Dataset Information

A large-scale model of the locust antennal lobe.

ABSTRACT: The antennal lobe (AL) is the primary structure within the locust's brain that receives information from olfactory receptor neurons (ORNs) within the antennae. Different odors activate distinct subsets of ORNs, implying that neuronal signals at the level of the antennae encode odors combinatorially. Within the AL, however, different odors produce signals with long-lasting dynamic transients carried by overlapping neural ensembles, suggesting a more complex coding scheme. In this work we use a large-scale point neuron model of the locust AL to investigate this shift in stimulus encoding and potential consequences for odor discrimination. Consistent with experiment, our model produces stimulus-sensitive, dynamically evolving populations of active AL neurons. Our model relies critically on the persistence time-scale associated with ORN input to the AL, sparse connectivity among projection neurons, and a synaptic slow inhibitory mechanism. Collectively, these architectural features can generate network odor representations of considerably higher dimension than would be generated by a direct feed-forward representation of stimulus space.

SUBMITTER: Patel M

PROVIDER: S-EPMC4070380 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

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Publications

A large-scale model of the locust antennal lobe.

Patel Mainak M Rangan Aaditya V AV Cai David D

Journal of computational neuroscience 20090623 3

The antennal lobe (AL) is the primary structure within the locust's brain that receives information from olfactory receptor neurons (ORNs) within the antennae. Different odors activate distinct subsets of ORNs, implying that neuronal signals at the level of the antennae encode odors combinatorially. Within the AL, however, different odors produce signals with long-lasting dynamic transients carried by overlapping neural ensembles, suggesting a more complex coding scheme. In this work we use a la ...[more]

PMID: 19548077

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Project description:Time is considered to be an important encoding dimension in olfaction, as neural populations generate odour-specific spatiotemporal responses to constant stimuli. However, during pheromone mediated anemotactic search insects must discriminate specific ratios of blend components from rapidly time varying input. The dynamics intrinsic to olfactory processing and those of naturalistic stimuli can therefore potentially collide, thereby confounding ratiometric information. In this paper we use a computational model of the macroglomerular complex of the insect antennal lobe to study the impact on ratiometric information of this potential collision between network and stimulus dynamics. We show that the model exhibits two different dynamical regimes depending upon the connectivity pattern between inhibitory interneurons (that we refer to as fixed point attractor and limit cycle attractor), which both generate ratio-specific trajectories in the projection neuron output population that are reminiscent of temporal patterning and periodic hyperpolarisation observed in olfactory antennal lobe neurons. We compare the performance of the two corresponding population codes for reporting ratiometric blend information to higher centres of the insect brain. Our key finding is that whilst the dynamically rich limit cycle attractor spatiotemporal code is faster and more efficient in transmitting blend information under certain conditions it is also more prone to interference between network and stimulus dynamics, thus degrading ratiometric information under naturalistic input conditions. Our results suggest that rich intrinsically generated network dynamics can provide a powerful means of encoding multidimensional stimuli with high accuracy and efficiency, but only when isolated from stimulus dynamics. This interference between temporal dynamics of the stimulus and temporal patterns of neural activity constitutes a real challenge that must be successfully solved by the nervous system when faced with naturalistic input.

Dataset Information

A large-scale model of the locust antennal lobe.

Publications

A large-scale model of the locust antennal lobe.

Similar Datasets

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