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Recent experimental results have shown that GABAergic interneurons in the central nervous system are frequently connected via electrical synapses. Hence, depending on the area or the subpopulation, interneurons interact via inhibitory synapses or electrical synapses alone or via both types of interactions. The theoretical work presented here addresses the significance of these different modes of interactions for the interneuron networks dynamics. We consider the simplest system in which this issue can be investigated in models or in experiments: a pair of neurons, interacting via electrical synapses, inhibitory synapses, or both, and activated by the injection of a noisy external current. Assuming that the couplings and the noise are weak, we derive an analytical expression relating the cross-correlation (CC) of the activity of the two neurons to the phase response function of the neurons. When electrical and inhibitory interactions are not too strong, they combine their effect in a linear manner. In this regime, the effect of electrical and inhibitory interactions when combined can be deduced knowing the effects of each of the interactions separately. As a consequence, depending on intrinsic neuronal proper-ties, electrical and inhibitory synapses may cooperate, both promoting synchrony, or may compete, with one promoting synchrony while the other impedes it. In contrast, for sufficiently strong couplings, the two types of synapses combine in a nonlinear fashion. Remarkably, we find that in this regime, combining electrical synapses with inhibition ampli-fies synchrony, whereas electrical synapses alone would desynchronize the activity of the neurons. We apply our theory to predict how the shape of the CC of two neurons changes as a function of ionic channel conduc-tances, focusing on the effect of persistent sodium conductance, of the firing rate of the neurons and the nature and the strength of their interac-tions. These predictions may be tested using dynamic clamp techniques.

International audience; Enhancement of semantic priming effects (hyperpriming) has been well documented in schizophrenic patients (Minzenberg et al. 2002). In a previous study, we demonstrated that hyperpriming was related to a more pronounced requirement to inhibit unrelated information (Lecardeur et al. 2007). On the other hand, several authors have suggested that hyperpriming could reflect enhanced spreading of semantic activation in schizophrenia (Spitzer et al. 1993; Spitzer 1997; Moritz et al. 2001). So far, there are few reports that attempted to study the spreading of activation in schizophrenic patients compared to healthy controls, when the links between semantically related information were manipulated. Notably, authors stated that activation could consequently spread farther and further in the semantic networks of schizophrenic patients. Moreover, enhanced spreading of activation was hypothesized to be responsible of the appearance of thought and language disorders in schizophrenic patients. Consequently, we examined whether the manipulation of the type of connection and the semantic distance between prime and target can modulate semantic priming effects in schizophrenic patients compared to healthy controls.

Motivated by experimental observations of the head direction system, we study a three population network model that operates as a continuous attractor network. This network is able to store in a short-term memory an angular variable (the head direction) as a spatial profile of activity across neurons in the absence of selective external inputs, and to accurately update this variable on the basis of angular velocity inputs. The network is composed of one excitatory population and two inhibitory populations, with inter-connections between populations but no connections within the neurons of a same population. In particular, there are no excitatory-to-excitatory connections. Angular velocity signals are represented as inputs in one inhibitory population (clockwise turns) or the other (counterclockwise turns). The system is studied using a combination of analytical and numerical methods. Analysis of a simplified model composed of threshold-linear neurons gives the conditions on the connectivity for (i) the emergence of the spatially selective profile, (ii) reliable integration of angular velocity inputs, and (iii) the range of angular velocities that can be accurately integrated by the model. Numerical simulations allow us to study the proposed scenario in a large network of spiking neurons and compare their dynamics with that of head direction cells recorded in the rat limbic system. In particular, we show that the directional representation encoded by the attractor network can be rapidly updated by external cues, consistent with the very short update latencies observed experimentally by Zugaro et al. (2003) in thalamic head direction cells.