Synaptic inhibition controls transient oscillatory synchronization in a model of the insect olfactory system

Abstract

In a variety of neuronal systems it has been hypothesized that inhibitory interneurons corral principal neurons into synchronously firing groups that encode sensory information and sub-serve behavior (Buzsáki and Chrobak, 1995; Buzsáki, 2008). This mechanism is particularly relevant to the olfactory system where spatiotemporal patterns of projection neuron (PN) activity act as robust markers of odor attributes (Laurent et al., 1996; Wehr and Laurent, 1996). In the insect antennal lobe (AL), a network of local inhibitory interneurons arborizes extensively throughout the AL (Leitch and Laurent, 1996) providing inhibitory input to the cholinergic PNs. Our theoretical work has attempted to elaborate the exact role of inhibition in the generation of odor specific PN responses (Bazhenov et al., 2001a,b; Assisi et al., 2011). In large-scale AL network models we characterized the inhibitory sub-network by its coloring (Assisi et al., 2011) and showed that it can entrain excitatory PNs to the odor specific patterns of transient synchronization. In this focused review, we further examine the dynamics of entrainment in more detail by simulating simple model networks in various parameter regimes. Our simulations in conjunction with earlier studies point to the key role played by lateral (between inhibitory interneurons) and feedback (from inhibitory interneurons to principal cells) inhibition in the generation of experimentally observed patterns of transient synchrony.

Linking dynamics of the inhibitory network to the input structure

“…LNs also form inhibitory connections to each other, thus forming a network of the kind we considered in this study. We showed previously that the inhibitory connections between LNs determine pattern of sequential LN activation and also that this pattern determines synchronization properties of the postsynaptic excitatory PNs (Assisi et al 2011; Assisi and Bazhenov 2012). This study, however assumed identical input to all of the inhibitory neurons of the AL network.…”

“…This led to the escape of the post-synaptic neuron from inhibition. Hence, neither of the two neurons could suppress activity of its postsynaptic target permanently, which gave rise to an anti-phase bursting rhythm (Assisi and Bazhenov 2012) shown in the Fig. 2e.…”

“…2e (Assisi and Bazhenov 2012). After a period of activity, the strong adaptation current, I AHP , caused a significant decrease of the firing rate in the presynaptic neuron.…”

“…However, the total number of possible patterns and the exact form of phase-locking relations depend on the size and coupling structure of the network. In contrast to the previous works, we kept the network structure as simple as possible, mimicking densely connected networks of interneurons in the olfactory system of insects (Warren and Kloppenburg 2014; Assisi and Bazhenov 2012). Instead, we introduced asymmetry by means of the heterogeneous external currents applied to the neurons (Eq.…”

Learning pattern recognition and decision making in the insect brain

“…The PNs and the LNs connect to each other via the glomeruli. The glomeruli structure induces a bipartite graph of connections that contrasts to the standard directed Bernoulli-induced graphs typically used in AL models [60,61,62,63,64,65] with a few exceptions [66]. Moreover, the connections via the glomeruli may be complicated enough because they can be presynaptic [67,68].…”