Disruption of the US pre-exposure effect and latent inhibition in two-way active avoidance by systemic amphetamine in C57BL/6 mice

Rationale: Pre-exposure to either one of the two to-be-associated stimuli alone is known to reduce the efficiency of the learning of their association when they are subsequently paired explicitly. In classical conditioning, pre-exposure to the conditioned stimulus (CS) gives rise to latent inhibition (LI); and pre-exposure to the unconditioned stimulus (US) results in the US pre-exposure effect (USPEE). Considerable evidence supports an important role of central dopamine in the regulation and modulation of LI; it has been suggested that the USPEE may be similarly controlled by dopamine, but this parallelism has only been directly demonstrated in the conditioned taste aversion paradigm. Objective: The present study tested this hypothesis by comparing the efficacy of systemic amphetamine treatment to affect the expression of LI and the USPEE in a two-way active avoidance paradigm. Methods: C57BL/6 male mice were tested in active avoidance using a tone CS and a foot-shock US. Twenty-four hours before, they were pre-exposed to 100 presentations of the CS or the US, or to the test apparatus only. Amphetamine (2.5mg/kg) or saline was administered before stimulus pre-exposure and conditioned avoidance test, in which the mice learned to avoid the shock by shuttling in response to the tone. Results: Amphetamine disrupted both stimulus pre-exposure effects, thus, lending further support to the hypothesis that the USPEE is similar to LI in its sensitivity to dopamine receptor agonist. Hence, the USPEE paradigm may represent a valuable addition to the study of dopamine-sensitive processes of selective learning currently implicated in LI and Kamin blockin

Haloperidol and clozapine antagonise amphetamine-induced disruption of latent inhibition of conditioned taste aversion

Rationale: Latent inhibition (LI) describes a process by which repeated pre-exposure of a stimulus without any consequence retards the learning of subsequent conditioned associations with that stimulus. It is well established that LI is impaired in rats and in humans by injections of the indirect dopamine agonist amphetamine (AMPH), and that this disruption can be prevented by co-administration of either the typical neuroleptic haloperidol (HAL) or the atypical neuroleptic clozapine (CLZ). Objectives: Most of what is known of the pharmacology of LI is derived from studies using either the conditioned emotional response or the conditioned active avoidance paradigm. The goal of the present study was to determine whether these results would generalize to the conditioned taste aversion assay. Methods: We tested whether AMPH (0.5mg/kg) pretreatment would disrupt LI of a conditioned aversion to sucrose, and if so, which stage of the procedure is critical for mediating the disruption; in addition, we tested whether HAL (0.2mg/kg) or CLZ (5.0mg/kg) could restore such an expected LI disruption. Results: We determined that AMPH disrupted LI when it was injected before pre-exposure and prior to conditioning, but not if the rats were injected before either stage alone. When HAL or CLZ was given 40min before AMPH (before both pre-exposure and conditioning), it blocked LI disruption. Conclusion: These results are in line with the pharmacology of LI as derived from other conditioning paradigms. We conclude that the pharmacological regulation of LI in the CTA paradigm is similar to what has been observed previously in the conditioned emotional response and the conditioned active avoidance paradigm

Enabling visual analysis in wireless sensor networks

This demo showcases some of the results obtained by the GreenEyes project, whose main objective is to enable visual analysis on resource-constrained multimedia sensor networks. The demo features a multi-hop visual sensor network operated by BeagleBones Linux computers with IEEE 802.15.4 communication capabilities, and capable of recognizing and tracking objects according to two different visual paradigms. In the traditional compress-then-analyze (CTA) paradigm, JPEG compressed images are transmitted through the network from a camera node to a central controller, where the analysis takes place. In the alternative analyze-then-compress (ATC) paradigm, the camera node extracts and compresses local binary visual features from the acquired images (either locally or in a distributed fashion) and transmits them to the central controller, where they are used to perform object recognition/tracking. We show that, in a bandwidth constrained scenario, the latter paradigm allows to reach better results in terms of application frame rates, still ensuring excellent analysis performance

Coding local and global binary visual features extracted from video sequences

Binary local features represent an effective alternative to real-valued descriptors, leading to comparable results for many visual analysis tasks, while being characterized by significantly lower computational complexity and memory requirements. When dealing with large collections, a more compact representation based on global features is often preferred, which can be obtained from local features by means of, e.g., the Bag-of-Visual-Word (BoVW) model. Several applications, including for example visual sensor networks and mobile augmented reality, require visual features to be transmitted over a bandwidth-limited network, thus calling for coding techniques that aim at reducing the required bit budget, while attaining a target level of efficiency. In this paper we investigate a coding scheme tailored to both local and global binary features, which aims at exploiting both spatial and temporal redundancy by means of intra- and inter-frame coding. In this respect, the proposed coding scheme can be conveniently adopted to support the Analyze-Then-Compress (ATC) paradigm. That is, visual features are extracted from the acquired content, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast with the traditional approach, in which visual content is acquired at a node, compressed and then sent to a central unit for further processing, according to the Compress-Then-Analyze (CTA) paradigm. In this paper we experimentally compare ATC and CTA by means of rate-efficiency curves in the context of two different visual analysis tasks: homography estimation and content-based retrieval. Our results show that the novel ATC paradigm based on the proposed coding primitives can be competitive with CTA, especially in bandwidth limited scenarios.Comment: submitted to IEEE Transactions on Image Processin

Compress-then-analyze vs. analyze-then-compress: Two paradigms for image analysis in visual sensor networks

We compare two paradigms for image analysis in vi- sual sensor networks (VSN). In the compress-then-analyze (CTA) paradigm, images acquired from camera nodes are compressed and sent to a central controller for further analysis. Conversely, in the analyze-then-compress (ATC) approach, camera nodes perform visual feature extraction and transmit a compressed version of these features to a central controller. We focus on state-of-the-art binary features which are particularly suitable for resource-constrained VSNs, and we show that the ”winning” paradigm depends primarily on the network conditions. Indeed, while the ATC approach might be the only possible way to perform analysis at low available bitrates, the CTA approach reaches the best results when the available bandwidth enables the transmission of high-quality images

Baclofen alters gustatory discrimination capabilities and induces a conditioned taste aversion (CTA)

<p>Abstract</p> <p>Background</p> <p>Studies intending to measure drug-induced changes in learning and memory are challenged to parse out the effects of drugs on sensory, motor, and associative systems in the brain. In the context of conditioned taste aversion (CTA), drugs that alter the sensorium of subjects and affect their ability to taste and/or feel malaise may limit the ability of investigators to make conclusions about associative effects of these substances. Since the GABAergic system is implicated in inhibition, the authors were hopeful to use the GABA agonist, baclofen (BAC), to enhance extinction of a CTA, but first a preliminary evaluation of BAC's peripheral effects on animals' sensorium had to be completed due to a lack of published literature in this area.</p> <p>Findings</p> <p>Our first experiment aimed to evaluate the extent to which the GABA_Bagonist, BAC, altered the ability of rats to differentiate between 0.3% and 0.6% saccharin (SAC) in a two bottle preference test. Here we report that 2 or 3 mg/kg (i.p.) BAC, but not 1 mg/kg BAC, impaired animals' gustatory discrimination abilities in this task. Furthermore, when SAC consumption was preceded by 2 or 3 mg/kg (i.p.) BAC, rats depressed their subsequent SAC drinking.</p> <p>A second experiment evaluated if the suppression of SAC and water drinking (revealed in Experiment 1) was mediated by amnesiac effects of BAC or whether BAC possessed US properties in the context of the CTA paradigm. The time necessary to reach an asymptotic level of CTA extinction was not significantly different in those animals that received the 3 mg/kg dose of BAC compared to more conventionally SAC + lithium chloride (LiCl, 81 mg/kg) conditioned animals.</p> <p>Conclusions</p> <p>Our findings were not consistent with a simple amnesia-of-neophobia explanation. Instead, results indicated that 2 and 3 mg/kg (i.p.) BAC were capable of inducing a CTA, which was extinguishable via repeated presentations of SAC only. Our data indicate that, depending on the dose, BAC can alter SAC taste discrimination and act as a potent US in the context of a CTA paradigm.</p

Rate-energy-accuracy optimization of convolutional architectures for face recognition

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Face recognition systems based on Convolutional Neural Networks (CNNs) or convolutional architectures currently represent the state of the art, achieving an accuracy comparable to that of humans. Nonetheless, there are two issues that might hinder their adoption on distributed battery-operated devices (e.g., visual sensor nodes, smartphones, and wearable devices). First, convolutional architectures are usually computationally demanding, especially when the depth of the network is increased to maximize accuracy. Second, transmitting the output features produced by a CNN might require a bitrate higher than the one needed for coding the input image. Therefore, in this paper we address the problem of optimizing the energy-rate-accuracy characteristics of a convolutional architecture for face recognition. We carefully profile a CNN implementation on a Raspberry Pi device and optimize the structure of the neural network, achieving a 17-fold speedup without significantly affecting recognition accuracy. Moreover, we propose a coding architecture custom-tailored to features extracted by such model. (C) 2015 Elsevier Inc. All rights reserved.Face recognition systems based on Convolutional Neural Networks (CNNs) or convolutional architectures currently represent the state of the art, achieving an accuracy comparable to that of humans. Nonetheless, there are two issues that might hinder their a36142148CNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)sem informação2013/11359-0sem informaçã

A Visual Sensor Network for Parking Lot Occupancy Detection in Smart Cities

Technology is quickly revolutionizing our everyday lives, helping us to perform complex tasks. The Internet of Things (IoT) paradigm is getting more and more popular and is key to the development of Smart Cities. Among all the applications of IoT in the context of Smart Cities, real-time parking lot occupancy detection recently gained a lot of attention. Solutions based on computer vision yield good performance in terms of accuracy and are deployable on top of visual sensor networks. Since the problem of detecting vacant parking lots is usually distributed over multiple cameras, adhoc algorithms for content acquisition and transmission are to be devised. A traditional paradigm consists in acquiring and encoding images or videos and transmitting them to a central controller, which is responsible for analyzing such content. A novel paradigm, which moves part of the analysis to sensing devices, is quickly becoming popular. We propose a system for distributed parking lot occupancy detection based on the latter paradigm, showing that onboard analysis and transmission of simple features yield better performance with respect to the traditional paradigm in terms of the overall rate-energy-accuracy performance