Exact neural mass model for synaptic-based working memory

Abstract

A synaptic theory of Working Memory (WM) has been developed in the last decade as a possible alternative to the persistent spiking paradigm. In this context, we have developed a neural mass model able to reproduce exactly the dynamics of heterogeneous spiking neural networks encompassing realistic cellular mechanisms for short-term synaptic plasticity. This population model reproduces the macroscopic dynamics of the network in terms of the firing rate and the mean membrane potential. The latter quantity allows us to gain insight of the Local Field Potential and electroencephalographic signals measured during WM tasks to characterize the brain activity. More specifically synaptic facilitation and depression integrate each other to efficiently mimic WM operations via either synaptic reactivation or persistent activity. Memory access and loading are related to stimulus-locked transient oscillations followed by a steady-state activity in the β-γ band, thus resembling what is observed in the cortex during vibrotactile stimuli in humans and object recognition in monkeys. Memory juggling and competition emerge already by loading only two items. However more items can be stored in WM by considering neural architectures composed of multiple excitatory populations and a common inhibitory pool. Memory capacity depends strongly on the presentation rate of the items and it maximizes for an optimal frequency range. In particular we provide an analytic expression for the maximal memory capacity. Furthermore, the mean membrane potential turns out to be a suitable proxy to measure the memory load, analogously to event driven potentials in experiments on humans. Finally we show that the γ power increases with the number of loaded items, as reported in many experiments, while θ and β power reveal non monotonic behaviours. In particular, β and γ rhythms are crucially sustained by the inhibitory activity, while the θ rhythm is controlled by excitatory synapses.

Neuronal cascades shape whole-brain functional dynamics at rest

“…The low computational load enables the simulation of fMRI-like sessions in TVB. Future applications allow the possibility of including synaptic dynamics (Taher, Torcini, & Olmi, 2020), adaptation (Gast, Schmidt, & Knösche, 2020), excitatory versus inhibitory populations (Dumont & Gutkin, 2019; Laing, 2017; Montbrío et al, 2015), among others. The use of these models establishes a new venue to address multiscale phenomena, maintaining the co-existence of microscopic and macroscopic scales of organization (Coombes & Byrne, 2019).…”

Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation

“…The presence of damped oscillations at the macroscopic level reflects the transitory synchronous firing of a fraction of the neurons in the ensemble. While this behavior is common in network models of spiking neurons, it is not captured by traditional firing-rate models (Schaffer et al, 2013 ; Devalle et al, 2017 ; Taher et al, 2020 ).…”

“…Furthermore, it has been successfully employed to reveal the mechanisms at the basis of theta-nested gamma oscillations (Ceni et al, 2020 ; Segneri et al, 2020 ) and the coexistence of slow and fast gamma oscillations (Bi et al, 2020 ). Finally, it has been recently applied to modeling electrical synapses (Montbrió and Pazó, 2020 ), working memory (Taher et al, 2020 ), the influence of transcranial magnetic stimulation on brain dynamics (Byrne et al, 2020 ), and brain resting state activity (Rabuffo et al, 2020 ).…”

“…The possibility to exactly move through the scales has not been fully exploited in this study, since we have focused the analysis on the extension of the single neural mass model to a multipopulation model, without adding other relevant features to the original model. However, it is possible to easily introduce, in the multipopulation model, biologically relevant characteristics, keeping intact the exact correspondence between microscopic and macroscopic scales, such as short-term synaptic plasticity (Taher et al, 2020 ), synaptic delays (Devalle et al, 2018 ), electrical coupling via gap junctions (Montbrió and Pazó, 2020 ), chemical synapses (Coombes and Byrne, 2019 ), and extrinsinc and endogenous noise (Goldobin et al, 2021 ). By adding short-term synaptic plasticity we expect to be able to reproduce the emergence of self-sustained activity in the high-activity state and, therefore, to describe a fully developed seizure.…”

A model of metabolic supply-demand mismatch leading to secondary brain injury

“…It can be categorized into two main families, macroscopic models and microscopic models, according to the level of biological organization they aim to represent. The former one focuses on describing the mean activities (such as mean firing rates, mean postsynaptic potentials) of neuronal populations (6)(7)(8)(9). In contrast, the latter one focuses on quantifying the dynamical behavior of single neurons, which are considered to be the primary computational units of brain function (10)(11)(12)(13)(14)(15).…”