By combining dynamic neural fields with stochastic optimal control systems the present framework explains a broad range of findings from experimental studies in both humans and animals, such as the influence of decision variables on the neuronal activity in parietal and premotor cortex areas, the effect of action competition on both motor and decision behavior, and the influence of effector competition on the neuronal activity in cortical areas that plan eye and hand movements.

It provides insights to a variety of findings in neurophysiological and behavioral studies, such as the competitive interactions between populations of neurons within [27, 28] and between [29] brain regions that result frequently in spatial averaging movements [10, 18, 30] and the effects of decisions variables on neuronal activity [31, 32].

The computational framework presented in the current study is a systems-level framework aimed to qualitatively model and predict response patterns of neuronal activities in ensembles of neurons, as well as decision and motor behavior in action selection tasks With competing alternatives.

However, it captures many features of neuronal activity recorded from different cortical areas such as the parietal reach region (PRR), area 5, lateral intraparietal area (LIP), premotor cortex, prefrontal cortex (PFC) and orbitofrontal cortex (OFC) in nonhuman primates that perform reaching and saccadic decision tasks with competing options.

The results showed that the present framework can reproduce many characteristics of neuronal activity and behavior reported in the eXperimental literature.

The neuronal activity was weaker while both potential targets were presented prior to the reach onset, compared to the single-target trials [27, 33].

Once an effector is chosen, the neuronal activity in the corresponding cortical area increases, while the activity in the area selective for the non-selected effector decreases.

A novel tri-compartment model accounting for astroglial Kir4.1 channels and membrane potential dynamics in K“ regulation of neuronal activity

Several models have investigated extracellular K+ regulation of neuronal activity , including glial uptake mechanisms [13—17,24,33,34].

To unravel the acute role of astrocytes in extracellular K+ homeostasis and neuronal activity , we used electrophysiological recordings With a tri-compartment model accounting for K+ dynamics between neurons, astrocytes and the extracellular space.

Impact of Kir4.1-mediated potassium buffering on neuronal activity in physiology and pathology

Perisynaptic astroglial processes are enriched in ionic channels, neurotransmitter receptors and transporters, enabling astrocytes to detect neuronal activity via calcium signaling [3] and ionic currents with various components, such as glutamate and GABA transporter [4—7] or potassium (K+) [8—10].

Thus astrocytes regulate neuronal activity through multiple mechanisms, involving signaling or homeostasis of extracellular space volume, glutamate, GABA or K+ levels [11].

Modeling studies have mostly investigated astroglial regulation of [K‘L]O during pathological conditions to clarify its impact on aberrant neuronal activity .

To model K+ ions dynamics during neuronal activity , we built a biophysical model that includes three compartments: the neuron, the astrocyte and the extracellular space (Fig.

Using this model, we shall investigate quantitatively the contribution of Kir4.1 channels to K+ uptake in relation to neuronal activity associated with different [K+]O.

We investigated the dynamics of the K+ cycle between neurons, extracellular space and astro-cytes induced by neuronal activity to decipher the time needed to restore basal extracellular and intra-neuronal K+ levels.

However, up to date, there has been no principled method to estimate the parameters of this model: mainly, the typical number of neurons K from the population involved in conveying the percept, and the typical time scale w during which these neurons’ activities are integrated.

(The number of neurons actually recorded is generally much smaller.)

Finally, we can measure a choice signal for each neuron, which captures the trial-to-trial co-variation of neuron activity ri(t) with the animal’s choice (Fig.

Here, each neuron’s spike train ri(t) is first integrated into a single number describing the neuron’s activity over the trial.

These signals, weak but often significant, arise from the unknown process by which each neuron’s activity influences—or is influenced by—the animal’s perceptual decision.

NMDA receptors also modify neuronal activity in lower areas, and another candidate receptor that could have a specific role in the influence of feedback connections on plasticity are metabotropic glutamate receptors, which are prominent in feedback pathways [86,87] and known to influence synaptic plasticity [88].

In the delayed saccade/anti-saccade task, training induced persistent neuronal activity tuned to the cue location and to the color of the fixation point, but only if it was relevant.

Furthermore, a recent study demonstrated that se-rotonergic neurons also carry a reward-predicting signal and that the optogenetic activation of serotonergic neurons acts as a positive reinforcer [65].

The first three tasks have been used to study the influence of learning on neuronal activity in area LIP and the fourth task to study Vibrotactile working memory in multiple cortical regions.