global orexinergic tone with a consolidation module driven by OX2R-specific replay dynamics. The model is based on a plasticity rule that considers SWR events as the unit of induction. Therefore, weight change requires sufficient replay content and depolarizing drive. We observed that, depending on the task structure, encoding-dominant learning benefits discrimination as the orexin drive increases. Consolidation-dominant learning shows a non-monotonic relationship with an internal optimum level of orexinergic tone. This interaction highlights that stronger truncation at higher receptor engagement shortens memory replay and reduces the efficacy of the consolidation process. The model predicts that the optimal regime depends on task complexity reflected in sequence length. Together, these results demonstrate how task demands determine whether increased orexinergic drive enhances or impairs effective consolidation and SWR-dependent replay in the hippocampus.
Liu, Y., Tian, L. Y., Tenenbaum, J. B. et al. · neuroscience
Computational models 94%Neural coding 66%
Abstract
How does the brain generate sequences of discrete elements, such as actions or words, that follow an abstract rule (a grammar)? Motivated by an experiment in which monkeys learned to draw multi-object images according to grammatical rules, we trained a recurrent neural network to generate sequences Am Bn: producing shape A m times, then B n times, or vice versa. Our findings are threefold. First, the network plans the sequence by moving to an initial state encoding m and n along orthogonal directions for upcoming execution. Second, the network repeats each shape by progressing along a shape-specific manifold. Third, transitions from Am to Bn follow parallel trajectories between manifolds across values of n, thus preserving n. Each of these is an experimentally testable prediction. The network generalizes to longer novel sequences by starting further along the same orthogonal directions. These results reveal a neural circuit implementation of a symbolic algorithm for generating rule-based action sequences.
Clinical sleep medicine repeatedly documents profound divergences between objective sleep duration and subjective daytime impairment, notably exemplified by sleep state misperception in paradoxical insomnia and boundary instability in narcolepsy type 1. Here, we present DyssomniaNN: a multi-scale biophysical computational framework coupling Borb\'ely's two-process regulation of sleep homeostasis and circadian drive with Saper's hypothalamic reciprocal flip-flop circuit and a recurrent corticothalamic leaky integrate-and-fire spiking neural network. We formalize five clinical cohorts across an identical 72-hour photoperiod: healthy control, circadian rhythm sleep-wake disorder (delayed-phase), chronic insomnia, paradoxical insomnia, and narcolepsy type 1. The synthetic local field potential converges closely with empirical human electroencephalography spectra from the PhysioNet Sleep-EDF database across all canonical frequency bands (spectral mean absolute error of 0.33--0.35). Furthermore, we introduce the Well-Rested Index to quantify restorative efficacy. Simulations reveal that sleep duration fails to predict restorative recovery: paradoxical insomnia achieves normal total sleep time (21.15 h) yet yields low restoration (37.6%) from unsuppressed cortical beta-band intrusion, whereas hypocretin deficiency in narcolepsy produces severe nocturnal fragmentation (781 min of wake after sleep onset) and 35 sleep-onset rapid eye movement periods. DyssomniaNN provides an in silico platform for mechanistic sleep research and virtual neuropharmacology.
Errington, S. P., Agayby, B., Berger, J. I. et al. · neuroscience
Neural coding 91%
Abstract
The ability to extract structure from sensory sequences requires tracking not only stimulus identity but also ordinal position within a sequence. How and where ordinal information is represented in the primate brain remains unclear. Here, we recorded activity in populations of single neurons simultaneously from auditory cortex and ventrolateral prefrontal cortex in macaques passively listening to structured auditory sequences. Population-level analyses revealed that frontal cortex activity evolved progressively across the sequence, occupying distinct regions of neural state space for successive ordinal positions. In contrast, auditory cortex population activity reset with each element, producing stereotyped, stimulus-locked trajectories. Consistent with this dissociation, frontal cortex populations robustly decoded ordinal position independently of stimulus identity, whereas auditory cortex showed weak positional decoding despite strong identity representations. Frontal neurons further exhibited longer intrinsic timescales and reduced synchronization to element presentation, supporting integration of information across multiple elements. Together, these findings identify frontal activity as a key substrate for ordinal position encoding during auditory sequence processing, while auditory cortex primarily supports element-specific sensory representations.
Green, L., Ito, S., Haufler, D. et al. · neuroscience
Computational models 90%Neural coding 30%
Abstract
The mammalian neocortex is a complex structure with many interacting regions and cell types. Multiple efforts have recently generated large datasets from multiple cortical regions to determine generalizing principles of cortical organization and activity. One such generalizing principle is the concept of a cortical hierarchy - low-dimensional macroscopic gradients of biological properties across cortical areas and a hierarchy of timescales for information processing. Such gradients in properties along a cortical hierarchy may explain how different cortical regions serve different computational roles (i.e. sensory processing versus working memory). We set out to investigate such principles using biologically realistic simulations. Starting from our previously published model of the mouse primary visual cortex (VISp) with updated connectivity reflecting recent large-scale connectomics and synaptic physiology datasets, we generated multiple versions of the cortical circuit model with increased connectivity as measured along the cortical hierarchy. We then measure the resulting neural activity in response to a simulated single-cell photostimulation paradigm and thalamocortical inputs. We found that overall circuit stability increased across the cortical hierarchy, due to both increases in connection density and increases in the relative proportion of SST cells. The results of this study link cortical cell types to population-level activity, generating insight into the function of local circuits across cortex.