Segregation of Unknown Odors From Mixtures Based on Stimulus Onset Asynchrony in Honey Bees

Sehdev, Aarti; Szyszka, Paul

doi:10.3389/fnbeh.2019.00155

Cited by 16 publications

(15 citation statements)

References 72 publications

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“…4A). The use of nonoverlapping cues follows the rationale that, in nature, filaments originating from different odors do not mix perfectly (53). The objective in this memory-recall task is to correctly detect the occurrences of the positively valenced odor (CS+) by means of a single MBON action potential as model output, while no output should be generated for all other cues (CS− or distractor odors).…”

Section: Robust Dynamic Memory Recall and Odor-background Segregationmentioning

confidence: 99%

“…3 implicitly addresses the issue of odor-background segregation. This refers to the problem that, in nature, cues of multiple odors of different sources are present, either in terms of mixtures or stimulus-onset asynchrony due to turbulent conditions (53,54). For behavior, it is relevant to reliably isolate and detect the relevant cues from any background or distractor cues.…”

Section: Odor-background Segregation: a Joint Effect Of Temporal And mentioning

confidence: 99%

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A spiking neural program for sensorimotor control during foraging in flying insects

Rapp

Nawrot

2020

Proc. Natl. Acad. Sci. U.S.A.

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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 and 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-and-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: Robust Dynamic Memory Recall and Odor-background Segregationmentioning

confidence: 99%

Section: Odor-background Segregation: a Joint Effect Of Temporal And mentioning

confidence: 99%

A spiking neural program for sensorimotor control during foraging in flying insects

Rapp

Nawrot

2020

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…3 implicitly addresses the issue of odor-background segregation. This refers to the problem that in nature cues of multiple odors of different sources are present, either in terms of mixtures or stimulus onset asynchrony due to turbulent conditions (50,51). For behavior it is relevant to reliably isolate and detect the relevant cues from any background or distractor cues.…”

Section: Discussionmentioning

confidence: 99%

“…4 A). The use of non-overlapping cues follows the rationale that, in nature, filaments originating from different odors do not mix perfectly (50).…”

Section: Robust Dynamic Memory Recall and Odor-background Segre-mentioning

confidence: 99%

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

Rapp

Nawrot

2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…Unsupervised algorithms(Hop eld 1991) can use differences in the temporal pro le between the background odor and target odor to segment them. These differences could also be onset delays (Sehdev and Szyszka 2019) between the target and the background (Verhagen et al 2007). These algorithms do not require previous knowledge of the odors and are intrinsically able to handle novel background odors, but they require a temporal gap between the presentation of the background and the target odors.…”

Section: Introductionmentioning

confidence: 99%

Odor identification in novel olfactory environments is selectively impaired in a mouse model of autism

Li¹,

Swerdloff²,

She³

et al. 2020

Preprint

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Aversion to novel stimuli in autism affects quality of life. We developed a behavioral paradigm to study the effect of novel background odors on odor discrimination in mouse models of autism. We trained wild type mice to discriminate target odors in known background odors. When tested, mice could discriminate known targets in novel background odors, a task similar to the visual CAPTCHA used to distinguish humans from computers. Using glomerular imaging data, we showed that WT mice used an algorithm that required less training data than a linear classifier or nearest neighbor classifier. The Cntnap2−/− mouse model of autism matched wild type mice performance in the presence of known backgrounds, but performance fell almost to chance levels in the presence of novel backgrounds. Wild-type mice use a robust algorithm for detecting odors in novel environments and this computation is selectively affected in a mouse model of autism.

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Segregation of Unknown Odors From Mixtures Based on Stimulus Onset Asynchrony in Honey Bees

Cited by 16 publications

References 72 publications

A spiking neural program for sensorimotor control during foraging in flying insects

A spiking neural program for sensorimotor control during foraging in flying insects

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

Odor identification in novel olfactory environments is selectively impaired in a mouse model of autism

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