Timing Self-generated Actions for Sensory Streaming

Caputi, Ángel A.

doi:10.1007/978-3-642-33269-2_28

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…As a consequence, coincidence over successive EODs is reduced (Heiligenberg et al, 1978;Capurro et al, 1999). It has been shown that the same dynamic process can explain both synchronization bouts as well as jamming avoidance responses (Fig.7B) (Caputi, 2012). In fact, during jamming avoidance, accelerations (red line in Fig.7B) occur when the allogenerated stimulus falls just before the EOD, causing a change in the response to the fish's own EOD of the receptors that originate the slow electrosensory pathway (Baker, 1980;Baker, 1981).…”

Section: The Computational Task: the Tradeoff Between Self-and Allo-gmentioning

confidence: 87%

From the intrinsic properties to the functional role of a neuron phenotype: an example from electric fish during signal trade-off

Nogueira

Caputi

2013

Journal of Experimental Biology

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SummaryThis review deals with the question: what is the relationship between the properties of a neuron and the role that the neuron plays within a given neural circuit? Answering this kind of question requires collecting evidence from multiple neuron phenotypes and comparing the role of each type in circuits that perform well-defined computational tasks. The focus here is on the spherical neurons in the electrosensory lobe of the electric fish Gymnotus omarorum. They belong to the one-spike-onset phenotype expressed at the early stages of signal processing in various sensory modalities and diverse taxa. First, we refer to the one-spike neuron intrinsic properties, their foundation on a low-threshold K + conductance, and the potential roles of this phenotype in different circuits within a comparative framework. Second, we present a brief description of the active electric sense of weakly electric fish and the particularities of spherical one-spike-onset neurons in the electrosensory lobe of G. omarorum. Third, we introduce one of the specific tasks in which these neurons are involved: the trade-off between self-and allo-generated signals. Fourth, we discuss recent evidence indicating a still-undescribed role for the one-spike phenotype. This role deals with the blockage of the pathway after being activated by the self-generated electric organ discharge and how this blockage favors selfgenerated electrosensory information in the context of allo-generated interference. Based on comparative analysis we conclude that one-spike-onset neurons may play several functional roles in animal sensory behavior. There are specific adaptations of the neuronʼs ʻresponse functionʼ to the circuit and task. Conversely, the way in which a task is accomplished depends on the intrinsic properties of the neurons involved. In short, the role of a neuron within a circuit depends on the neuron and its functional context.

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Section: The Computational Task: the Tradeoff Between Self-and Allo-gmentioning

confidence: 87%

From the intrinsic properties to the functional role of a neuron phenotype: an example from electric fish during signal trade-off

Nogueira

Caputi

2013

Journal of Experimental Biology

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“…For the training of the autonomous vehicle driving model, we have used Adam optimizer that can change the learning rate dynamically (43). The mean square error based loss function, a dropout rate of 0.5 in the last four FC layers, and L2 regularization are used to reduce overfitting and underfitting and to minimize training error (44). We use model checkpoints to stop the training when the validation loss is not decreasing over time (45).…”

Section: Model Training and Validationmentioning

confidence: 99%

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

Islam

Chowdhury

et al. 2019

Transportation Research Record

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Vision-based navigation of autonomous vehicles primarily depends on the Deep Neural Network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems of the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adversarial inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicle by unexpected roadway hazards, such as debris and roadblocks. In this study, we first introduce a roadway hazardous environment (both intentional and unintentional roadway hazards) that can compromise the DNNbased navigational system of an autonomous vehicle, and produces an incorrect steering wheel angle, which can cause crashes resulting in fatality and injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazardous environment, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system including hazardous object detection and semantic segmentation improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared to the traditional DNN-based autonomous vehicle driving system.

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“…Agreeing with that, there is evidence that the fish adjusts its own signal to make the other one fire at a “preferential” phase [ 8 ], and this is probably related to dominance [ 3 ]. These effects can be observed in an integrate-and-fire model with non-linear inputs taking into account a phase preference for eliciting acceleration [ 9 ]. As the refractoriness of the fast electrosensory path is the most important jamming effect we call this effect “electrosensory refractoriness avoidance response” (RAR).…”

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confidence: 99%