Biology subjects

Verschure, P. F.

Publications and source records attributed to Verschure, P. F..

7 recordsLinked to original sources

bioRxiv2024

Metastable Dynamics Emerge from Local Excitatory-Inhibitory Homeostasis in the Cortex at Rest

The dynamics of the human cortex are highly metastable, driving the spontaneous exploration of network states. This metastability depends on circuit-level edge-of-bifurcation dynamics, which emerge from firing-rate control through multiple mechanisms of excitatory-inhibitory (E-I) homeostasis. However, it is unclear how these contribute to the metastability of cortical networks. We propose that individual mechanisms of E-I homeostasis contribute uniquely to the emergence of resting-state dynamics and test this hypothesis in a large-scale model of the human cortex. We show that empirical connectivity and dynamics can only be reproduced when accounting for multiple mechanisms of E-I homeostasis. More specifically, while the homeostasis of excitation and inhibition enhances metastability, the regulation of intrinsic excitability ensures moderate synchrony, maximizing functional complexity. Furthermore, the modulation bifurcation modulation by the homeostasis of excitation and intrinsic excitability compensates for strong input fluctuations in connector hubs. Importantly, this only occurs in models accounting for local gamma oscillations, suggesting a relationship between E-I balance, gamma rhythms, and metastable dynamics. Altogether, our results show that cortical networks self-organize toward maximal metastability through the multi-factor homeostasis of E-I balance. Therefore, the benefits of combining multiple homeostatic mechanisms transcend the circuit level, supporting the metastable dynamics of large-scale cortical networks.

neuroscience↗

bioRxiv2024

Excitatory-Inhibitory Homeostasis and Bifurcation Control in the Wilson-Cowan Model of Cortical Dynamics

Although the primary function of excitatory-inhibitory (E-I) homeostasis is the maintenance of mean firing rates, the conjugation of multiple homeostatic mechanisms is thought to be pivotal to ensuring edge-of-bifurcation dynamics in cortical circuits. However, computational studies on E-I homeostasis have focused solely on the plasticity of inhibition, neglecting the impact of different modes of E-I homeostasis on cortical dynamics. Therefore, we investigate how oscillations and edge-of-bifurcation dynamics are shaped by the diverse mechanisms of E-I homeostasis employed by cortical networks. Using the Wilson-Cowan model, we explore how distinct modes of E-I homeostasis maintain stable firing rates in models with varying levels of input and how it affects circuit dynamics. Our results confirm that E-I homeostasis can be leveraged to control edge-of-bifurcation dynamics and that some modes of homeostasis maintain mean firing rates under higher levels of input by modulating the distance to the bifurcation. Additionally, relying on multiple modes of homeostasis ensures stable activity while keeping oscillation frequencies within a physiological range. Our findings tie relevant features of cortical networks, such as E-I balance, the generation of gamma oscillations, and edge-of-bifurcation dynamics, under the framework of firing-rate homeostasis, providing a mechanistic explanation for the heterogeneity in the distance to the bifurcation found across cortical areas. In addition, we reveal the functional benefits of relying upon different homeostatic mechanisms, providing a robust method to regulate network dynamics with minimal perturbation to the generation of gamma rhythms and explaining the correlation between inhibition and gamma frequencies found in cortical networks. Author summaryWe study how excitatory-inhibitory (E-I) homeostasis controls edge-of-bifurcation dynamics in cortical networks and how it impacts the generation of gamma oscillations. Importantly, while previous studies have limited E-I homeostasis to the plasticity of inhibition, we explore the wide range of mechanisms employed by cortical networks and, more importantly, how they interact. Here, we derive the mathematical solution for the Wilson-Cowan model under distinct modes of homeostasis and study how they shape model dynamics and the generation of gamma oscillations. That said, we demonstrate that E-I homeostasis, particularly of excitation and intrinsic excitability, modulates model dynamics relative to the bifurcation between damped and sustained oscillations in a manner previously unaccounted for, providing a mechanism for the implementation of heterogeneous distances to the bifurcation across cortical areas. Furthermore, our results stress the functional benefits of relying on multiple modes of homeostasis, allowing for the control of firing rates and circuit dynamics while ensuring that gamma oscillations remain within a physiological range and explaining the relationship between inhibition and gamma frequencies found in empirical data. With these results, we unify E-I balance, edge-of-bifurcation dynamics, and gamma oscillations under the lens of firing-rate homeostasis.

neuroscience↗

bioRxiv2022

Theta oscillations in the human hippocampus normalize the information content of episodic memory

The principles governing the formation of episodic memories from the continuous stream of sensory stimuli are not fully understood. Theoretical models of the hippocampus propose that the representational format of episodic memories comprise oscillations in the theta frequency band (2-8 Hz) that set the time boundaries in which discrete events are bound encoded in the gamma frequency range (>30 Hz). We investigated this temporal segmentation and binding process by analyzing the intracranial EEG (iEEG) of surgically implanted epileptic patients performing a virtual-navigation task. We found a positive correlation between sensory information density encountered by the subject and hippocampal theta-frequency, suggesting that the human hippocampus normalizes the information content of episodic memories relative to the density of sensory information. This interpretation is further supported by the observation that as a marker of mnemonic encoding, i.e. the amount of persistent gamma events, directly correlates with sensory information density, gamma-frequency power and the phase relation between theta and gamma oscillations remain constant. Using a theoretical model of the hippocampus, we build a model that analogously displays a similar normalization of gamma episodes per theta cycle relative to information density by accounting for the physiological signatures of theta-gamma coding through combining fast and slow inhibitory feedback. We propose that this intrinsic normalization mechanism optimizes the trade-off between the discretization and compression of continuous experience relative to the limited capacity of episodic memory. SummaryWe move in continuous time and space, yet we can encode and recall discrete episodes from our past experiences. The neural mechanism behind this discretization is not fully understood. It has been previously observed that rodent locomotion modulates ongoing hippocampal theta rhythms. Thus, raising the question of whether these slow rhythms bind events together during a single oscillatory cycle relative to the movement speed or overall information density. We quantified the effects of increasing locomotion and sensory information in modulating theta oscillations during virtual navigation with intracranial hippocampal activity from human epileptic patients.. We observed hippocampal theta waves increased with higher speed and higher sensory demands, thereby maintaining constant information per oscillatory cycle. These results highlight the role of hippocampal theta oscillations in discretizing ongoing experience relative to the available information and explain how episodic memory integrates a fixed number of items per oscillatory theta cycle irrespective of richness of the external world.

neuroscience↗

bioRxiv2020

Basal forebrain rhythmicity is modulated by the exploration phase of novel environments

Acquaintance to novel environments requires the encoding of spatial memories and the processing of unfamiliar sensory information in the hippocampus. Cholinergic signaling promotes the stabilization of hippocampal long-term potentiation (LTP) and contributes to theta-gamma oscillations balance, which is known to be crucial for learning and memory. However, the oscillatory mechanisms by which cholinergic signals are conveyed to the hippocampus are still poorly defined. We analyzed local field potentials from the basal forebrain (BF), a major source of cholinergic projections to the hippocampus, while rats explored a novel environment, and compared the modulation of BF theta (4-10Hz) and gamma (40-80Hz) frequency bands at distinct stages of spatial exploration. We found that BF theta and gamma display learning stage-related rhythmicity and that theta-gamma coupling is stronger at the later stages of exploration, a phenomenon previously observed in the hippocampus. Overall, our results suggest that the BF-hippocampal cholinergic signaling is conveyed via the stereotypical oscillatory patterns found during mnemonic processes, which questions the origins of the learning-related rhythmic activity found in the hippocampus. KEY-POINTSO_LIBasal forebrain theta oscillations decrease their strength in function of exploration time, as observed in the hippocampus. C_LIO_LIBF gamma ripples (bursting events) are longer after learning. C_LIO_LIBF Theta-gamma coupling increases after initial spatial exploration, suggesting BF cross-frequency coupling relation to the learning stage. C_LI

neuroscience↗

bioRxiv2019

A simplified spiking model of grid-cell scale and intrinsic frequencies

The hexagonal tessellation pattern of grid cells scales up progressively along the dorsal-to-ventral axis of the medial entorhinal cortex (MEC) layer II. This scaling gradient has been hypothesized to originate either from inter population synaptic dynamics as postulated by attractor networks, from projected theta frequencies to different axis levels, as in oscillatory models, or from cellular dynamics dependent on hyperpolarization-activated cation currents. To test the hypothesis that intrinsic cellular properties account for the scale gradient as well as the different oscillatory frequencies observed along the dorsal-to-ventral axis, we have modeled and analyzed data from a population of grid cells simulated with spiking neurons interacting through low-dimensional attractor dynamics. To investigate the causal relationship between oscillatory frequencies and grid scale increase, we analyzed the dominant frequencies of the membrane potential for cells with distinct after-spike dynamics. We observed that intrinsic neuronal membrane properties of simulated cells could induce an increase of grid scale when modulated by after-spike reset values. Differences in the membrane potential oscillatory frequency were observed along the simulated dorsal-to-ventral axis, suggesting that, rather than driving to the increase of grid scale as proposed by interference models of grid cells, they are the result of intrinsic cellular properties of neurons at each axis level. Overall, our results suggest that the after-spike dynamics of cation currents may play a major role in determining the grid cells scale and that oscillatory frequencies are a consequence of intrinsic cellular properties that are specific to different levels of the dorsal-to-ventral axis in the MEC layer II.

neuroscience↗

bioRxiv2018

Human vicarious trial and error is predictive of spatial navigation performance

When learning new environments, rats often pause at decision points and look back and forth over their possible trajectories as if they were imagining the future outcome of their actions, a behavior termed \"Vicarious trial and error\" (VTE). As the animal learns the environmental configuration, rats change from deliberative to habitual behavior, and VTE tends to disappear, suggesting a functional relevance in the early stages of learning. Despite the extensive research on spatial navigation, learning and VTE in the rat model, fewer studies have focused on humans. Here, we tested whether head-scanning behaviors that humans typically exhibit during spatial navigation are as predictive of spatial learning as in the rat. Subjects performed a goal-oriented virtual navigation task in a symmetric environment. Spatial learning was assessed through the analysis of trajectories, timings, and head orientations, under habitual and deliberative spatial navigation conditions. As expected, we found that trajectory length and duration decreased with the trial number, implying that subjects learned the spatial configuration of the environment over trials. Interestingly, IdPhi (a standard metric of VTE) also decreased with the trial number, suggesting that humans benefit from the same head-orientation scanning behavior as rats at spatial decision-points. Moreover, IdPhi captured exclusively at the first decision-point of each trial, was correlated with trial trajectory duration and length. Our findings demonstrate that in VTE is a signature of the stage of spatial learning in humans, and can be used to predict performance in navigation tasks with high accuracy.

animal behavior and cognition↗

bioRxiv2017

Insect behavioral evidence of spatial memories during environmental reconfiguration

Insects are great explorers, able to navigate through long-distance trajectories and successfully find their way back. Their navigational routes cross dynamic environments suggesting adaptation to novel configurations. Arthropods and vertebrates share neural organizational principles and it has been shown that rodents modulate their neural spatial representation accordingly with environmental changes. However, it is unclear whether insects reflexively adapt to environmental changes or retain memory traces of previously explored situations. We sought to disambiguate between insect behavior at environmental novel situations and reconfiguration conditions. An immersive mixed-reality multi-sensory setup was built to replicate multi-sensory cues. We have designed an experimental setup where female crickets Gryllus Bimaculatus were trained to move towards paired auditory and visual cues during primarily phonotactic driven behavior. We hypothesized that insects were capable of identifying sensory modifications in known environments. Our results show that, regardless of the animals history, novel situation conditions did not compromise the animals performance and navigational directionality towards a novel target location. However, in trials where visual and auditory stimuli were spatially decoupled, the animals heading variability towards a previously known location significantly increased. Our findings showed that crickets are able to behaviorally manifest environmental reconfiguration, suggesting the encoding for spatial representation.

animal behavior and cognition↗