Spatio-temporal activity patterns of odor-induced synchronized potentials revealed by voltage-sensitive dye imaging and intracellular recording in the antennal lobe of the cockroach

Watanabe, Hidehiro; Ai, Hiroyuki; Yokohari, Fumio

doi:10.3389/fnsys.2012.00055

Cited by 16 publications

(25 citation statements)

References 48 publications

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“…As an effect of cellular adaptation in ORNs and KCs, odor stimulation (Fig 2 A) results in temporally precise and adaptive responses across all layers of the network (Fig 2 B). The effect of SFA implemented in ORNs is transitive and thus carries over to the postsynaptic PN and LN populations in agreement with experimental observations across species (28)(29)(30)(31).…”

Section: Resultssupporting

confidence: 87%

A spiking neural program for sensory-motor control during foraging in flying insects

Rapp

Nawrot

2020

Preprint

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Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying neural circuits and computational principles we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning and motor control. We focus on cast & surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a spike-based plasticity rule the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. We show that, without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short time scales generates cast & surge motor commands. Our generic systems approach predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. Our work successfully combines biological computational principles with spike-based machine learning. It shows how knowledge transfer from static to arbitrary complex dynamic conditions can be achieved by foraging insects and may serve as inspiration for agent-based machine learning.

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Section: Resultssupporting

confidence: 87%

A spiking neural program for sensory-motor control during foraging in flying insects

Rapp

Nawrot

2020

Preprint

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“…As an effect of cellular adaptation in ORNs and KCs, odor stimulation (Fig 2 A) results in temporally precise and adaptive responses across all layers of the network (Fig 2 B). The effect of SFA implemented in ORNs is transitive and thus carries over to the postsynaptic PN and LN populations in agreement with experimental 90 observations across species (Stopfer et al, 2003;Wilson et al, 2004;Bhandawat et al, 2007;Krofczik et al, 2009;Watanabe et al, 2012). In the KC population the background firing rate is very low.…”

Section: Sparse Coding In Space and Timesupporting

confidence: 82%

Neural computation underlying rapid learning and dynamic memory recall for sensori-motor control in insects

Rapp

Nawrot

2020

Preprint

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Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying nervous systems and abstract computational principles we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning and motor control. We focus on cast & surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a synaptic plasticity rule the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. Without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short timescales generates cast & surge motor commands. Our systems approach is generic and predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. IntroductionNavigating towards a food source during foraging requires dynamical sensory processing, accumulation of sensory evidence and appropriate high level motor control. Navigation based on an animals' olfactory sense is a challenging task due to the complex spatiotemporal landscape of odor molecules. A core aspect of foraging is the acquisition of sensory cue samples in the natural environment where odor concentrations vary rapidly and steeply across space. Experimental 5 access to the neural substrate is challenging in freely behaving insects. Biologically realistic models thus play a key role in investigating the relevant computational mechanisms. Consequently, recent efforts at understanding foraging behavior have focused on identifying viable computational strategies for making navigation decisions (Vergassola et al.

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“…Indeed, using simultaneous dual-electrode intracellular recordings, Lei et al ( 2002 ) showed inter- and intra-glomerular spike synchrony among PNs in response to pheromone blend stimulation. Odor-induced interglomerular synchrony in the AL was also reported in cockroaches using voltage-sensitive-dye imaging methods, suggesting that the synchrony code operates at a broad spatial scale (Watanabe, 2012 ). Moreover, experiments that simultaneously recorded neuronal activity across the glomerular array in M. sexta showed that neurons with the most similar odor response profiles produced the highest degree of coincident spikes (Lei et al, 2004 ).…”

Section: Olfactory Attraction For Monitoring and Trappingmentioning

confidence: 75%

Neuroethology of Olfactory-Guided Behavior and Its Potential Application in the Control of Harmful Insects

2016

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Harmful insects include pests of crops and storage goods, and vectors of human and animal diseases. Throughout their history, humans have been fighting them using diverse methods. The fairly recent development of synthetic chemical insecticides promised efficient crop and health protection at a relatively low cost. However, the negative effects of those insecticides on human health and the environment, as well as the development of insect resistance, have been fueling the search for alternative control tools. New and promising alternative methods to fight harmful insects include the manipulation of their behavior using synthetic versions of “semiochemicals”, which are natural volatile and non-volatile substances involved in the intra- and/or inter-specific communication between organisms. Synthetic semiochemicals can be used as trap baits to monitor the presence of insects, so that insecticide spraying can be planned rationally (i.e., only when and where insects are actually present). Other methods that use semiochemicals include insect annihilation by mass trapping, attract-and- kill techniques, behavioral disruption, and the use of repellents. In the last decades many investigations focused on the neural bases of insect's responses to semiochemicals. Those studies help understand how the olfactory system detects and processes information about odors, which could lead to the design of efficient control tools, including odor baits, repellents or ways to confound insects. Here we review our current knowledge about the neural mechanisms controlling olfactory responses to semiochemicals in harmful insects. We also discuss how this neuroethology approach can be used to design or improve pest/vector management strategies.

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Spatio-temporal activity patterns of odor-induced synchronized potentials revealed by voltage-sensitive dye imaging and intracellular recording in the antennal lobe of the cockroach

Cited by 16 publications

References 48 publications

A spiking neural program for sensory-motor control during foraging in flying insects

A spiking neural program for sensory-motor control during foraging in flying insects

Neural computation underlying rapid learning and dynamic memory recall for sensori-motor control in insects

Neuroethology of Olfactory-Guided Behavior and Its Potential Application in the Control of Harmful Insects

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