Biology subjects

Di Volo, M.

Publications and source records attributed to Di Volo, M..

5 recordsLinked to original sources

bioRxiv2025

A mean-field model of neural networks with PV and SOM interneurons reveals connectivity-based mechanisms of gamma oscillations

Classic theoretical models of cortical oscillations are based on the interactions between two populations of excitatory and inhibitory neurons. Nevertheless, experimental studies and network simulations suggest that interneuron subclasses such as parvalbumin (PV) and somatostatin (SOM) exert distinct control over oscillatory dynamics. Yet, we lack a theoretical understanding of the mechanisms underlying oscillations in E-PV-SOM circuits and of the differences with respect to the classical mechanisms for oscillations in simpler E-I networks. Here, we derive a biologically realistic mean-field model of a canonical three-population E-PV-SOM circuit. This model robustly generates oscillations whose features are consistent with experimental observations, including the relative timing of PV and SOM activity and the effects of optogenetic perturbations. By reducing the model to a linear analytical form, we demonstrate that gamma oscillations emerge directly from the cell-specific connectivity of the three-population circuit. This connectivity motif alone accounts for experimentally observed phase relationships, with PV activity consistently leading that of SOM neurons. Together, this mean field model identifies a distinct structural mechanism giving rise to oscillations in canonical E-PV-SOM circuits and provides theoretical primitives for constructing large-scale, cell-type-specific models of cortical dynamics.

neuroscience↗

bioRxiv2025

Neural dynamics reveal foraging-like computations in the frontal cortex

The prefrontal cortex has a central role in flexible behavior. Computations for weighing alternatives, committing to an option, integrating evidence, and abandoning an option are dominantly formalized in reinforcement learning (RL). Yet, ecological considerations showcase foraging as a competing account. Identifying how the brain actually solves efficient weighing of options requires the identification of the neural representation of this abstract yet precise computation. We recorded neural populations from three areas of the primate frontal cortex involved in decision making to characterize neural dynamics while animals performed a standard RL task. By combining behavioral and neuron population analysis, we found a neural subspace in midcingulate (MCC) and ventrolateral prefrontal cortex (vLPFC) that precisely tracked value computations. We demonstrated that MCC dynamics showed signatures of foraging-like computation but conflicted with RL requirements. Our study thus provides a detailed neural account of foraging-like flexibility in the primate frontal cortex.

neuroscience↗

bioRxiv2025

Biologically realistic mean field model of spiking neural networks with fast and slow inhibitory synapses

We present a mean field model for a spiking neural network of excitatory and inhibitory neurons with fast GABAA and nonlinear slow GABAB inhibitory conductance-based synapses. This mean field model can predict the spontaneous and evoked response of the network to external stimulation in asynchronous irregular regimes. The model displays theta oscillations for sufficiently strong GABAB conductance. Optogenetic activation of interneurons and an increase of GABAB conductance caused opposite effects on the emergence of gamma oscillations in the model. In agreement with direct numerical simulations of neural networks and experimental data, the mean field model predicts that an increase of GABAB conductance reduces gamma oscillations. Furthermore, the slow dynamics of GABAB synapses regulates the appearance and duration of transient gamma oscillations, namely gamma bursts, in the mean field model. Finally, we show that nonlinear GABAB synapses play a major role to stabilize the network from the emergence of epileptic seizures.

neuroscience↗

bioRxiv2021

Coherent oscillations in balanced neural networks driven by endogenous fluctuations

We present a detailed analysis of the dynamical regimes observed in a balanced network of identical Quadratic Integrate-and-Fire (QIF) neurons with a sparse connectivity for homogeneous and heterogeneous in-degree distribution. Depending on the parameter values, either an asynchronous regime or periodic oscillations spontaneously emerge. Numerical simulations are compared with a mean field model based on a self-consistent Fokker-Planck equation (FPE). The FPE reproduces quite well the asynchronous dynamics in the homogeneous case by either assuming a Poissonian or renewal distribution for the incoming spike trains. An exact self consistent solution for the mean firing rate obtained in the limit of infinite in-degree allows identifying balanced regimes that can be either mean- or fluctuation-driven. A low-dimensional reduction of the FPE in terms of circular cumulants is also considered. Two cumulants suffice to reproduce the transition scenario observed in the network. The emergence of periodic collective oscillations is well captured both in the homogeneous and heterogeneous set-ups by the mean field models upon tuning either the connectivity, or the input DC current. In the heterogeneous situation we analyze also the role of structural heterogeneity.

neuroscience↗

bioRxiv2021

Nonlinear collision between anisotropic propagating waves in mouse somatosensory cortex

How does cellular organization shape the spatio-temporal patterns of activity in the cortex while processing sensory information? After measuring the propagation of activity in the mouse primary somatosensory cortex (S1) in response to single whisker deflections with Voltage Sensitive Dye (VSD) imaging, we developed a two dimensional mean field model of S1. We observed that, for strong enough excitatory cortical interactions, whisker deflections generate a propagating wave in S1. We developed an inversion method that reconstructs model parameters from VSD data, revealing that a spatially heterogeneous organization of synaptic strengths between pyramidal neurons in S1 is likely to be responsible for the anisotropic spatio-temporal patterns of activity measured experimentally. Finally, we report that two consecutive stimuli activating different spatial locations in S1 generate two waves which collide sub-linearly. In the model, such sub-linear interaction is explained by a lower sensitivity to external perturbations of neural networks during activated states.

neuroscience↗