Computational Models of Olfaction in Fruit Flies

Gupta, Ankur; Faghihi, Faramarz; Moustafa, Ahmed A.

doi:10.1002/9781119159193.ch15

Cited by 3 publications

(5 citation statements)

References 72 publications

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“…Our trial-based model approach does not accommodate explicit time scales that would allow to differentiate between simultaneous, delay and trace conditioning ( Dylla et al, 2013 ), memory acquisition and consolidation ( Felsenberg et al, 2017 ) or decay ( Shuai et al, 2015 ). Future extensions may retain full temporal dynamics, e.g., by using spiking neural network models that have previously been used successfully to study classical conditioning in fruit flies ( Smith et al, 2008 ; Wessnitzer et al, 2012 ; Faghihi et al, 2017 ; Gupta et al, 2018 ; Rapp and Nawrot, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

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A Mechanistic Model for Reward Prediction and Extinction Learning in the Fruit Fly

Springer

Nawrot

2021

eNeuro

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Section: Discussionmentioning

confidence: 99%

“…by using spiking neural network models that have previously been used successfully to study classical conditioning in fruit flies (e.g. Faghihi, Moustafa, Heinrich, & Wörgötter, 2017;Gupta, Faghihi, & Moustafa, 2018;Rapp & Nawrot, 2020;D. Smith, Wessnitzer, & Webb, 2008;Wessnitzer, Young, Armstrong, & Webb, 2012).…”

Section: Limitations Of the Modelmentioning

confidence: 99%

A Mechanistic Model for Reward Prediction and Extinction Learning in the Fruit Fly

Springer

Nawrot

2021

eNeuro

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“…These models should, therefore, be intended as phenomenological rather than structural, and as mesoscopic rather than microscopic representations. At the same time, an important limitation of the present work which should be acknowledged is the fact that no attempt was made to decode the complex association between the type of odor presented and the spatiotemporal distribution of activation over the glomeruli and somata [6]- [10]. Pooling all the data and resorting to a fundamental and linear tool such as Granger causality to establish the correspondence between the glomeruli and the somata may be viewed as an abdication from answering this question.…”

Section: E the Scope For Transfer Functionsmentioning

confidence: 97%

Transfer Function-Based Characterization of the Honey Bee Olfactory System: From Biology to Electronic Circuits

et al. 2022

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We exemplify an interdisciplinary approach wherein a mesoscopic-scale functional model of a biological system is derived from time-series recordings, yielding transfer functions that can be used to design analog electronic circuits. Namely, sensory processing in the honey bee, a universal model for studying olfaction, is considered. Existing studies have focused on its antennal lobe, wherein only the responses of its functional units, known as glomeruli, have been accessible. Here, high temporal resolution calcium imaging is deployed to track the dynamics of odor-evoked activity beyond this processing stage. The responses in the somata outside of the antennal lobe are recorded, showing for the first time how the glomerular signals are transformed before entering the higher brain centers. A transfer function approach is applied to capture as a "gray box model" the remarkably heterogeneous signal transformations between odor input and glomerular response, and between glomerular signals and somata activity. The somata are tentatively mapped to the glomeruli via Granger causality, while machine learning classification and clustering allow grouping common properties regarding response amplitudes and temporal profiles. The obtained low-order transfer functions display time-and frequency-domain input-output properties closely similar to the biological system. Because transfer functions have universal applicability, once they have been determined, it is readily possible to design corresponding analog electronic circuits, with possible future applications in sensor signal conditioning. To exemplify this, examples based on resistor-capacitor (RC) networks and operational amplifiers are physically built and confirmed to generate responses highly correlated to the initial biological recordings.

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“…A minimalistic model of such circuitry has been proposed recently [74] to account for classical appetitive and aversive conditioning with memory extinction. This model is tailored to existing anatomical data, with two circuits of critical importance that exploit highly plastic synaptic connections between principal neurons (PN), functionally identified Kenyon cells (K), essential for olfactory learning and memory facilitated by dopamine-driven plasticity [72][73][74][75] of their signaling in response to odors, and functionally identified output neurons (ON) in separate and mutually inhibiting reward (attraction) and punishment (repulsion) learning pathways. Neuromodulation through recurrent network connections and the plasticity thereof permit implementing a simple mechanism that generates testable predictions in the temporal domain for the rapid encoding of associations of the conditioned stimulus with a reward or a punishment in single-trial learning (Figure 4).…”

Section: Adaptation To the Unexpectedmentioning

confidence: 99%

“…Such would include the ability to choose an alternative trajectory when the programmed one presents an unexpected obstacle, for example. Data from conditioning experiments suggest that two parallel but opposing memory traces coexist in the functional neural network architectures of biological reinforcement and extinction learning [70][71][72][73][74][75][76]. A minimalistic model of such circuitry has been proposed recently [74] to account for classical appetitive and aversive conditioning with memory extinction.…”

Section: Adaptation To the Unexpectedmentioning

confidence: 99%

From Biological Synapses to “Intelligent” Robots

Dresp-Langley

2022

Electronics

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This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward. Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems.

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Computational Models of Olfaction in Fruit Flies

Cited by 3 publications

References 72 publications

A Mechanistic Model for Reward Prediction and Extinction Learning in the Fruit Fly

A Mechanistic Model for Reward Prediction and Extinction Learning in the Fruit Fly

Transfer Function-Based Characterization of the Honey Bee Olfactory System: From Biology to Electronic Circuits

From Biological Synapses to “Intelligent” Robots

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