Non-linear blend coding in the moth antennal lobe emerges from random glomerular networks

Abstract

Neural responses to odor blends often exhibit non-linear interactions to blend components. The first olfactory processing center in insects, the antennal lobe (AL), exhibits a complex network connectivity. We attempt to determine if non-linear blend interactions can arise purely as a function of the AL network connectivity itself, without necessitating additional factors such as competitive ligand binding at the periphery or intrinsic cellular properties. To assess this, we compared blend interactions among responses from single neurons recorded intracellularly in the AL of the moth Manduca sexta with those generated using a population-based computational model constructed from the morphologically based connectivity pattern of projection neurons (PNs) and local interneurons (LNs) with randomized connection probabilities from which we excluded detailed intrinsic neuronal properties. The model accurately predicted most of the proportions of blend interaction types observed in the physiological data. Our simulations also indicate that input from LNs is important in establishing both the type of blend interaction and the nature of the neuronal response (excitation or inhibition) exhibited by AL neurons. For LNs, the only input that significantly impacted the blend interaction type was received from other LNs, while for PNs the input from olfactory sensory neurons and other PNs contributed agonistically with the LN input to shape the AL output. Our results demonstrate that non-linear blend interactions can be a natural consequence of AL connectivity, and highlight the importance of lateral inhibition as a key feature of blend coding to be addressed in future experimental and computational studies.

Circuit and Cellular Mechanisms Facilitate the Transformation from Dense to Sparse Coding in the Insect Olfactory System

“…Lateral inhibition in the AL is mediated by inhibitory LNs (Wilson, 2013). It is crucial for establishing the population code at the level of PNs (Wilson et al, 2004;Krofczik et al, 2009;Olsen et al, 2010), for gain control (Stopfer et al, 2003;Olsen and Wilson, 2008), for decorrelation of odor representations (Wilson and Laurent, 2005), and for mixture interactions (Krofczik et al, 2009;Deisig et al, 2010;Capurro et al, 2012).…”

Rapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex

“…Existing computational models of the insect AL processing can be separated into two distinct categories: AL processing of general odors (Getz and Lutz, 1999; Bazhenov et al, 2001a,b; Martinez and Montejo, 2008; Capurro et al, 2012) that emphasize highly dimensional olfactory input required for general odor processing, through to computational models focusing on pheromone processing with relatively few stimulus dimensions (Linster et al, 1993, 1994; Av-Ron and Rospars, 1995; Av-Ron and Vibert, 1996; Linster and Dreyfus, 1996; Zavada et al, 2011; Chong et al, 2012). Early pheromone processing models by Linster and co-workers emphasized the role of oscillations arising between the interaction of LN and PN neurons.…”

Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

“…We model the network by three vectors $\overrightarrow{x},\overrightarrow{y},$ and $\overrightarrow{z}$, where each element in the vector represents a neuron and modeled by a firing-rate unit (Linster et al, 2005; Capurro et al, 2012; Chong et al, 2012). The three vectors correspond to the three anatomical groups RNs, PNs, and LNs, respectively:…”