Section: New Results

From the microscopic to the mesoscopic scale

Participants: Laure Buhry, Axel Hutt, Francesco Giovannini, Mélanie Aussel, Ivan Kotiuchi.

In collaboration with Radu Ranta (university of Lorraine), Beate Knauer and Motoharu Yoshida (Ruhr university) and LieJune Shiau (university of Houston)

Neural oscillations are thought to be correlated with the execution of cognitive functions. Indeed, gamma oscillations are often recorded in functionally-coupled brain regions for cooperation during memory tasks, and this rhythmic behavior is thought to result from synaptic GABAergic interactions between interneurons. Interestingly, GABAergic synaptic and extrasynaptic receptors have been shown to be the preferred target of the most commonly used anesthetic agents. We presented a in-depth computational study (F. Giovannini and L. Buhry, Tonic inhibition mediates a synchronization enhancement during propofol anesthesia in a network of hippocampal interneurons: a modeling study Journal of computational neuroscience (Submitted) 2017) [1] of the action of anesthesia on neural oscillations by introducing a new mathematical model which takes into account the four main effects of the anesthetic agent propofol on GABAergic hippocampal interneurons. These are: the action on synaptic GABA_A receptors, which includes an amplification and an extension of the duration of the synaptic currents, as well as an increase in current baseline, and the action on extrasynaptic GABA_A receptors mediating a tonic inhibitory current. Our results indicate that propofol-mediated tonic inhibition contributes to an unexpected enhancement of synchronization in the activity of a network of hippocampal interneurons. This enhanced synchronization could provide a possible mechanism supporting the occurrence of intraoperative awareness, explicit memory formation, and even paradoxical excitation under general anesthesia, by transiently facilitating the communication between brain structures which should supposedly be not allowed to do so when anesthetized.

Ring networks, a particular form of Hopfield neural networks, can be used to model the activity of place cells, a type of cells in the hippocampus that are involved in the building and memorization of a cognitive map of one’s environment. The behavior of these models is highly dependent on their recurrent synaptic connectivity matrix and on individual neurons' activation function, which must be chosen appropriately to obtain physiologically meaningful conclusions. In [4], we proposed several simpler ways to adjust this synaptic connectivity matrix compared to existing literature so as to achieve stability in a ring attractor network with a piece-wise affine activation functions, and we link these results to the possible stable states the network can converge to.