Gestational protein restriction induces CA3 dendritic atrophy in dorsal hippocampal neurons but does not alter learning and memory performance in adult offspring

Studies have demonstrated that nutrient deficiency during pregnancy or in early postnatal life results in structural abnormalities in the offspring hippocampus and in cognitive impairment. In an attempt to analyze whether gestational protein restriction might induce learning and memory impairments associated with structural changes in the hippocampus, we carried out a detailed morphometric analysis of the hippocampus of male adult rats together with the behavioral characterization of these animals in the Morris water maze (MWM). Our results demonstrate that gestational protein restriction leads to a decrease in total basal dendritic length and in the number of intersections of CA3 pyramidal neurons whereas the cytoarchitecture of CA1 and dentate gyrus remained unchanged. Despite presenting significant structural rearrangements, we did not observe impairments in the MWM test. Considering the clear dissociation between the behavioral profile and the hippocampus neuronal changes, the functional significance of dendritic remodeling in fetal processing remains undisclosed. © 2012 ISDN.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

Ontogeny of manipulative behavior and nut-cracking in young tufted capuchin monkeys (Cebus apella): a Perception-action perspective

How do capuchin monkeys learn to use stones to crack open nuts? Perception-action theory posits that individuals explore producing varying spatial and force relations among objects and surfaces, thereby learning about affordances of such relations and how to produce them. Such learning supports the discovery of tool use. We present longitudinal developmental data from semifree-ranging tufted capuchin monkeys (Cebus apella) to evaluate predictions arising from Perception-action theory linking manipulative development and the onset of tool-using. Percussive actions bringing an object into contact with a surface appeared within the first year of life. Most infants readily struck nuts and other objects against stones or other surfaces from 6 months of age, but percussive actions alone were not sufficient to produce nut-cracking sequences. Placing the nut on the anvil surface and then releasing it, so that it could be struck with a stone, was the last element necessary for nut-cracking to appear in capuchins. Young chimpanzees may face a different challenge in learning to crack nuts: they readily place objects on surfaces and release them, but rarely vigorously strike objects against surfaces or other objects. Thus the challenges facing the two species in developing the same behavior (nut-cracking using a stone hammer and an anvil) may be quite different. Capuchins must inhibit a strong bias to hold nuts so that they can release them; chimpanzees must generate a percussive action rather than a gentle placing action. Generating the right actions may be as challenging as achieving the right sequence of actions in both species. Our analysis suggests a new direction for studies of social influence on young primates learning sequences of actions involving manipulation of objects in relation to surfaces.

OBSERVING BEHAVIOR AND ATYPICALLY RESTRICTED STIMULUS CONTROL

Restricted stimulus control refers to discrimination learning with atypical limitations in the range of controlling stimuli or stimulus features In the study reported here 4 normally capable individuals and 10 individuals with Intellectual disabilities (ID) performed two-sample delayed matching to sample Sample stimulus observing was recorded with an eye tracking apparatus High accuracy scores indicated stimulus control by both sample stimuli for the 4 nondisabled participants and 4 participants with ID and eye tracking data showed reliable observing of all stimuli Intermediate accuracy scores indicated restricted stimulus control for the remaining 6 participants Their eye tracking data showed that errors were related to failures to observe sample stimuli and relatively brief observing durations Five of these participants were then given interventions designed to improve observing behavior For 4 participants the interventions resulted initially in elimination of observing failures increased observing durations and Increased accuracy For 2 of these participants contingencies sufficient to maintain adequate observing were not always sufficient to maintain high accuracy subsequent procedure modifications restored It however For the 5th participant initial improvements in observing were not accompanied by improved accuracy in apparent Instance of observing without attending accuracy improved only after an additional intervention that imposed contingencies on observing behavior Thus interventions that control observing behavior seem necessary but may not always be sufficient for the remediation of restricted stimulus control

Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

Hypoactivity of the central dopaminergic system and autistic-like behavior induced by a single early prenatal exposure to lipopolysaccharide

The aim of the present study was to evaluate the behavioral patterns associated with autism and the prevalence of these behaviors in males and females, to verify whether our model of lipopolysaccharide (LPS) administration represents an experimental model of autism. For this, we prenatally exposed Wistar rats to LPS (100 mu g/kg, intraperitoneally, on gestational day 9.5), which mimics infection by gram-negative bacteria. Furthermore, because the exact mechanisms by which autism develops are still unknown, we investigated the neurological mechanisms that might underlie the behavioral alterations that were observed. Because we previously had demonstrated that prenatal LPS decreases striatal dopamine (DA) and metabolite levels, the striatal dopaminergic system (tyrosine hydroxylase [TH] and DA receptors D1a and D2) and glial cells (astrocytes and microglia) were analyzed by using immunohistochemistry, immunoblotting, and real-time PCR. Our results show that prenatal LPS exposure impaired communication (ultrasonic vocalizations) in male pups and learning and memory (T-maze spontaneous alternation) in male adults, as well as inducing repetitive/restricted behavior, but did not change social interactions in either infancy (play behavior) or adulthood in females. Moreover, although the expression of DA receptors was unchanged, the experimental animals exhibited reduced striatal TH levels, indicating that reduced DA synthesis impaired the striatal dopaminergic system. The expression of glial cell markers was not increased, which suggests that prenatal LPS did not induce permanent neuroinflammation in the striatum. Together with our previous finding of social impairments in males, the present findings demonstrate that prenatal LPS induced autism-like effects and also a hypoactivation of the dopaminergic system. (c) 2012 Wiley Periodicals, Inc.

Modelling shared attention through relational reinforcement learning

Shared attention is a type of communication very important among human beings. It is sometimes reserved for the more complex form of communication being constituted by a sequence of four steps: mutual gaze, gaze following, imperative pointing and declarative pointing. Some approaches have been proposed in Human-Robot Interaction area to solve part of shared attention process, that is, the most of works proposed try to solve the first two steps. Models based on temporal difference, neural networks, probabilistic and reinforcement learning are methods used in several works. In this article, we are presenting a robotic architecture that provides a robot or agent, the capacity of learning mutual gaze, gaze following and declarative pointing using a robotic head interacting with a caregiver. Three learning methods have been incorporated to this architecture and a comparison of their performance has been done to find the most adequate to be used in real experiment. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human in a controlled environment. The experimental results show that the robotic head is able to produce appropriate behavior and to learn from sociable interaction.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.

The Effects of Altered Prenatal Melatonin Signaling on Adult Behavior and Hippocampal Gene Expression of the Male Rat: A Circadioneuroendocrine-Axis Hypothesis of Psychopathology

Disturbances in melatonin - the neurohormone that signals environmental darkness as part of the circadian circuit of mammals - have been implicated in various psychopathologies in humans. At present, experimental evidence linking prenatal melatonin signaling to adult physiology, behavior, and gene expression is lacking. We hypothesized that administration of melatonin (5 mg/kg) or the melatonin receptor antagonist luzindole (5 mg/kg) to rats in utero would permanently alter the circadian circuit to produce differential growth, adult behavior, and hippocampal gene expressionin the male rat. Prenatal treatment was found to increase growth in melatonin-treated animals. In addition, subjects exposed to melatonin prenatally displayed increased rearing in the open field test and an increased right turn preference in the elevated plusmaze. Rats administered luzindole prenatally, however, displayed greater freezing and grooming behavior in the open field test and improved learning in the Morris water maze. Analysis of relative adult hippocampal gene expression with RT-PCR revealed increasedexpression of brain-derived neurotrophic factor (BDNF) with a trend toward increased expression of melatonin 1A (MEL1A) receptors in melatonin-exposed animals whereas overall prenatal treatment had a significant effect on microtubule-associated protein 2(MAP2) expression. Our data support the conclusion that the manipulation of maternal melatonin levels alters brain development and leads to physiological and behavioral abnormalities in adult offspring. We designate the term circadioneuroendocrine (CNE)axis and propose the CNE-axis hypothesis of psychopathology.

Dopamine-related deficit in reward learning after catecholamine depletion in unmedicated, remitted subjects with bulimia nervosa

Disturbances in reward processing have been implicated in bulimia nervosa (BN). Abnormalities in processing reward-related stimuli might be linked to dysfunctions of the catecholaminergic neurotransmitter system, but findings have been inconclusive. A powerful way to investigate the relationship between catecholaminergic function and behavior is to examine behavioral changes in response to experimental catecholamine depletion (CD). The purpose of this study was to uncover putative catecholaminergic dysfunction in remitted subjects with BN who performed a reinforcement-learning task after CD. CD was achieved by oral alpha-methyl-para-tyrosine (AMPT) in 19 unmedicated female subjects with remitted BN (rBN) and 28 demographically matched healthy female controls (HC). Sham depletion administered identical capsules containing diphenhydramine. The study design consisted of a randomized, double-blind, placebo-controlled crossover, single-site experimental trial. The main outcome measures were reward learning in a probabilistic reward task analyzed using signal-detection theory. Secondary outcome measures included self-report assessments, including the Eating Disorder Examination-Questionnaire. Relative to healthy controls, rBN subjects were characterized by blunted reward learning in the AMPT-but not in placebo-condition. Highlighting the specificity of these findings, groups did not differ in their ability to perceptually distinguish between stimuli. Increased CD-induced anhedonic (but not eating disorder) symptoms were associated with a reduced response bias toward a more frequently rewarded stimulus. In conclusion, under CD, rBN subjects showed reduced reward learning compared with healthy control subjects. These deficits uncover disturbance of the central reward processing systems in rBN related to altered brain catecholamine levels, which might reflect a trait-like deficit increasing vulnerability to BN.

Cognitive functioning, behavior, and quality of life after stroke in childhood

Rationale: To provide a better understanding of cognitive functioning, motor outcome, behavior and quality of life after childhood stroke and to study the relationship between variables expected to influence rehabilitation and outcome (age at stroke, time elapsed since stroke, lateralization, location and size of lesion). Methods: Children who suffered from stroke between birth and their eighteenth year of life underwent an assessment consisting of cognitive tests (WISC-III, WAIS-R, K-ABC, TAP, Rey-Figure, German Version of the CVLT) and questionnaires (Conner's Scales, KIDSCREEN). Results: Twenty-one patients after stroke in childhood (15 males, mean 11;11 years, SD 4;3, range 6;10-21;2) participated in the study. Mean Intelligence Quotients (IQ) were situated within the normal range (mean Full Scale IQ 96.5, range IQ 79-129). However, significantly more patients showed deficits in various cognitive domains than expected from a healthy population (Performance IQ p = .000; Digit Span p = .000, Arithmetic's p = .007, Divided Attention p = .028, Alertness p = .002). Verbal IQ was significantly better than Performance IQ in 13 of 17 patients, independent of the hemispheric side of lesion. Symptoms of ADHD occurred more often in the patients' sample than in a healthy population (learning difficulties/inattention p = .000; impulsivity/hyperactivity p = .006; psychosomatics p = .006). Certain aspects of quality of life were reduced (autonomy p = .003; parents' relation p = .003; social acceptance p = .037). Three patients had a right-sided hemiparesis, mean values of motor functions of the other patients were slightly impaired (sequential finger movements p = .000, hand alternation p = .001, foot tapping p = .043). In patients without hemiparesis, there was no relation between the lateralization of lesion and motor outcome. Lesion that occurred in the midst of childhood (5-10 years) led to better cognitive outcome than lesion in the very early (0-5 years) or late childhood (10-18 years). Other variables such as presence of seizure, elapsed time since stroke and size of lesion had a small to no impact on prognosis. Conclusion: Moderate cognitive and motor deficits, behavioral problems, and impairment in some aspects of quality of life frequently remain after stroke in childhood. Visuospatial functions are more often reduced than verbal functions, independent of the hemispheric side of lesion. This indicates a functional superiority of verbal skills compared to visuospatial skills in the process of recovery after brain injury. Compared to the cognitive outcome following stroke in adults, cognitive sequelae after childhood stroke do indicate neither the lateralization nor the location of the lesion focus. Age at stroke seems to be the only determining factor influencing cognitive outcome.

Learning flexible sensori-motor mappings in a complex network

Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex problem.