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Dog Training

What training approaches produce faster skill acquisition and better retention — reward timing, session length, clicker vs verbal, frequency.

Strong consensus

Operant conditioning is one of the best-replicated findings in behavioral science. Immediate reward timing and positive reinforcement consistently outperform aversive methods for both acquisition speed and retention.

Evidence base

Min quality:

50 papers

Meta-analysisHigh evidence score

Intervention Programs Targeting the Mental Health, Professional Burnout, and/or Wellbeing of School Teachers: Systematic Review and Meta-Analyses

Joanne R. Beames, Samantha Spanos, Anna Roberts +5 more · Educational Psychology Review · 2023 · 73 citations

Abstract This paper outlines a systematic review and meta-analyses to identify, describe, and evaluate randomised and non-randomised controlled trials of psychological programs targeting the mental health, professional burnout, and/or wellbeing of school classroom teachers. Eighty-eight unique studies were identified for inclusion in the review, and of those 46 were included in the meta-analyses (23 randomised controlled trials). In randomised controlled trials, the programs examined had large effects on stress ( g =0.93), and moderate effects on anxiety ( g =0.65), depression ( g =0.51), professional burnout ( g= 0.57), and wellbeing ( g =0.56) at post. In non-randomised controlled trials, programs had moderate effects on stress ( g= 0.50), and small effects on anxiety ( g =0.38) and wellbeing ( g =0.38) at post. Studies were heterogeneous in design and methodological quality was generally poor, particularly in non-randomised controlled trials. There was an inadequate number of comparisons to perform sub-group analyses, meta-regression, or publication bias analyses. Most of the programs examined required significant time, effort, and resources to deliver and complete. These programs may not translate well outside of research trials to real-world contexts due to teachers being time-poor. Priorities for research include using methodologically rigorous designs, developing programs for teachers with teachers (i.e. co-design), and considering implementation factors to ensure feasibility, acceptability, and uptake. Systematic Review Registration Number: PROPSERO - CRD42020159805.

Meta-analysisLeading journalHigh evidence score

Quantifying Individual Variation in the Propensity to Attribute Incentive Salience to Reward Cues

Paul Meyer, Vedran Lovic, Benjamin T. Saunders +4 more · PLoS ONE · 2012 · 319 citations

If reward-associated cues acquire the properties of incentive stimuli they can come to powerfully control behavior, and potentially promote maladaptive behavior. Pavlovian incentive stimuli are defined as stimuli that have three fundamental properties: they are attractive, they are themselves desired, and they can spur instrumental actions. We have found, however, that there is considerable individual variation in the extent to which animals attribute Pavlovian incentive motivational properties ("incentive salience") to reward cues. The purpose of this paper was to develop criteria for identifying and classifying individuals based on their propensity to attribute incentive salience to reward cues. To do this, we conducted a meta-analysis of a large sample of rats (N = 1,878) subjected to a classic Pavlovian conditioning procedure. We then used the propensity of animals to approach a cue predictive of reward (one index of the extent to which the cue was attributed with incentive salience), to characterize two behavioral phenotypes in this population: animals that approached the cue ("sign-trackers") vs. others that approached the location of reward delivery ("goal-trackers"). This variation in Pavlovian approach behavior predicted other behavioral indices of the propensity to attribute incentive salience to reward cues. Thus, the procedures reported here should be useful for making comparisons across studies and for assessing individual variation in incentive salience attribution in small samples of the population, or even for classifying single animals.

StudyModerate

Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0

Nathalie Percie du Sert, Amrita Ahluwalia, Sabina Alam +25 more · PLoS Biology · 2020 · 2,747 citations

Improving the reproducibility of biomedical research is a major challenge. Transparent and accurate reporting is vital to this process; it allows readers to assess the reliability of the findings and repeat or build upon the work of other researchers. The ARRIVE guidelines (Animal Research: Reporting In Vivo Experiments) were developed in 2010 to help authors and journals identify the minimum information necessary to report in publications describing in vivo experiments. Despite widespread endorsement by the scientific community, the impact of ARRIVE on the transparency of reporting in animal research publications has been limited. We have revised the ARRIVE guidelines to update them and facilitate their use in practice. The revised guidelines are published alongside this paper. This explanation and elaboration document was developed as part of the revision. It provides further information about each of the 21 items in ARRIVE 2.0, including the rationale and supporting evidence for their inclusion in the guidelines, elaboration of details to report, and examples of good reporting from the published literature. This document also covers advice and best practice in the design and conduct of animal studies to support researchers in improving standards from the start of the experimental design process through to publication.

StudyModerate

Guidelines for the welfare and use of animals in cancer research

Paul Workman, Eric O. Aboagye, Frances R. Balkwill +14 more · British Journal of Cancer · 2010 · 1,435 citations

Animal experiments remain essential to understand the fundamental mechanisms underpinning malignancy and to discover improved methods to prevent, diagnose and treat cancer. Excellent standards of animal care are fully consistent with the conduct of high quality cancer research. Here we provide updated guidelines on the welfare and use of animals in cancer research. All experiments should incorporate the 3Rs: replacement, reduction and refinement. Focusing on animal welfare, we present recommendations on all aspects of cancer research, including: study design, statistics and pilot studies; choice of tumour models (e.g., genetically engineered, orthotopic and metastatic); therapy (including drugs and radiation); imaging (covering techniques, anaesthesia and restraint); humane endpoints (including tumour burden and site); and publication of best practice.

StudyTop journalModerate

Dual microglia effects on blood brain barrier permeability induced by systemic inflammation

Koichiro Haruwaka, Ako Ikegami, Yoshihisa Tachibana +10 more · Nature Communications · 2019 · 929 citations

Microglia survey brain parenchyma, responding to injury and infections. Microglia also respond to systemic disease, but the role of blood-brain barrier (BBB) integrity in this process remains unclear. Using simultaneous in vivo imaging, we demonstrated that systemic inflammation induces CCR5-dependent migration of brain resident microglia to the cerebral vasculature. Vessel-associated microglia initially maintain BBB integrity via expression of the tight-junction protein Claudin-5 and make physical contact with endothelial cells. During sustained inflammation, microglia phagocytose astrocytic end-feet and impair BBB function. Our results show microglia play a dual role in maintaining BBB integrity with implications for elucidating how systemic immune-activation impacts neural functions.

StudyModerate

Music listening enhances cognitive recovery and mood after middle cerebral artery stroke

Teppo Särkämö, Mari Tervaniemi, S. Laitinen +9 more · Brain · 2008 · 886 citations

We know from animal studies that a stimulating and enriched environment can enhance recovery after stroke, but little is known about the effects of an enriched sound environment on recovery from neural damage in humans. In humans, music listening activates a wide-spread bilateral network of brain regions related to attention, semantic processing, memory, motor functions, and emotional processing. Music exposure also enhances emotional and cognitive functioning in healthy subjects and in various clinical patient groups. The potential role of music in neurological rehabilitation, however, has not been systematically investigated. This single-blind, randomized, and controlled trial was designed to determine whether everyday music listening can facilitate the recovery of cognitive functions and mood after stroke. In the acute recovery phase, 60 patients with a left or right hemisphere middle cerebral artery (MCA) stroke were randomly assigned to a music group, a language group, or a control group. During the following two months, the music and language groups listened daily to self-selected music or audio books, respectively, while the control group received no listening material. In addition, all patients received standard medical care and rehabilitation. All patients underwent an extensive neuropsychological assessment, which included a wide range of cognitive tests as well as mood and quality of life questionnaires, one week (baseline), 3 months, and 6 months after the stroke. Fifty-four patients completed the study. Results showed that recovery in the domains of verbal memory and focused attention improved significantly more in the music group than in the language and control groups. The music group also experienced less depressed and confused mood than the control group. These findings demonstrate for the first time that music listening during the early post-stroke stage can enhance cognitive recovery and prevent negative mood. The neural mechanisms potentially underlying these effects are discussed.

StudyModerate

Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

Mike Davies, Andreas Wild, Garrick Orchard +5 more · Proceedings of the IEEE · 2021 · 607 citations

Deep artificial neural networks apply principles of the brain's information processing that led to breakthroughs in machine learning spanning many problem domains. Neuromorphic computing aims to take this a step further to chips more directly inspired by the form and function of biological neural circuits, so they can process new knowledge, adapt, behave, and learn in real time at low power levels. Despite several decades of research, until recently, very few published results have shown that today's neuromorphic chips can demonstrate quantitative computational value. This is now changing with the advent of Intel's Loihi, a neuromorphic research processor designed to support a broad range of spiking neural networks with sufficient scale, performance, and features to deliver competitive results compared to state-of-the-art contemporary computing architectures. This survey reviews results that are obtained to date with Loihi across the major algorithmic domains under study, including deep learning approaches and novel approaches that aim to more directly harness the key features of spike-based neuromorphic hardware. While conventional feedforward deep neural networks show modest if any benefit on Loihi, more brain-inspired networks using recurrence, precise spike-timing relationships, synaptic plasticity, stochasticity, and sparsity perform certain computation with orders of magnitude lower latency and energy compared to state-of-the-art conventional approaches. These compelling neuromorphic networks solve a diverse range of problems representative of brain-like computation, such as event-based data processing, adaptive control, constrained optimization, sparse feature regression, and graph search.

StudyTop journalModerate

Norepinephrine ignites local hotspots of neuronal excitation: How arousal amplifies selectivity in perception and memory

Mara Mather, David Clewett, Michiko Sakaki +1 more · Behavioral and Brain Sciences · 2015 · 706 citations

Emotional arousal enhances perception and memory of high-priority information but impairs processing of other information. Here, we propose that, under arousal, local glutamate levels signal the current strength of a representation and interact with norepinephrine (NE) to enhance high priority representations and out-compete or suppress lower priority representations. In our "glutamate amplifies noradrenergic effects" (GANE) model, high glutamate at the site of prioritized representations increases local NE release from the locus coeruleus (LC) to generate "NE hotspots." At these NE hotspots, local glutamate and NE release are mutually enhancing and amplify activation of prioritized representations. In contrast, arousal-induced LC activity inhibits less active representations via two mechanisms: 1) Where there are hotspots, lateral inhibition is amplified; 2) Where no hotspots emerge, NE levels are only high enough to activate low-threshold inhibitory adrenoreceptors. Thus, LC activation promotes a few hotspots of excitation in the context of widespread suppression, enhancing high priority representations while suppressing the rest. Hotspots also help synchronize oscillations across neural ensembles transmitting high-priority information. Furthermore, brain structures that detect stimulus priority interact with phasic NE release to preferentially route such information through large-scale functional brain networks. A surge of NE before, during, or after encoding enhances synaptic plasticity at NE hotspots, triggering local protein synthesis processes that enhance selective memory consolidation. Together, these noradrenergic mechanisms promote selective attention and memory under arousal. GANE not only reconciles apparently contradictory findings in the emotion-cognition literature but also extends previous influential theories of LC neuromodulation by proposing specific mechanisms for how LC-NE activity increases neural gain.

ObservationalLeading journalModerate

A Critical Evaluation of the Biological Construct Skeletal Muscle Hypertrophy: Size Matters but So Does the Measurement

Cody T. Haun, Christopher G. Vann, Brandon M. Roberts +3 more · Frontiers in Physiology · 2019 · 181 citations

Skeletal muscle is highly adaptable and has consistently been shown to morphologically respond to exercise training. Skeletal muscle growth during periods of resistance training has traditionally been referred to as skeletal muscle hypertrophy, and this manifests as increases in muscle mass, muscle thickness, muscle area, muscle volume, and muscle fiber cross-sectional area (fCSA). Delicate electron microscopy and biochemical techniques have also been used to demonstrate that resistance exercise promotes ultrastructural adaptations within muscle fibers. Decades of research in this area of exercise physiology have promulgated a widespread hypothetical model of training-induced skeletal muscle hypertrophy; specifically, fCSA increases are accompanied by proportional increases in myofibrillar protein, leading to an expansion in the number of sarcomeres in parallel and/or an increase in myofibril number. However, there is ample evidence to suggest that myofibrillar protein concentration may be diluted through sarcoplasmic expansion as fCSA increases occur. Furthermore, and perhaps more problematic, are numerous investigations reporting that pre-to-post training change scores in macroscopic, microscopic, and molecular variables supporting this model are often poorly associated with one another. The current review first provides a brief description of skeletal muscle composition and structure. We then provide a historical overview of muscle hypertrophy assessment. Next, current-day methods commonly used to assess skeletal muscle hypertrophy at the biochemical, ultramicroscopic, microscopic, macroscopic, and whole-body levels in response to training are examined. Data from our laboratory, and others, demonstrating correlations (or the lack thereof) between these variables are also presented, and reasons for comparative discrepancies are discussed with particular attention directed to studies reporting ultrastructural and muscle protein concentration alterations. Finally, we critically evaluate the biological construct of skeletal muscle hypertrophy, propose potential operational definitions, and provide suggestions for consideration in hopes of guiding future research in this area.

StudyModerate

A practical guide to multi-objective reinforcement learning and planning

Conor F. Hayes, Roxana Rădulescu, Eugenio Bargiacchi +15 more · VUBIR (Vrije Universiteit Brussel) · 2022 · 320 citations

Real-world sequential decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems.

StudyModerate

Anomalies: Intertemporal Choice

George Loewenstein, Richard H. Thaler · The Journal of Economic Perspectives · 1989 · 870 citations

We examine a number of situations in which people do not appear to discount money flows at the market rate of interest or any other single discount rate. Discount rates observed in both laboratory and field decision-making environments are shown to depend on the magnitude and sign of what is being discounted, on the time delay, on whether the choice is cast in terms of speed-up or delay, on the way in which a choice is framed, and on whether future benefits or costs induce savoring or dread.

RCTHigh evidence score

Weight gain prevention in young adults: design of the study of novel approaches to weight gain prevention (SNAP) randomized controlled trial

Rena R. Wing, Deborah F. Tate, Mark A. Espeland +7 more · BMC Public Health · 2013 · 66 citations

BACKGROUND: Weight gain during young adulthood is common and is associated with increased cardiovascular risk. Preventing this weight gain from occurring may be critical to improving long-term health. Few studies have focused on weight gain prevention, and these studies have had limited success. SNAP (Study of Novel Approaches to Weight Gain Prevention) is an NIH-funded randomized clinical trial examining the efficacy of two novel self-regulation approaches to weight gain prevention in young adults compared to a minimal treatment control. The interventions focus on either small, consistent changes in eating and exercise behaviors, or larger, periodic changes to buffer against expected weight gains. METHODS/DESIGN: SNAP targets recruitment of six hundred young adults (18-35 years) with a body mass index between 21.0-30.0 kg/m2, who will be randomly assigned with equal probability to: (1) minimal intervention control; (2) self-regulation with Small Changes; or (3) self-regulation with Large Changes. Both interventions receive 8 weekly face-to-face group sessions, followed by 2 monthly sessions, with two 4-week refresher courses in each of subsequent years. Participants are instructed to report weight via web at least monthly thereafter, and receive monthly email feedback. Participants in Small Changes are taught to make small daily changes (~100 calorie changes) in how much or what they eat and to accumulate 2000 additional steps per day. Participants in Large Changes are taught to create a weight loss buffer of 5-10 pounds once per year to protect against anticipated weight gains. Both groups are encouraged to self-weigh daily and taught a self-regulation color zone system that specifies action depending on weight gain prevention success. Individualized treatment contact is offered to participants who report weight gains. Participants are assessed at baseline, 4 months, and then annually. The primary outcome is weight gain over an average of 3 years of follow-up; secondary outcomes include diet and physical activity behaviors, psychosocial measures, and cardiovascular disease risk factors. DISCUSSION: SNAP is unique in its focus on weight gain prevention in young adulthood. The trial will provide important information about whether either or both of these novel interventions are effective in preventing weight gain. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01183689.

StudyTop journalModerate

Cross-Frequency Phase–Phase Coupling between Theta and Gamma Oscillations in the Hippocampus

Mariano Belluscio, Kenji Mizuseki, Robert Schmidt +2 more · Journal of Neuroscience · 2012 · 841 citations

Neuronal oscillations allow for temporal segmentation of neuronal spikes. Interdependent oscillators can integrate multiple layers of information. We examined phase-phase coupling of theta and gamma oscillators in the CA1 region of rat hippocampus during maze exploration and rapid eye movement sleep. Hippocampal theta waves were asymmetric, and estimation of the spatial position of the animal was improved by identifying the waveform-based phase of spiking, compared to traditional methods used for phase estimation. Using the waveform-based theta phase, three distinct gamma bands were identified: slow gamma(S) (gamma(S); 30-50 Hz), midfrequency gamma(M) (gamma(M); 50-90 Hz), and fast gamma(F) (gamma(F); 90-150 Hz or epsilon band). The amplitude of each sub-band was modulated by the theta phase. In addition, we found reliable phase-phase coupling between theta and both gamma(S) and gamma(M) but not gamma(F) oscillators. We suggest that cross-frequency phase coupling can support multiple time-scale control of neuronal spikes within and across structures.

StudyTop journalModerate

Reward Value-Based Gain Control: Divisive Normalization in Parietal Cortex

Kenway Louie, Lauren E. Grattan, Paul W. Glimcher · Journal of Neuroscience · 2011 · 332 citations

The representation of value is a critical component of decision making. Rational choice theory assumes that options are assigned absolute values, independent of the value or existence of other alternatives. However, context-dependent choice behavior in both animals and humans violates this assumption, suggesting that biological decision processes rely on comparative evaluation. Here we show that neurons in the monkey lateral intraparietal cortex encode a relative form of saccadic value, explicitly dependent on the values of the other available alternatives. Analogous to extra-classical receptive field effects in visual cortex, this relative representation incorporates target values outside the response field and is observed in both stimulus-driven activity and baseline firing rates. This context-dependent modulation is precisely described by divisive normalization, indicating that this standard form of sensory gain control may be a general mechanism of cortical computation. Such normalization in decision circuits effectively implements an adaptive gain control for value coding and provides a possible mechanistic basis for behavioral context-dependent violations of rationality.

StudyModerate

Toward an Integration of Deep Learning and Neuroscience

Adam Marblestone, Greg Wayne, Konrad P. Körding · arXiv (Cornell University) · 2016 · 688 citations

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. In support of these hypotheses, we argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brain's specialized systems can be interpreted as enabling efficient optimization for specific problem classes. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses.

StudyTop journalModerate

Prediction, cognition and the brain

Andreja Bubić · Frontiers in Human Neuroscience · 2010 · 658 citations

The term "predictive brain" depicts one of the most relevant concepts in cognitive neuroscience which emphasizes the importance of "looking into the future", namely prediction, preparation, anticipation, prospection or expectations in various cognitive domains. Analogously, it has been suggested that predictive processing represents one of the fundamental principles of neural computations and that errors of prediction may be crucial for driving neural and cognitive processes as well as behavior. This review discusses research areas which have recognized the importance of prediction and introduces the relevant terminology and leading theories in the field in an attempt to abstract some generative mechanisms of predictive processing. Furthermore, we discuss the process of testing the validity of postulated expectations by matching these to the realized events and compare the subsequent processing of events which confirm to those which violate the initial predictions. We conclude by suggesting that, although a lot is known about this type of processing, there are still many open issues which need to be resolved before a unified theory of predictive processing can be postulated with regard to both cognitive and neural functioning.

StudyTop journalModerate

The Cost of Accumulating Evidence in Perceptual Decision Making

Jan Drugowitsch, Rubén Moreno‐Bote, Anne K. Churchland +2 more · Journal of Neuroscience · 2012 · 623 citations

Decision making often involves the accumulation of information over time, but acquiring information typically comes at a cost. Little is known about the cost incurred by animals and humans for acquiring additional information from sensory variables due, for instance, to attentional efforts. Through a novel integration of diffusion models and dynamic programming, we were able to estimate the cost of making additional observations per unit of time from two monkeys and six humans in a reaction time (RT) random-dot motion discrimination task. Surprisingly, we find that the cost is neither zero nor constant over time, but for the animals and humans features a brief period in which it is constant but increases thereafter. In addition, we show that our theory accurately matches the observed reaction time distributions for each stimulus condition, the time-dependent choice accuracy both conditional on stimulus strength and independent of it, and choice accuracy and mean reaction times as a function of stimulus strength. The theory also correctly predicts that urgency signals in the brain should be independent of the difficulty, or stimulus strength, at each trial.

StudyLeading journalModerate

Procedures for Behavioral Experiments in Head-Fixed Mice

Zengcai V. Guo, Samuel Andrew Hires, Nuo Li +11 more · PLoS ONE · 2014 · 516 citations

The mouse is an increasingly prominent model for the analysis of mammalian neuronal circuits. Neural circuits ultimately have to be probed during behaviors that engage the circuits. Linking circuit dynamics to behavior requires precise control of sensory stimuli and measurement of body movements. Head-fixation has been used for behavioral research, particularly in non-human primates, to facilitate precise stimulus control, behavioral monitoring and neural recording. However, choice-based, perceptual decision tasks by head-fixed mice have only recently been introduced. Training mice relies on motivating mice using water restriction. Here we describe procedures for head-fixation, water restriction and behavioral training for head-fixed mice, with a focus on active, whisker-based tactile behaviors. In these experiments mice had restricted access to water (typically 1 ml/day). After ten days of water restriction, body weight stabilized at approximately 80% of initial weight. At that point mice were trained to discriminate sensory stimuli using operant conditioning. Head-fixed mice reported stimuli by licking in go/no-go tasks and also using a forced choice paradigm using a dual lickport. In some cases mice learned to discriminate sensory stimuli in a few trials within the first behavioral session. Delay epochs lasting a second or more were used to separate sensation (e.g. tactile exploration) and action (i.e. licking). Mice performed a variety of perceptual decision tasks with high performance for hundreds of trials per behavioral session. Up to four months of continuous water restriction showed no adverse health effects. Behavioral performance correlated with the degree of water restriction, supporting the importance of controlling access to water. These behavioral paradigms can be combined with cellular resolution imaging, random access photostimulation, and whole cell recordings.

StudyTop journalModerate

A framework for mesencephalic dopamine systems based on predictive Hebbian learning

P. Read Montague, Peter Dayan, TJ Sejnowski · Journal of Neuroscience · 1996 · 2,103 citations

We develop a theoretical framework that shows how mesencephalic dopamine systems could distribute to their targets a signal that represents information about future expectations. In particular, we show how activity in the cerebral cortex can make predictions about future receipt of reward and how fluctuations in the activity levels of neurons in diffuse dopamine systems above and below baseline levels would represent errors in these predictions that are delivered to cortical and subcortical targets. We present a model for how such errors could be constructed in a real brain that is consistent with physiological results for a subset of dopaminergic neurons located in the ventral tegmental area and surrounding dopaminergic neurons. The theory also makes testable predictions about human choice behavior on a simple decision-making task. Furthermore, we show that, through a simple influence on synaptic plasticity, fluctuations in dopamine release can act to change the predictions in an appropriate manner.

StudyLeading journalModerate

A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque

Seyed A. Hassani, Mariann Oemisch, Matthew Balcarras +5 more · Scientific Reports · 2017 · 369 citations

Noradrenaline is believed to support cognitive flexibility through the alpha 2A noradrenergic receptor (a2A-NAR) acting in prefrontal cortex. Enhanced flexibility has been inferred from improved working memory with the a2A-NA agonist Guanfacine. But it has been unclear whether Guanfacine improves specific attention and learning mechanisms beyond working memory, and whether the drug effects can be formalized computationally to allow single subject predictions. We tested and confirmed these suggestions in a case study with a healthy nonhuman primate performing a feature-based reversal learning task evaluating performance using Bayesian and Reinforcement learning models. In an initial dose-testing phase we found a Guanfacine dose that increased performance accuracy, decreased distractibility and improved learning. In a second experimental phase using only that dose we examined the faster feature-based reversal learning with Guanfacine with single-subject computational modeling. Parameter estimation suggested that improved learning is not accounted for by varying a single reinforcement learning mechanism, but by changing the set of parameter values to higher learning rates and stronger suppression of non-chosen over chosen feature information. These findings provide an important starting point for developing nonhuman primate models to discern the synaptic mechanisms of attention and learning functions within the context of a computational neuropsychiatry framework.

StudyTop journalModerate

Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework

Tomas Ros, Bernard J. Baars, Ruth A. Lanius +1 more · Frontiers in Human Neuroscience · 2014 · 257 citations

Neurofeedback (NFB) is emerging as a promising technique that enables self-regulation of ongoing brain oscillations. However, despite a rise in empirical evidence attesting to its clinical benefits, a solid theoretical basis is still lacking on the manner in which NFB is able to achieve these outcomes. The present work attempts to bring together various concepts from neurobiology, engineering, and dynamical systems so as to propose a contemporary theoretical framework for the mechanistic effects of NFB. The objective is to provide a firmly neurophysiological account of NFB, which goes beyond traditional behaviorist interpretations that attempt to explain psychological processes solely from a descriptive standpoint whilst treating the brain as a "black box". To this end, we interlink evidence from experimental findings that encompass a broad range of intrinsic brain phenomena: starting from "bottom-up" mechanisms of neural synchronization, followed by "top-down" regulation of internal brain states, moving to dynamical systems plus control-theoretic principles, and concluding with activity-dependent as well as homeostatic forms of brain plasticity. In support of our framework, we examine the effects of NFB in several brain disorders, including attention-deficit hyperactivity (ADHD) and post-traumatic stress disorder (PTSD). In sum, it is argued that pathological oscillations emerge from an abnormal formation of brain-state attractor landscape(s). The central thesis put forward is that NFB tunes brain oscillations toward a homeostatic set-point which affords an optimal balance between network flexibility and stability (i.e., self-organised criticality (SOC)).

StudyModerate

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

Chellammal Surianarayanan, John Jeyasekaran Lawrence, Pethuru Raj Chelliah +2 more · Sensors · 2023 · 104 citations

Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.

StudyModerate

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Muddasar Naeem, Syed Tahir Hussain Rizvi, Antonio Coronato · IEEE Access · 2020 · 241 citations

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision.

StudyTop journalModerate

Noradrenaline and Dopamine Neurons in the Reward/Effort Trade-Off: A Direct Electrophysiological Comparison in Behaving Monkeys

Chiara Varazzani, Aurore San‐Galli, Sophie Gilardeau +1 more · Journal of Neuroscience · 2015 · 508 citations

Motivation determines multiple aspects of behavior, including action selection and energization of behavior. Several components of the underlying neural systems have been examined closely, but the specific role of the different neuromodulatory systems in motivation remains unclear. Here, we compare directly the activity of dopaminergic neurons from the substantia nigra pars compacta and noradrenergic neurons from the locus coeruleus in monkeys performing a task manipulating the reward/effort trade-off. Consistent with previous reports, dopaminergic neurons encoded the expected reward, but we found that they also anticipated the upcoming effort cost in connection with its negative influence on action selection. Conversely, the firing of noradrenergic neurons increased with both pupil dilation and effort production in relation to the energization of behavior. Therefore, this work underlines the contribution of dopamine to effort-based decision making and uncovers a specific role of noradrenaline in energizing behavior to face challenges.

StudyModerate

2021 AAHA/AAFP Feline Life Stage Guidelines

Jessica M. Quimby, Shannon Gowland, Hazel C Carney +3 more · Journal of Feline Medicine and Surgery · 2021 · 156 citations

The guidelines, authored by a Task Force of experts in feline clinical medicine, are an update and extension of the AAFP–AAHA Feline Life Stage Guidelines published in 2010. The guidelines are published simultaneously in the Journal of Feline Medicine and Surgery (volume 23, issue 3, pages 211–233, DOI: 10.1177/1098612X21993657) and the Journal of the American Animal Hospital Association (volume 57, issue 2, pages 51–72, DOI: 10.5326/JAAHA-MS-7189). A noteworthy change from the earlier guidelines is the division of the cat’s lifespan into a five-stage grouping with four distinct age-related stages (kitten, young adult, mature adult, and senior) as well as an end-of-life stage, instead of the previous six. This simplified grouping is consistent with how pet owners generally perceive their cat’s maturation and aging process, and provides a readily understood basis for an evolving, individualized, lifelong feline healthcare strategy. The guidelines include a comprehensive table on the components of a feline wellness visit that provides a framework for systematically implementing an individualized life stage approach to feline healthcare. Included are recommendations for managing the most critical health-related factors in relation to a cat’s life stage. These recommendations are further explained in the following categories: behavior and environmental needs; elimination; life stage nutrition and weight management; oral health; parasite control; vaccination; zoonoses and human safety; and recommended diagnostics based on life stage. A discussion on overcoming barriers to veterinary visits by cat owners offers practical advice on one of the most challenging aspects of delivering regular feline healthcare.

StudyLeading journalModerate

How Prediction Errors Shape Perception, Attention, and Motivation

Hanneke E.M. den Ouden, Peter Kok, Floris P. de Lange · Frontiers in Psychology · 2012 · 544 citations

Prediction errors (PE) are a central notion in theoretical models of reinforcement learning, perceptual inference, decision-making and cognition, and prediction error signals have been reported across a wide range of brain regions and experimental paradigms. Here, we will make an attempt to see the forest for the trees and consider the commonalities and differences of reported PE signals in light of recent suggestions that the computation of PE forms a fundamental mode of brain function. We discuss where different types of PE are encoded, how they are generated, and the different functional roles they fulfill. We suggest that while encoding of PE is a common computation across brain regions, the content and function of these error signals can be very different and are determined by the afferent and efferent connections within the neural circuitry in which they arise.

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Replay and Time Compression of Recurring Spike Sequences in the Hippocampus

Zoltán Nádasdy, Hajime Hirase, András Czurkó +2 more · Journal of Neuroscience · 1999 · 970 citations

Information in neuronal networks may be represented by the spatiotemporal patterns of spikes. Here we examined the temporal coordination of pyramidal cell spikes in the rat hippocampus during slow-wave sleep. In addition, rats were trained to run in a defined position in space (running wheel) to activate a selected group of pyramidal cells. A template-matching method and a joint probability map method were used for sequence search. Repeating spike sequences in excess of chance occurrence were examined by comparing the number of repeating sequences in the original spike trains and in surrogate trains after Monte Carlo shuffling of the spikes. Four different shuffling procedures were used to control for the population dynamics of hippocampal neurons. Repeating spike sequences in the recorded cell assemblies were present in both the awake and sleeping animal in excess of what might be predicted by random variations. Spike sequences observed during wheel running were "replayed" at a faster timescale during single sharp-wave bursts of slow-wave sleep. We hypothesize that the endogenously expressed spike sequences during sleep reflect reactivation of the circuitry modified by previous experience. Reactivation of acquired sequences may serve to consolidate information.

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Responses of monkey dopamine neurons to reward and conditioned stimuli during successive steps of learning a delayed response task

Wolfram Schultz, Paul Apicella, T. Ljungberg · Journal of Neuroscience · 1993 · 1,300 citations

The present investigation had two aims: (1) to study responses of dopamine neurons to stimuli with attentional and motivational significance during several steps of learning a behavioral task, and (2) to study the activity of dopamine neurons during the performance of cognitive tasks known to be impaired after lesions of these neurons. Monkeys that had previously learned a simple reaction time task were trained to perform a spatial delayed response task via two intermediate tasks. During the learning of each new task, a total of 25% of 76 dopamine neurons showed phasic responses to the delivery of primary liquid reward, whereas only 9% of 163 neurons responded to this event once task performance was established. This produced an average population response during but not after learning of each task. Reward responses during learning were significantly more numerous and pronounced in area A10, as compared to areas A8 and A9. Dopamine neurons also showed phasic responses to the two conditioned stimuli. These were the instruction cue, which was the first stimulus in each trial and indicated the target of the upcoming arm movement (58% of 76 neurons during and 44% of 163 neurons after learning), and the trigger stimulus, which was a conditioned incentive stimulus predicting reward and eliciting a saccadic eye movement and an arm reaching movement (38% of neurons during and 40% after learning). None of the dopamine neurons showed sustained activity in the delay between the instruction and trigger stimuli that would resemble the activity of neurons in dopamine terminal areas, such as the striatum and frontal cortex. Thus, dopamine neurons respond phasically to alerting external stimuli with behavioral significance whose detection is crucial for learning and performing delayed response tasks. The lack of sustained activity suggests that dopamine neurons do not encode representational processes, such as working memory, expectation of external stimuli or reward, or preparation of movement. Rather, dopamine neurons are involved with transient changes of impulse activity in basic attentional and motivational processes underlying learning and cognitive behavior.

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Distributed Neural Representation of Expected Value

Brian Knutson, Jonathan Taylor, Matthew T. Kaufman +2 more · Journal of Neuroscience · 2005 · 962 citations

Anticipated reward magnitude and probability comprise dual components of expected value (EV), a cornerstone of economic and psychological theory. However, the neural mechanisms that compute EV have not been characterized. Using event-related functional magnetic resonance imaging, we examined neural activation as subjects anticipated monetary gains and losses that varied in magnitude and probability. Group analyses indicated that, although the subcortical nucleus accumbens (NAcc) activated proportional to anticipated gain magnitude, the cortical mesial prefrontal cortex (MPFC) additionally activated according to anticipated gain probability. Individual difference analyses indicated that, although NAcc activation correlated with self-reported positive arousal, MPFC activation correlated with probability estimates. These findings suggest that mesolimbic brain regions support the computation of EV in an ascending and distributed manner: whereas subcortical regions represent an affective component, cortical regions also represent a probabilistic component, and, furthermore, may integrate the two.

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Get the Story Straight: Contextual Repetition Promotes Word Learning from Storybooks

Jessica S. Horst, Kelly L. Parsons, Natasha M. Bryan · Frontiers in Psychology · 2011 · 185 citations

Although shared storybook reading is a common activity believed to improve the language skills of preschool children, how children learn new vocabulary from such experiences has been largely neglected in the literature. The current study systematically explores the effects of repeatedly reading the same storybooks on both young children's fast and slow mapping abilities. Specially created storybooks were read to 3-year-old children three times during the course of 1 week. Each of the nine storybooks contained two novel name-object pairs. At each session, children either heard three different stories with the same two novel name-object pairs or the same story three times. Importantly, all children heard each novel name the same number of times. Both immediate recall and retention were tested with a four-alternative forced-choice task with pictures of the novel objects. Children who heard the same stories repeatedly were very accurate on both the immediate recall and retention tasks. In contrast, children who heard different stories were only accurate on immediate recall during the last two sessions and failed to learn any of the new words. Overall, then, we found a dramatic increase in children's ability to both recall and retain novel name-object associations encountered during shared storybook reading when they heard the same stories multiple times in succession. Results are discussed in terms of contextual cueing effects observed in other cognitive domains.

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Further evidence for the capacity of mirror self-recognition in cleaner fish and the significance of ecologically relevant marks

Masanori Kohda, Shumpei Sogawa, Alex Jordan +6 more · PLoS Biology · 2022 · 98 citations

An animal that tries to remove a mark from its body that is only visible when looking into a mirror displays the capacity for mirror self-recognition (MSR), which has been interpreted as evidence for self-awareness. Conservative interpretations of existing data conclude that convincing evidence for MSR is currently restricted to great apes. Here, we address proposed shortcomings of a previous study on MSR in the cleaner wrasse Labroides dimidiatus, by varying preexposure to mirrors and by marking individuals with different colors. We found that (1) 14/14 new individuals scraped their throat when a brown mark had been provisioned, but only in the presence of a mirror; (2) blue and green color marks did not elicit scraping; (3) intentionally injecting the mark deeper beneath the skin reliably elicited spontaneous scraping in the absence of a mirror; (4) mirror-naive individuals injected with a brown mark scraped their throat with lower probability and/or lower frequency compared to mirror-experienced individuals; (5) in contrast to the mirror images, seeing another fish with the same marking did not induce throat scraping; and (6) moving the mirror to another location did not elicit renewed aggression in mirror-experienced individuals. Taken together, these results increase our confidence that cleaner fish indeed pass the mark test, although only if it is presented in ecologically relevant contexts. Therefore, we reiterate the conclusion of the previous study that either self-awareness in animals or the validity of the mirror test needs to be revised.

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A Model of Interval Timing by Neural Integration

Patrick Simen, Fuat Balcı, Laura deSouza +2 more · Journal of Neuroscience · 2011 · 341 citations

We show that simple assumptions about neural processing lead to a model of interval timing as a temporal integration process, in which a noisy firing-rate representation of time rises linearly on average toward a response threshold over the course of an interval. Our assumptions include: that neural spike trains are approximately independent Poisson processes, that correlations among them can be largely cancelled by balancing excitation and inhibition, that neural populations can act as integrators, and that the objective of timed behavior is maximal accuracy and minimal variance. The model accounts for a variety of physiological and behavioral findings in rodents, monkeys, and humans, including ramping firing rates between the onset of reward-predicting cues and the receipt of delayed rewards, and universally scale-invariant response time distributions in interval timing tasks. It furthermore makes specific, well-supported predictions about the skewness of these distributions, a feature of timing data that is usually ignored. The model also incorporates a rapid (potentially one-shot) duration-learning procedure. Human behavioral data support the learning rule's predictions regarding learning speed in sequences of timed responses. These results suggest that simple, integration-based models should play as prominent a role in interval timing theory as they do in theories of perceptual decision making, and that a common neural mechanism may underlie both types of behavior.

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Effort-Based Cost–Benefit Valuation and the Human Brain

Paula L. Croxson, Mark E. Walton, Jill X. O’Reilly +2 more · Journal of Neuroscience · 2009 · 568 citations

In both the wild and the laboratory, animals' preferences for one course of action over another reflect not just reward expectations but also the cost in terms of effort that must be invested in pursuing the course of action. The ventral striatum and dorsal anterior cingulate cortex (ACCd) are implicated in the making of cost-benefit decisions in the rat, but there is little information about how effort costs are processed and influence calculations of expected net value in other mammals including humans. We performed a functional magnetic resonance imaging study to determine whether and where activity in the human brain was available to guide effort-based cost-benefit valuation. Subjects were scanned while they performed a series of effortful actions to obtain secondary reinforcers. At the beginning of each trial, subjects were presented with one of eight different visual cues that they had learned indicated how much effort the course of action would entail and how much reward could be expected at its completion. Cue-locked activity in the ventral striatum and midbrain reflected the net value of the course of action, signaling the expected amount of reward discounted by the amount of effort to be invested. Activity in ACCd also reflected the interaction of both expected reward and effort costs. Posterior orbitofrontal and insular activity, however, only reflected the expected reward magnitude. The ventral striatum and anterior cingulate cortex may be the substrate of effort-based cost-benefit valuation in primates as well as in rats.

StudyLeading journalModerate

Novelty or Surprise?

Andrew G. Barto, Marco Mirolli, Gianluca Baldassarre · Frontiers in Psychology · 2013 · 351 citations

Novelty and surprise play significant roles in animal behavior and in attempts to understand the neural mechanisms underlying it. They also play important roles in technology, where detecting observations that are novel or surprising is central to many applications, such as medical diagnosis, text processing, surveillance, and security. Theories of motivation, particularly of intrinsic motivation, place novelty and surprise among the primary factors that arouse interest, motivate exploratory or avoidance behavior, and drive learning. In many of these studies, novelty and surprise are not distinguished from one another: the words are used more-or-less interchangeably. However, while undeniably closely related, novelty and surprise are very different. The purpose of this article is first to highlight the differences between novelty and surprise and to discuss how they are related by presenting an extensive review of mathematical and computational proposals related to them, and then to explore the implications of this for understanding behavioral and neuroscience data. We argue that opportunities for improved understanding of behavior and its neural basis are likely being missed by failing to distinguish between novelty and surprise.

StudyLeading journalModerate

Learning and Animal Movement

Mark A. Lewis, William F. Fagan, Marie Auger‐Méthé +6 more · Frontiers in Ecology and Evolution · 2021 · 93 citations

Integrating diverse concepts from animal behavior, movement ecology, and machine learning, we develop an overview of the ecology of learning and animal movement. Learning-based movement is clearly relevant to ecological problems, but the subject is rooted firmly in psychology, including a distinct terminology. We contrast this psychological origin of learning with the task-oriented perspective on learning that has emerged from the field of machine learning. We review conceptual frameworks that characterize the role of learning in movement, discuss emerging trends, and summarize recent developments in the analysis of movement data. We also discuss the relative advantages of different modeling approaches for exploring the learning-movement interface. We explore in depth how individual and social modalities of learning can matter to the ecology of animal movement, and highlight how diverse kinds of field studies, ranging from translocation efforts to manipulative experiments, can provide critical insight into the learning process in animal movement.

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Self-Control and Saving for Retirement

David Laibson, Andrea Repetto, Jeremy Tobacman +3 more · Brookings Papers on Economic Activity · 1998 · 725 citations

David I. Laibson, Andrea Repetto, Jeremy Tobacman, Robert E. Hall, William G. Gale, George A. Akerlof, Self-Control and Saving for Retirement, Brookings Papers on Economic Activity, Vol. 1998, No. 1 (1998), pp. 91-196

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Human Insula Activation Reflects Risk Prediction Errors As Well As Risk

Kerstin Preuschoff, Steven R. Quartz, Peter Bossaerts · Journal of Neuroscience · 2008 · 796 citations

Understanding how organisms deal with probabilistic stimulus-reward associations has been advanced by a convergence between reinforcement learning models and primate physiology, which demonstrated that the brain encodes a reward prediction error signal. However, organisms must also predict the level of risk associated with reward forecasts, monitor the errors in those risk predictions, and update these in light of new information. Risk prediction serves a dual purpose: (1) to guide choice in risk-sensitive organisms and (2) to modulate learning of uncertain rewards. To date, it is not known whether or how the brain accomplishes risk prediction. Using functional imaging during a simple gambling task in which we constantly changed risk, we show that an early-onset activation in the human insula correlates significantly with risk prediction error and that its time course is consistent with a role in rapid updating. Additionally, we show that activation previously associated with general uncertainty emerges with a delay consistent with a role in risk prediction. The activations correlating with risk prediction and risk prediction errors are the analogy for risk of activations correlating with reward prediction and reward prediction errors for reward expectation. As such, our findings indicate that our understanding of the neural basis of reward anticipation under uncertainty needs to be expanded to include risk prediction.

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The counting stroop: An interference task specialized for functional neuroimaging-validation study with functional MRI

George Bush, Paul J. Whalen, Bruce R. Rosen +3 more · Human Brain Mapping · 1998 · 704 citations

The anterior cingulate cortex has been activated by color Stroop tasks, supporting the hypothesis that it is recruited to mediate response selection or allocate attentional resources when confronted with competing information-processing streams. The current study used the newly developed "Counting Stroop" to identify the mediating neural substrate of cognitive interference. The Counting Stroop, a Stroop variant allowing on-line response time measurements while obviating speech, was created because speaking produces head movements that can exceed those tolerated by functional magnetic resonance imaging (fMRI), preventing the collection of vital performance data. During this task, subjects report by button-press the number of words (1-4) on the screen, regardless of word meaning. Interference trials contain number words that are incongruent with the correct response (e.g., "two" written three times), while neutral trials contain single semantic category common animals (e.g., "bird"). Nine normal right-handed adult volunteers underwent fMRI while performing the Counting Stroop. Group fMRI data revealed significant (P < or = 10(-4) activity in the cognitive division of anterior cingulate cortex when contrasting the interference vs. neutral conditions. On-line performance data showed 1) longer reaction times for interference blocks than for neutral ones, and 2) decreasing reaction times with practice during interference trials (diminished interference effects), indicating that learning occurred. The performance data proved to be a useful guide in analyzing the image data. The relative difference in anterior cingulate activity between the interference and neutral conditions decreased as subjects learned the task. These findings have ramifications for attentional, cognitive interference, learning, and motor control mechanism theories.

StudyLeading journalModerate

Differences in facial expressions during positive anticipation and frustration in dogs awaiting a reward

Annika Bremhorst, Nicole Alessandra Sutter, Hanno Würbel +2 more · Scientific Reports · 2019 · 102 citations

Facial expressions are considered sensitive indicators of emotional states in humans and many animals. Identifying facial indicators of emotion is a major challenge and little systematic research has been done in non-primate species. In dogs, such research is important not only to address fundamental and applied scientific questions but also for practical reasons, since many problem behaviours are assumed to have an emotional basis, e.g. aggression based on frustration. Frustration responses can occur in superficially similar contexts as the emotional state of positive anticipation. For instance, the anticipated delivery of a food reward may induce the state of positive anticipation, but over time, if the food is not delivered, this will be replaced by frustration. We examined dogs' facial expressions in contexts presumed to induce both positive anticipation and frustration, respectively, within a single controlled experimental setting. Using DogFACS, an anatomically-based method for coding facial expressions of dogs, we found that the "Ears adductor" action was more common in the positive condition and "Blink", "Lips part", "Jaw drop", "Nose lick", and "Ears flattener" were more common in the negative condition. This study demonstrates how differences in facial expression in emotionally ambiguous contexts may be used to help infer emotional states of different valence.

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Sex-Specific Programming of Offspring Emotionality after Stress Early in Pregnancy

Bridget R. Mueller, Tracy L. Bale · Journal of Neuroscience · 2008 · 987 citations

Prenatal stress is associated with an increased vulnerability to neurodevelopmental disorders, including autism and schizophrenia. To determine the critical time window when fetal antecedents may induce a disease predisposition, we examined behavioral responses in offspring exposed to stress during early, mid, and late gestation. We found that male offspring exposed to stress early in gestation displayed maladaptive behavioral stress responsivity, anhedonia, and an increased sensitivity to selective serotonin reuptake inhibitor treatment. Long-term alterations in central corticotropin-releasing factor (CRF) and glucocorticoid receptor (GR) expression, as well as increased hypothalamic-pituitary-adrenal (HPA) axis responsivity, were present in these mice and likely contributed to an elevated stress sensitivity. Changes in CRF and GR gene methylation correlated with altered gene expression, providing important evidence of epigenetic programming during early prenatal stress. In addition, we found the core mechanism underlying male vulnerability may involve sex-specific placenta responsivity, where stress early in pregnancy significantly increased expression of PPARalpha (peroxisome proliferator-activated receptor alpha), IGFBP-1 (insulin-like growth factor binding protein 1), HIF3alpha (hypoxia-inducible factor 3a), and GLUT4 (glucose transporter 4) in male placentas but not females. Examination of placental epigenetic machinery revealed basal sex differences, providing further evidence that sex-specific programming begins very early in pregnancy, and may contribute to the timing and vulnerability of the developing fetus to maternal perturbations. Overall, these results indicate that stress experience early in pregnancy may contribute to male neurodevelopmental disorders through impacts on placental function and fetal development.