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Reintroduction programs have increased amid unprecedented biodiversity loss, yet the success of these programs lag. A prominent reason for program failure is dispersal beyond the planned range of the population. Management techniques, such as hazing, can be used to prevent movement beyond set boundaries, but to be effective long‐term, the animals must learn to avoid the areas where they are hazed. Although concepts of animal learning have been used to improve reintroduction programs, learning is not often explicitly tested or used as an indicator of program success. We used a conservation behavior framework to evaluate how a range of management techniques influenced learning in a reintroduced population of bison in Banff National Park, Canada. We hypothesized exposure to stronger negative stimuli would enhance learning, leading to more pronounced behavioral responses. Specifically, we tested the degree to which management actions (i.e., drift fence encounters, foot, horseback, helicopter, and combined hazing) elicited behavioral responses and how they facilitated learning. Consistent with our predictions, drift fence interactions and foot and horseback hazing elicited fewer behavioral responses of a smaller magnitude than helicopter hazing or combined methods, suggesting these techniques cause less disturbance to the bison. Bison continually returned to locations where they encountered management actions that caused the least disturbance, demonstrating a lack of associative learning. Bison appeared to form negative associations with locations where they were hazed via helicopter or combined methods, however, and rarely returned to these locations. Evaluating management techniques is essential for improving conservation success. We demonstrate that by bridging the fields of conservation biology and animal learning, we can understand how management techniques influence learning and behavior thereby facilitating effective conservation plans that incorporate disruption levels of the animals, financial costs, and overall effectiveness. Effective conservation plans, in turn, improve our likelihood of successfully managing and recovering species. [ABSTRACT FROM AUTHOR]

Theoretical and empirical scholars of cultural evolution have traditionally studied social learning strategies, such as conformity, as adaptive strategies to obtain accurate information about the environment, whereas within social psychology there has been a greater focus upon the social consequences of such strategies. Although these two approaches are often used in concert when studying human social learning, we believe the potential social benefits of conformity, and of social learning more broadly, have been overlooked in studies of non‐humans. We review evidence from studies of homophily, imitation, and rapid facial mimicry that suggests that behaving like others affords social benefits to non‐human animals and that behaviour matching may be deployed strategically to increase affiliation. Furthermore, we review studies of conformity in dispersers, and suggest that forgoing personal information or preferences in favour of those of the new group during immigration may be a strategy to facilitate social integration. We therefore propose that the informational and social functions of conformity apply to humans and animals alike. We use this perspective to generate several interesting research questions to inspire work in this field. For example, under what conditions do animals use informational or social conformity and what role does uncertainty play in social learning in immigrant individuals? [ABSTRACT FROM AUTHOR]

This article explores the use of camera traps as novel research agents in studying human‐animal interactions and animal behaviour. Drawing on case studies from ecological research in India and ritual practices in Thailand, the authors examine how camera traps transform methodologies across the natural and social sciences and ask what these technologies reveal about animal responses to human presence and the cognitive underpinnings of their behaviours. Using Actor‐Network‐Theory frameworks and more‐than‐human philosophy, the authors argue that camera traps are not passive recording devices but active participants that shape the multispecies realities they observe. The case studies illustrate how animals learn to recognize and respond to camera traps, demonstrating situational awareness, evaluative cognition and adaptive learning. The authors advocate for an interdisciplinary approach to studying human‐animal interactions that accounts for the agentive capacities of both humans and nonhumans, including technological agents like camera traps. This article contributes to ongoing discussions in ethology, anthropology and cognitive science about the use of remote imaging in field research and the conceptual and ethical implications of technological interventions in more‐than‐human lifeworlds. [ABSTRACT FROM AUTHOR]

The study investigated the effect of spatial learning training on group foraging efficiency in goldfish. The researchers conducted experiments using different landmark conditions and found that goldfish with spatial learning experience showed improved performance in completing the foraging task compared to untrained fish. The presence of visual landmarks and the color of external cues did not significantly affect the training effectiveness. The study also found that shoals composed of trained fish had a shorter latency to complete the foraging task than shoals composed of untrained fish. Overall, the findings suggest that individual spatial learning ability contributes to group foraging efficiency in goldfish. [Extracted from the article]

Response disequilibrium theory, an extension of the response deprivation hypothesis, proposes that behavior-change is due to disruptions in an organism's baseline levels of responding. This theory challenges the notion of distinct stimuli as reinforcers or punishers and instead emphasizes the role of instrumental and contingent activities. The present study aimed to test the predictions of response disequilibrium theory by replicating Heth and Warren's, Animal Learning & Behavior, 6, 294–300, (1978) experiment using a modernized apparatus. Sixteen undergraduate psychology students participated in the experiment, which included a familiarization period, baseline periods, and a contingency period. During the contingency period, the instrumental activity was the activation of a video that resulted in a deficit or excess of audio relative to baseline. According to response disequilibrium theory, a deficit will produce a reinforcement effect whereas an excess will produce a punishment effect. The results supported the predictions of response disequilibrium theory, as instrumental video activity increased for participants in the deficit condition and decreased for participants in the excess condition. In addition, the study revealed a strong correlation between the size of disequilibrium and the magnitude of behavior change. These findings further support the use of disequilibrium models to a priori identify effective contingency arrangements in both research and practice. Informal applications of the Premack principle may be supplanted with the precision of a formal model of reinforcement and punishment in response disequilibrium theory. [ABSTRACT FROM AUTHOR]

This paper describes how a mathematically-based model of cognition and species evolution, the Model of Hierarchical Complexity (MHC) can be applied to create more effective and smarter artificial intelligence that is based upon how humans and animals solve problems. To more precisely emulate how a human acts upon the environment, a computer must learn from the environment in a way that is closer to the way that humans do. Moreover, the way humans learn from the environment is an evolutionary extension of how nonhuman animals learn. Hence, to more precisely emulate a human, nonhuman animal learning should also be taken into account. To do so, the MHC proposes an analytic, a priori measurement of the difficulty of task-actions called the Order of Hierarchical Complexity (Commons & Pekker, 2008). Task-actions mean actions directed toward problem-solving. According to the MHC, task-actions grow in complexity throughout development and evolution. The definitions for what makes an action more hierarchically complex will be presented. An application of the MHC to a general artificial intelligence architecture will be presented, and an application of it to a physics problem, called the balance beam problem, will be described. Following that, the possibility of creating truly intelligent androids based on the MHC architectural concept is discussed. An android is a computer based "organism" designed to act like an animal or human. It receives signals or input through sensors (simulating afferent nerves) and acts upon the environment through output agents (simulating muscles activated through motor nerves). The notions from the MHC that will be applied to the design of androids will also be discussed. Based on these developmental and evolutionary principles, such androids will be at least as smart as humans. They will not only pass the Turing test, as will be explained, but they will also be able to complete other tests specifically designed and appropriate for humans. [ABSTRACT FROM AUTHOR]

Background: The nucleotide-binding oligomeric domain (NOD)-like receptor protein 3 (NLRP3) inflammasome is believed to be a key mediator of neuroinflammation and subsequent secondary brain injury induced by ischemic stroke. However, the role and underlying mechanism of the NLRP3 inflammasome in neonates with hypoxic-ischemic encephalopathy (HIE) are still unclear. Methods: The protein expressions of the NLRP3 inflammasome including NLRP3, cysteinyl aspartate specific proteinase-1 (caspase-1) and interleukin-1β (IL-1β), the α-amino-3-hydroxy-5-methyl-4-isoxazole-propionicacid receptor (AMPAR) subunit, and the ATPase valosin-containing protein (VCP/p97), were determined by Western blotting. The interaction between p97 and AMPA glutamate receptor 1 (GluA1) was determined by co-immunoprecipitation. The histopathological level of hypoxic-ischemic brain damage (HIBD) was determined by triphenyltetrazolium chloride (TTC) staining. Polymerase chain reaction (PCR) and Western blotting were used to confirm the genotype of the knockout mice. Motor functions, including myodynamia and coordination, were evaluated by using grasping and rotarod tests. Hippocampus-dependent spatial cognitive function was measured by using the Morris-water maze (MWM). Results: We reported that the NLRP3 inflammasome signaling pathway, such as NLRP3, caspase-1 and IL-1β, was activated in rats with HIBD and oxygen-glucose deprivation (OGD)-treated cultured primary neurons. Further studies showed that the protein level of the AMPAR GluA1 subunit on the hippocampal postsynaptic membrane was significantly decreased in rats with HIBD, and it could be restored to control levels after treatment with the specific caspase-1 inhibitor AC-YVAD-CMK. Similarly, in vitro studies showed that OGD reduced GluA1 protein levels on the plasma membrane in cultured primary neurons, whereas AC-YVAD-CMK treatment restored this reduction. Importantly, we showed that OGD treatment obviously enhanced the interaction between p97 and GluA1, while AC-YVAD-CMK treatment promoted the dissociation of p97 from the GluA1 complex and consequently facilitated the localization of GluA1 on the plasma membrane of cultured primary neurons. Finally, we reported that the deficits in motor function, learning and memory in animals with HIBD, were ameliorated by pharmacological intervention or genetic ablation of caspase-1. Conclusion: Inhibiting the NLRP3 inflammasome signaling pathway promotes neurological recovery in animals with HIBD by increasing p97-mediated surface GluA1 expression, thereby providing new insight into HIE therapy. [ABSTRACT FROM AUTHOR]

The serial reaction time task is a widely used task in behavioural and cognitive neuroscience to assess human and animal learning. Many publications refer to this task as a 'motor learning task', but it is also a perceptual learning task. We emphasize here that the incorrect use of the term 'motor learning' misleads researchers and medical doctors by emphasizing the motor cortex's exclusive role. It has the potential to lead to the misinterpretation of neuroscientific, neuroimaging and clinical studies. The domino effect has the potential to generate more flawed hypotheses and theories. [ABSTRACT FROM AUTHOR]

Background: There is increasing evidence that supplementation of S-adenosylmethionine (SAM) can improve cognitive function in animals and humans, although the outcomes are not always inconsistent. Objective: We conducted a systematic review and meta-analysis to evaluate the correlation between SAM supplementation and improved cognitive function. Methods: We searched studies in the PubMed, Cochrane Library, Embase, Web of Science, and Clinical Trials databases from January 1, 2002 to January 1, 2022. Risk of bias was assessed using the Cochrane risk of bias 2.0 (human studies) and the Systematic Review Center for Laboratory Animal Experimentation risk of bias (animal studies) tools; and evidence quality was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation. STATA software was employed to perform meta-analysis, and the random-effects models was used to evaluate the standardized mean difference with 95% confidence intervals. Results: Out of the 2,375 studies screened, 30 studies met the inclusion criteria. Meta-analyses of animal (p = 0.213) and human (p = 0.047) studies showed that there were no significant differences between the SAM supplementation and control groups. The results of the subgroup analyses showed that the animals aged ≤8 weeks (p = 0.027) and the intervention duration >8 weeks (p = 0.009) were significantly different compared to the controls. Additionally, the Morris water maze test (p = 0.005) used to assess the cognitive level of the animals revealed that SAM could enhance spatial learning and memory in animals. Conclusion: SAM supplementation showed no significant improvement in cognition. Therefore, further studies are needed to assess the effectiveness of SAM supplementation. [ABSTRACT FROM AUTHOR]

Animals learn multiple spatiotemporal contingencies and organize their anticipatory responses accordingly. The representational/computational capacity that underlies such spatiotemporally guided behaviors is not fully understood. To this end, we investigated whether mice make temporal inferences of novel locations based on previously learned spatiotemporal contingencies. We trained 18 C57BL/6J mice to anticipate reward after three different intervals at three different locations and tested their temporal expectations of a reward at five locations simultaneously, including two locations that were not previously associated with reward delivery but adjacent to the previously trained locations. If mice made spatiotemporal inferences, they were expected to interpolate between duration pairs associated with previously reinforced hoppers surrounding the novel hopper. We found that the maximal response rate at the novel locations indeed fell between the two intervals reinforced at the surrounding hoppers. We argue that this pattern of responding might be underlain by spatially constrained Bayesian computations. [ABSTRACT FROM AUTHOR]

Rodents can learn from exposure to rewarding odors to make better and quicker decisions. The piriform cortex is thought to be important for learning complex odor associations; however, it is not understood exactly how it learns to remember discriminations between many, sometimes overlapping, odor mixtures. We investigated how odor mixtures are represented in the posterior piriform cortex (pPC) of mice while they learn to discriminate a unique target odor mixture against hundreds of nontarget mixtures. We find that a significant proportion of pPC neurons discriminate between the target and all other nontarget odor mixtures. Neurons that prefer the target odor mixture tend to respond with brief increases in firing rate at odor onset compared to other neurons, which exhibit sustained and/or decreased firing. We allowed mice to continue training after they had reached high levels of performance and find that pPC neurons become more selective for target odor mixtures as well as for randomly chosen repeated nontarget odor mixtures that mice did not have to discriminate from other nontargets. These single unit changes during overtraining are accompanied by better categorization decoding at the population level, even though behavioral metrics of mice such as reward rate and latency to respond do not change. However, when difficult ambiguous trial types are introduced, the robustness of the target selectivity is correlated with better performance on the difficult trials. Taken together, these data reveal pPC as a dynamic and robust system that can optimize for both current and possible future task demands at once. The neural representation of important features of the environment can change as an animal learns. This study shows that salient odor mixtures become over-represented and distinct in the olfactory cortex of mice, allowing a better and more robust categorization of these stimuli. [ABSTRACT FROM AUTHOR]

Caenorhabditis elegans is capable of learning and remembering behaviorally relevant cues such as smells, tastes, and temperature. This is an example of associative learning, a process in which behavior is modified by making associations between various stimuli. Since the mathematical theory of conditioning does not account for some of its salient aspects, such as spontaneous recovery of extinguished associations, accurate modeling of behavior of real animals during conditioning has turned out difficult. Here, we do this in the context of the dynamics of the thermal preference of C. elegans. We quantify C. elegans thermotaxis in response to various conditioning temperatures, starvation durations, and genetic perturbations using a high-resolution microfluidic droplet assay. We model these data comprehensively, within a biologically interpretable, multi-modal framework. We find that the strength of the thermal preference is composed of two independent, genetically separable contributions and requires a model with at least four dynamical variables. One pathway positively associates the experienced temperature independently of food and the other negatively associates with the temperature when food is absent. The multidimensional structure of the association strength provides an explanation for the apparent classical temperature–food association of C. elegans thermal preference and a number of longstanding questions in animal learning, including spontaneous recovery, asymmetric response to appetitive vs. aversive cues, latent inhibition, and generalization among similar cues. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aims: the focus of this study is to evaluate if the combination of an antibiotic with a ceramic biomaterial is effective in treating osteomyelitis in an infected animal model and to define which model and protocol are best suited for in vivo experiments of local bone infection treatment. Methods: a systematic review was carried out based on PRISMA statement guidelines. A PubMed search was conducted to find original papers on animal models of bone infections using local antibiotic delivery systems with the characteristics of bone substitutes. Articles without a control group, differing from the experimental group only by the addition of antibiotics to the bone substitute, were excluded. Results: a total of 1185 records were retrieved, and after a three-step selection, 34 papers were included. Six manuscripts studied the effect of antibiotic-loaded biomaterials on bone infection prevention. Five articles studied infection in the presence of foreign bodies. In all but one, the combination of an antibiotic with bioceramic bone substitutes tended to prevent or cure bone infection while promoting biomaterial osteointegration. Conclusions: this systematic review shows that the combination of antibiotics with bioceramic bone substitutes may be appropriate to treat bone infection when applied locally. The variability of the animal models, time to develop an infection, antibiotic used, way of carrying and releasing antibiotics, type of ceramic material, and endpoints limits the conclusions on the ideal therapy, enhancing the need for consistent models and guidelines to develop an adequate combination of material and antimicrobial agent leading to an effective human application. [ABSTRACT FROM AUTHOR]

The subdomain of computer vision applications is Image Classification which helps in categorizing the images. The advent of handheld devices and image sensors leads to the availability of a huge amount of data without labels. Hence, to categorize these images, a supervised learning algorithm won't be suitable as it requires labels. On the other hand, unsupervised learning uses clustering that also not useful as its accuracy is not reliable as the data are not labeled in advance. Self-Supervised Learning techniques can be used to overcome this problem. In this work, we present a novel Swin Transformer based Contrastive Self-Supervised Learning (Swin-TCSSL), where the paired sample is formed using the transformation of the given input image and this paired sample is passed to the Swin-T transformer which produces a feature vector. The maximum Mutual Information of these feature vectors is used to form robust clusters and these cluster labels get propagates to the Swin Transformer block until the appropriate clusters are obtained. It is then followed by contrastive learning and finally produces the classified output. The experimental results prove that the proposed system is invariant to occlusion, viewpoint variation, and illumination effects. The proposed Swin-TSSCL achieves state-of-the-art results in 5 benchmark datasets namely CIFAR-10, Snapshot Serengeti, Stanford dogs, Animals with attributes, and ImageNet dataset. As evident from the rigorous experiments, the proposed Swin-TCSSL has set a new global state-of-the-art with an average accuracy of 97.63%, which is comparatively higher than the state-of-the-art systems. [ABSTRACT FROM AUTHOR]

Introduction: The aim of the present study was to investigate the behavioral effects of the benzodiazepine midazolam in male mice, in models of anxiolysis, learning, and abuse-related effects. Methods: In a first set of experiments, male Swiss mice were submitted to the training session of a discriminative avoidance (DA) task on the elevated plus maze to evaluate anxiety-like behavior and learning after vehicle or midazolam (1, 2 or 5 mg/kg, i.g.) administration. The same animals were submitted to a conditioned place preference (CPP) protocol with midazolam (1, 2 or 5 mg/kg, i.g.). In a second experiment, outbred (Swiss) and inbred (C57BL/6) male mice were submitted to a two-bottle choice (TBC) oral midazolam drinking procedure. Animals were exposed to one sucrose bottle and one midazolam (0.008, 0.016 or 0.032 mg/ml) plus sucrose bottle. Results: Midazolam (1 and 2 mg/kg) induced anxiolytic-like effects, and all doses of midazolam prevented animals from learning to avoid the aversive closed arm during the DA training session. Assessment of midazolam reward via the CPP procedure and choice via the TBC procedure showed notable variability. A 2-step cluster analysis for the CPP data showed that midazolam data were well-fitted to 2 separate clusters (preference vs. aversion), albeit with the majority of mice showing preference (75%). Correlational and regression analyses showed no relationship between midazolam reward and anxiolytic-like effects (time spent in the open arms in the DA test) or learning/memory. Two-step cluster analysis of the TBC data also demonstrated that, regardless of strain, mice overall fell into two clusters identified as midazolam-preferring or midazolam-avoiding groups. Both midazolam preference and avoidance were concentration-dependent in a subset of mice. Discussion: Our findings show that midazolam preference is a multifactorial behavior, and is not dependent solely on the emergence of therapeutic (anxiolytic-like) effects, learning impairments, or on genetic factors (inbred vs. outbred animals). [ABSTRACT FROM AUTHOR]

Both natural and artificial agents face the challenge of learning in ways that support effective future behaviour. This may be achieved by different learning regimes, associated with distinct dynamics, and differing dimensionality and geometry of neural task representations. Where two different tasks are learned, neural codes for task-relevant information may be factorised in neocortex. Combinations of supervised and unsupervised learning mechanisms may help partition task knowledge and avoid catastrophic interference (i.e., overwriting existing knowledge). How do humans and other animals learn new tasks? A wave of brain recording studies has investigated how neural representations change during task learning, with a focus on how tasks can be acquired and coded in ways that minimise mutual interference. We review recent work that has explored the geometry and dimensionality of neural task representations in neocortex, and computational models that have exploited these findings to understand how the brain may partition knowledge between tasks. We discuss how ideas from machine learning, including those that combine supervised and unsupervised learning, are helping neuroscientists understand how natural tasks are learned and coded in biological brains. [ABSTRACT FROM AUTHOR]

In northern China, four animals—the fox, weasel, hedgehog, and snake—are commonly believed to have the magical power of immortality, and they are referred to as the Four Animal Spirits (四大门, sidamen). Researchers have regarded these spirits as part of a form of shamanism, but what I learned from my fieldwork in Beijing suggests a different understanding. From the 19th century to the present day, many inhabitants of Beijing have consistently believed that the Four Animal Spirits have their own personalities, intentionalities, and social organisation. They can change their status through self-cultivation, and they share the city with humans, who are their neighbours. As humans can understand animals, these animals can understand humans and respond rationally to changes happening in the world. These beliefs are not unique to Beijing's residents; indeed, similar ideas can be found in classical Chinese literature before the Han Dynasty (202–220 BCE); moreover, these beliefs differ significantly from the widely accepted theory of shamanism. Knowing about the Four Animal Spirits does not constitute a window into or a way of organising human societies; the Four Animal Spirits do not represent a cultural structure or deep unconsciousness. They provide knowledge about the relationship between humans and animals and entice people to learn about these animals, live with them, and, ultimately, construct a world in which humans and animals can coexist. [ABSTRACT FROM AUTHOR]

Amidst lockdown policies in response to the COVID‐19 pandemic, many used video games as a method to maintain a connection with others while ensuring social distancing. A new edition of the Animal Crossing series of games had been released in March 2020 and beat sales and downloads records. The game focuses on living in a natural environment, building a house and a village, as well as capturing, exhibiting, and selling species to progress. Here we examine whether players gain species identification skills and whether it is transferred to real‐life models. We used the results from a survey conducted from the end of March to early April 2020 on 200 people (72 players and 128 nonplayers of Animal Crossing). Participants were first asked to rank their personal interest in nature and then to identify species from photos. The photos displayed both organisms present in the game and organisms that were not. We expected players to obtain a slightly higher score than nonplayers for questions related to the species present in the game and a similar score in both groups for questions related to species not present in the game. Multivariate analyses (multiple linear regression and principal components analysis [PCA]) showed that players were better than nonplayers at identifying real‐life species that were present in the game. The role of the species in the game design impacts the ability to identify the species in real life, such as plants having mainly a role of ornamentation. Additionally, this study suggests that survey participants could correctly assess their naturalistic knowledge in general. This article shows that video games can help enhance ecological learning, improve organisms identification, and might be used as a tool for education in conservation biology. [ABSTRACT FROM AUTHOR]

Humans and other animals demonstrate a remarkable ability to generalize knowledge across distinct contexts and objects during natural behavior. We posit that this ability to generalize arises from a specific representational geometry, that we call abstract and that is referred to as disentangled in machine learning. These abstract representations have been observed in recent neurophysiological studies. However, it is unknown how they emerge. Here, using feedforward neural networks, we demonstrate that the learning of multiple tasks causes abstract representations to emerge, using both supervised and reinforcement learning. We show that these abstract representations enable few-sample learning and reliable generalization on novel tasks. We conclude that abstract representations of sensory and cognitive variables may emerge from the multiple behaviors that animals exhibit in the natural world, and, as a consequence, could be pervasive in high-level brain regions. We also make several specific predictions about which variables will be represented abstractly. How animals learn to generalize from one context to another remains unresolved. Here, the authors show that the abstract representations that are thought to underlie this form of generalization emerge naturally in neural networks trained to perform multiple tasks. [ABSTRACT FROM AUTHOR]

Neurons in parietal cortex exhibit task-related activity during decision-making tasks. However, it remains unclear how long-term training to perform different tasks over months or even years shapes neural computations and representations. We examine lateral intraparietal area (LIP) responses during a visual motion delayed-match-to-category task. We consider two pairs of male macaque monkeys with different training histories: one trained only on the categorization task, and another first trained to perform fine motion-direction discrimination (i.e., pretrained). We introduce a novel analytical approach—generalized multilinear models—to quantify low-dimensional, task-relevant components in population activity. During the categorization task, we found stronger cosine-like motion-direction tuning in the pretrained monkeys than in the category-only monkeys, and that the pretrained monkeys' performance depended more heavily on fine discrimination between sample and test stimuli. These results suggest that sensory representations in LIP depend on the sequence of tasks that the animals have learned, underscoring the importance of considering training history in studies with complex behavioral tasks. Posterior parietal cortex supports visual categorization in macaque monkeys. Here, the authors quantify low-dimensional neural population activity using tensor regression to find that long term training history impacts encoding of categorization. [ABSTRACT FROM AUTHOR]

Many real-world decisions in social contexts are made while observing a partner's actions. To study dynamic interactions during such decisions, we developed a setup where two agents seated face-to-face to engage in game-theoretical tasks on a shared transparent touchscreen display ('transparent games'). We compared human and macaque pairs in a transparent version of the coordination game 'Bach-or-Stravinsky', which entails a conflict about which of two individually-preferred opposing options to choose to achieve coordination. Most human pairs developed coordinated behavior and adopted dynamic turn-taking to equalize the payoffs. All macaque pairs converged on simpler, static coordination. Remarkably, two animals learned to coordinate dynamically after training with a human confederate. This pair selected the faster agent's preferred option, exhibiting turn-taking behavior that was captured by modeling the visibility of the partner's action before one's own movement. Such competitive turn-taking was unlike the prosocial turn-taking in humans, who equally often initiated switches to and from their preferred option. Thus, the dynamic coordination is not restricted to humans but can occur on the background of different social attitudes and cognitive capacities in rhesus monkeys. Overall, our results illustrate how action visibility promotes the emergence and maintenance of coordination when agents can observe and time their mutual actions. [ABSTRACT FROM AUTHOR]

Development of the heart, from early morphogenesis to functional maturation, as well as maintenance of its homeostasis are tasks requiring collaborative efforts of cardiac tissue and different extra‐cardiac organ systems. The brain, lymphoid organs, and gut are among the interaction partners that can communicate with the heart through a wide array of paracrine signals acting at local or systemic level. Disturbance of cardiac homeostasis following ischemic injury also needs immediate response from these distant organs. Our hearts replace dead muscles with non‐contractile fibrotic scars. We have learned from animal models capable of scarless repair that regenerative capability of the heart does not depend only on competency of the myocardium and cardiac‐intrinsic factors but also on long‐range molecular signals originating in other parts of the body. Here, we provide an overview of inter‐organ signals that take part in development and regeneration of the heart. We highlight recent findings and remaining questions. Finally, we discuss the potential of inter‐organ modulatory approaches for possible therapeutic use. [ABSTRACT FROM AUTHOR]

Wildlife conservation and management (WCM) practices have been historically drawn from a wide variety of academic fields, yet practitioners have been slow to engage with emerging conversations about animals as complex beings, whose individuality and sociality influence their relationships with humans. We propose an explicit acknowledgement of wild, nonhuman animals as active participants in WCM. We examined 190 studies of WCM interventions and outcomes to highlight 3 common assumptions that underpin many present approaches to WCM: animal behaviors are rigid and homogeneous; wildlife exhibit idealized wild behavior and prefer pristine habitats; and human–wildlife relationships are of marginal or secondary importance relative to nonhuman interactions. We found that these management interventions insufficiently considered animal learning, decision‐making, individuality, sociality, and relationships with humans and led to unanticipated detrimental outcomes. To address these shortcomings, we synthesized theoretical advances in animal behavioral sciences, animal geographies, and animal legal theory that may help conservation professionals reconceptualize animals and their relationships with humans. Based on advances in these fields, we constructed the concept of animal agency, which we define as the ability of animals to actively influence conservation and management outcomes through their adaptive, context‐specific, and complex behaviors that are predicated on their sentience, individuality, lived experiences, cognition, sociality, and cultures in ways that shape and reshape shared human–wildlife cultures, spaces, and histories. Conservation practices, such as compassionate conservation, convivial conservation, and ecological justice, incorporate facets of animal agency. Animal agency can be incorporated in conservation problem‐solving by assessing the ways in which agency contributes to species' survival and by encouraging more adaptive and collaborative decision‐making among human and nonhuman stakeholders. Article impact statement: Incorporating animal agency into wildlife conservation and management can lead to more effective, nuanced, and just outcomes. [ABSTRACT FROM AUTHOR]

Attitudes towards animals present an important foundation for nature conservation activity. This study investigates the attitude of five- to six-year-old children towards some unpopular animals. We used three types of live animals: earthworms, mealworm beetles, and a toad. In the initial tests, during which the children had the opportunity to observe and to handle the animals, we assessed the degree of children's negative attitudes towards the three types of animals chosen based on their behavioural responses. After this, we investigated children's understanding of the three types of animals through extensive inclusion of direct contact with animals in hands-on activities in an experimental group. The children in the control group had no opportunity to deal with the live animals. After the activities, the tests were repeated for both groups. The degree of negative attitude towards the animals decreased significantly among the experimental group, which was not the case in the control group. No sex differences were found in the children's response. This study provides empirical findings that direct contact with animals positively affects children's attitude towards them and supports the recommendations that experiential learning with live animals should be an integral part of the curriculum in the early years. [ABSTRACT FROM AUTHOR]

Animal pose estimation is very useful in analyzing animal behavior, monitoring animal health and moving trajectories, etc. However, occlusions, complex backgrounds, and unconstrained illumination conditions in wild-animal images often lead to large errors in pose estimation, i.e., the detected key points have large deviations from their true positions in 2D images. In this paper, we propose a method to improve animal pose estimation accuracy by exploiting 3D prior constraints. Firstly, we learn the 3D animal pose dictionary, in which each atom provides prior knowledge about 3D animal poses. Secondly, given the initially estimated 2D animal pose in the image, we represent its latent 3D pose with the learned dictionary. Finally, the representation coefficients are optimized to minimize the difference between the initially estimated 2D pose and the 2D projection of the latent 3D pose. Furthermore, we construct 2D and 3D animal pose datasets, which are used to evaluate the algorithm's performance and learn the 3D pose dictionary, respectively. Our experimental results demonstrate that the proposed method makes good use of the 3D pose knowledge and can effectively improve 2D animal pose estimation. [ABSTRACT FROM AUTHOR]

Constructing states from sequences of observations is an important component of reinforcement learning agents. One solution for state construction is to use recurrent neural networks. Back-propagation through time (BPTT), and real-time recurrent learning (RTRL) are two popular gradient-based methods for recurrent learning. BPTT requires complete trajectories of observations before it can compute the gradients and is unsuitable for online updates. RTRL can do online updates but scales poorly to large networks. In this paper, we propose two constraints that make RTRL scalable. We show that by either decomposing the network into independent modules or learning the network in stages, we can make RTRL scale linearly with the number of parameters. Unlike prior scalable gradient estimation algorithms, such as UORO and Truncated-BPTT, our algorithms do not add noise or bias to the gradient estimate. Instead, they trade off the functional capacity of the network for computationally efficient learning. We demonstrate the effectiveness of our approach over Truncated-BPTT on a prediction benchmark inspired by animal learning and by doing policy evaluation of pre-trained policies for Atari 2600 games. [ABSTRACT FROM AUTHOR]

Heat stress is known to result in neuroinflammation, neuronal damage, and disabilities in learning and memory in animals and humans. It has previously been reported that cognitive impairment caused by neuroinflammation may at least in part be mediated by defective hippocampal neurogenesis, and defective neurogenesis has been linked to aberrantly activated microglial cells. Moreover, the release of cytokines within the brain has been shown to contribute to the disruption of cognitive functions in several conditions following neuroinflammation. In this review, we summarize evolving evidence for the current understanding of inflammation-induced deficits in hippocampal neurogenesis, and the resulting behavioral impairments after heat stress. Furthermore, we provide valuable insights into the molecular and cellular mechanisms underlying neuroinflammation-induced deficits in hippocampal neurogenesis, particularly relating to cognitive dysfunction following heat stress. Lastly, we aim to identify potential mechanisms through which neuroinflammation induces cognitive dysfunction, and elucidate how neuroinflammation contributes to defective hippocampal neurogenesis. This review may therefore help to better understand the relationship between hippocampal neurogenesis and heat stress. [ABSTRACT FROM AUTHOR]

Learners with autism are affected by different challenges during learning. There is a growing focus on education to facilitate learners to become more self-resilient by involving animals in learning spaces. This qualitative study explored the impact of animal involvement on the learning experiences of learners with autism. Twenty diaries, 32 questionnaires, and 40 guided narratives and pictorial voices were used to collect data. The study found that learners demonstrated improved communication, imagination, empathy, motivation, calculation skills, responsibility, and well-being while interacting with animals. The study concluded that involving animals in the learning spaces has significant positive impact on learners. [ABSTRACT FROM AUTHOR]

Humans are an interesting subject of study in comparative cognition. While humans have a lot of anecdotal and subjective knowledge about their own minds and behaviors, researchers tend not to study humans the way they study other species. Instead, comparisons between humans and other animals tend to be based on either assumptions about human behavior and cognition, or very different testing methods. Here we emphasize the importance of using insider knowledge about humans to form interesting research questions about animal cognition while simultaneously stepping back and treating humans like just another species as if one were an alien researcher. This perspective is extremely helpful to identify what aspects of cognitive processes may be interesting and relevant across the animal kingdom. Here we outline some examples of how this objective human-centric approach has helped us to move forward knowledge in several areas of animal acoustic cognition (rhythm, harmonicity, and vocal units). We describe how this approach works, what kind of benefits we obtain, and how it can be applied to other areas of animal cognition. While an objective human-centric approach is not useful when studying traits that do not occur in humans (e.g., magnetic spatial navigation), it can be extremely helpful when studying traits that are relevant to humans (e.g., communication). Overall, we hope to entice more people working in animal cognition to use a similar approach to maximize the benefits of being part of the animal kingdom while maintaining a detached and scientific perspective on the human species. [ABSTRACT FROM AUTHOR]

The ability to learn and flexibly apply sophisticated concepts is thought by many to be what differentiates humans from all other animals. A basic assumption underlying this belief is that some "lower-order" associative learning mechanisms link perceptual events to specific reactions, whereas the kinds of verbalizable concepts that humans form depend on "higher-order" cognitive processes that rely less on perception and more on rational thought. Evidence in support of this interpretation comes largely from experiments in which animals either fail to learn or generalize concepts that humans readily learn, or learn them with great difficulty. Here, we argue that the formation of generalizable relational concepts may depend more on an individual's capacity to shift attention than on the possession of representational processes that are unique to humans. Studies of relational concept learning in non-human animals show that they can learn generalizable concepts when conditions are favorable. In particular, repetition of similar training experiences appears to facilitate attentional redirection, thereby enabling animals to flexibly reenact past events and to judge the similarity of items within stimulus sets. The conditions that promote concept learning in humans may differ substantially from those experienced by most other animals. This does not imply, however, that either (1) conceptual learning mechanisms differ qualitatively from other learning mechanisms, or (2) that the processes that lead to concept formation in humans differ significantly from those present in other species. [ABSTRACT FROM AUTHOR]

With the limitations imposed by the COVID-19 pandemic, new technologies were used as methods to continue teaching and learning activities. This scenario brought forth the need to develop online tools for teaching. Therefore, this research aimed to develop a digital platform linking the knowledge about the central nervous system (CNS) anatomy from feline, equine, and sheep models. The platform was produced from the analysis of a collection of mesoscopic slides made from the sequenced crosssection of the CNS of a feline, an equine, and a sheep. All sections were analysed and stained using the Paul-Wiegert modified technique. The platform was organized in four modules: (1) Neuroanatomy of the Central Nervous System; (2) Neuroanatomy of Feline; (3) Neuroanatomy of Equine; and (4) Neuroanatomy of sheep. For each module, an explanatory document in PDF was developed, as well as video lectures and a descriptive atlas identifying the structures present in the encephalon and in the cervical part of the spinal cord. Even though there are numerous online platforms that allow the study of veterinary anatomy of different species and organs, the veterinary neuroanatomy platform presented here is the first platform that conjointly addresses the CNS anatomy of felines, equines, and sheep. Future research applying this platform as an aid to the study of neuroanatomy by students, teachers, and veterinary professionals should validate its use as a complementary tool for teaching and learning animal neuroanatomy. [ABSTRACT FROM AUTHOR]

Anatomy faculty with cadaver-based laboratory courses were presented with a significant challenge in March 2020 to create equivalent learning experiences without cadaveric access. The undergraduate domestic animal anatomy course at the Colorado State University was halfway into a 16-week semester when COVID-19 lockdown orders and the transition to remote instruction began. The new course curriculum was critically evaluated using student surveys and course outcome data. Most students (92.5%) agreed that the transition to online learning was a success; however, students who valued face-to-face lectures prior to March were less likely to perceive the transition as a success. Qualitative and quantitative analyses of survey results suggest that the resources perceived as most helpful for the transition to online learning were not the same as those that helped facilitate animal anatomy learning. Most students (92.5%) agreed that the Virtual Animal Anatomy (VAA) helped them learn anatomy, and 82.2% indicated that the VAA was a valuable resource following the transition to online learning. Additional resources associated with transition success included course instructors, weekly quizzes, written descriptions of anatomical structures and open laboratory sessions. In contrast, those resources associated with facilitating learning included guided quizzes and asynchronous lecture recordings. These findings suggest that the VAA can support online anatomy learning when used in conjunction with other best practices for online teaching. [ABSTRACT FROM AUTHOR]

Collective learning, the improvement of behaviours through experience of collective actions, is an area of animal learning that has received little attention. We investigated how individual learning during collective actions could produce improvements in collective performance, and how collective decision-making processes, including leadership dynamics, could impact upon learning. We trained artificial neural networks, either solo or paired, at an orientation task, based upon collective navigation in animals. In pairs, we implemented two rules of collective decision making: 'democratic' (weighted average of individual propositions) or 'despotic' (one individual's proposition, determined randomly with weighted probabilities in each trial). Decision-making weightings were varied between pairs, but fixed for a given pair, with asymmetric weightings generating 'leaders' and 'followers'. We found nearly all pairs improved their orientation, but more slowly than solo learners. Within pairs, leaders learnt more quickly than followers ('the passenger–driver effect'). In democratic pairs, collective performance improved through individuals learning to compensate for partner error. This emergent process was not observed in pairs with despotic decision making, in which individuals learnt similarly to solo learners. Our model helps to clarify the links between individual learning, collective decision making and collective performance, in the context of collective navigation, and collective behaviour, more generally. • We modelled collective learning using artificial neural networks. • The passenger-driver effect emerged: leaders learned more quickly than followers. • We found that in democratic pairs, compensation for partner error emerged. • In despotic pairs, individuals learned similarly to solo learners. [ABSTRACT FROM AUTHOR]

Serial reversal-learning procedures are simple preparations that allow for a better understanding of how animals learn about environmental changes, including flexibly shifting responding to adapt to changing reinforcement contingencies. The present study examined serial reversal learning with humans by arranging both midsession and variable contingency reversals across two experiments. We also examined the effects of extinction by adding nonreinforced trials at the end of later sessions and provided the first evaluation of effects of win-stay/lose-shift versus counting strategies on accuracy and response latency of humans' reversal-learning performance. In each experiment, responding tracked contingency reversals, primarily with participants using either win-stay/lose-shift or counting strategies. Introducing variable reversal points in the second experiment resulted in near-exclusive win-stay/lose-shift responding among participants and eliminated counting of trials. Each experiment also revealed an immediate shift from S2 to S1 after experiencing extinction during the initial test trial, indicating resurgence of the initial response through a win-stay/lose-shift response pattern. Therefore, the present study replicates and extends prior findings of a win-stay/lose shift response pattern in situations of greater uncertainty. These findings suggest that differences in environmental certainty induce qualitatively different decision-making strategies. [ABSTRACT FROM AUTHOR]

Despite the growing use of conservation breeding and translocations in conservation, many translocation efforts fail due to predation post-release. Released animals often lack appropriate behaviors for survival, including anti-predator responses. Anti-predator training—a method for encouraging animals to exhibit wariness and defensive responses to predators—has been used to address this challenge with varying degrees of success. The efficacy of anti-predator training hinges on animals learning to recognize and respond to predators, but learning is rarely assessed, or interventions miss key experimental controls to document learning. We present an experimental framework for designing anti-predator training that incorporates suitable controls to infer predator-specific learning and illustrate their use with the critically endangered Hawaiian crow, 'alalā (Corvus hawaiiensis). Our training aimed to increase anti-predator behavior towards a natural predator, the Hawaiian hawk, 'io (Buteo solitaries). In addition to running live predator training trials, we included two control groups, to determine if responses could otherwise be due to accumulated stress and agitation or to generalized increases in fear of movement. We could not verify that the training induced anti-predator learning because responses were similar across all experimental groups. Therefore, without these control groups, we may have wrongly concluded that predator-specific learning occurred. Additionally, despite generations in human care that can erode anti-predator responses, 'alalā showed high levels of predatory wariness during baseline assessments. We discuss the implications of a learning-focused approach to training for managing endangered species that require improved anti-predator competence and the importance of understanding learning mechanisms in diagnosing behavioral problems. Significance statement: Knowledge of animal learning has the potential to improve skills training for animals prior to release in the wild but only if animals learn from the stimuli provided in survival-relevant ways. Here, we test whether a setup used for anti-predator training actually leads to learning when applied with the critically endangered 'alalā (Corvus hawaiiensis) that struggle to survive alongside their native predator. We found that various experimental controls (e.g., capture net and live chicken) produce similar responses to the training itself, which questions the efficacy of training for inducing anti-predator learning. Such investigations are an important tool for improving the effectiveness of conservation behavior interventions that rely on learning outcomes to improve survival. [ABSTRACT FROM AUTHOR]

Drones have emerged as a powerful tool in animal detection, significantly advancing wildlife monitoring, conservation, and management by capturing high-resolution, real-time imagery over areas often inaccessible or challenging for human observers to reach. However, manual analysis of drone imagery for animal detection is labour-intensive and time-consuming. The application of deep learning methods, particularly convolutional neural networks, in automating animal detection from drone imagery has the potential to revolutionise wildlife monitoring, conservation, and management protocols. This review provides a comprehensive overview of the increasing use and prospects of deep learning in animal detection using drone imagery. It explores successful applications of deep learning for animal detection, localisation, recognition, and their combinations. The paper also discusses the challenges, limitations, and future research directions of this field. A key message from this review is the need for representative training data covering the various scenarios in which target animals may appear, image annotation difficulties, and the comparability of DL model results across studies. Many studies have focused on single species, locations, or images with a high density of common target species. Assessments of models are potentially biased from using a single test set; many studies lack metrics to evaluate model efficiency, feasibility, and generalizability, and there are uncertainties regarding the optimal number of training images and required ground sample distance (GSD) for different animal detection tasks in drone imagery. The potential applications of deep learning in wildlife monitoring, conservation, and ecological research using drone imagery are substantial. By enhancing the accuracy and efficiency of animal detection in imagery, this technology could contribute to the understanding and protecting animal populations. To expand the applicability of deep learning to diverse species, environments, and spatial scales, researchers should create standardised benchmark datasets and prioritise open collaboration and data sharing, which would aid in addressing the current challenges. • This paper surveys deep learning methods in animal detection from drone images. • It focuses on animal detection, localisation, recognition, and their combinations. • It also discusses the challenges, limitations, and prospects of this field. • It covers the utility of such methods in wildlife monitoring and conservation. [ABSTRACT FROM AUTHOR]

To adapt to a changing world, we must be able to switch between rules already learned and, at other times, learn rules anew. Often we must do both at the same time, switching between known rules while also constantly re-estimating them. Here, we show these two processes, rule switching and rule learning, rely on distinct but intertwined computations, namely fast inference and slower incremental learning. To this end, we studied how monkeys switched between three rules. Each rule was compositional, requiring the animal to discriminate one of two features of a stimulus and then respond with an associated eye movement along one of two different response axes. By modeling behavior, we found the animals learned the axis of response using fast inference (rule switching) while continuously re-estimating the stimulus-response associations within an axis (rule learning). Our results shed light on the computational interactions between rule switching and rule learning, and make testable neural predictions for these interactions. [ABSTRACT FROM AUTHOR]

Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new training is interleaved with periods of sleep for memory consolidation. Here we used spiking network to study mechanisms behind catastrophic forgetting and the role of sleep in preventing it. The network could be trained to learn a complex foraging task but exhibited catastrophic forgetting when trained sequentially on different tasks. In synaptic weight space, new task training moved the synaptic weight configuration away from the manifold representing old task leading to forgetting. Interleaving new task training with periods of off-line reactivation, mimicking biological sleep, mitigated catastrophic forgetting by constraining the network synaptic weight state to the previously learned manifold, while allowing the weight configuration to converge towards the intersection of the manifolds representing old and new tasks. The study reveals a possible strategy of synaptic weights dynamics the brain applies during sleep to prevent forgetting and optimize learning. Author summary: Artificial neural networks can achieve superhuman performance in many domains. Despite these advances, these networks fail in sequential learning; they achieve optimal performance on newer tasks at the expense of performance on previously learned tasks. Humans and animals on the other hand have a remarkable ability to learn continuously and incorporate new data into their corpus of existing knowledge. Sleep has been hypothesized to play an important role in memory and learning by enabling spontaneous reactivation of previously learned memory patterns. Here we use a spiking neural network model, simulating sensory processing and reinforcement learning in animal brain, to demonstrate that interleaving new task training with sleep-like activity optimizes the network's memory representation in synaptic weight space to prevent forgetting old memories. Sleep makes this possible by replaying old memory traces without the explicit usage of the old task data. [ABSTRACT FROM AUTHOR]

The use of head fixation has become routine in systems neuroscience. However, whether the behavior changes with head fixation, whether animals can learn aspects of a task while freely moving and transfer this knowledge to the head fixed condition, has not been examined in much detail. Here, we used a novel floating platform, the "Air-Track", which simulates free movement in a real-world environment to address the effect of head fixation and developed methods to accelerate training of behavioral tasks for head fixed mice. We trained mice in a Y maze two choice discrimination task. One group was trained while head fixed and compared to a separate group that was pre-trained while freely moving and then trained on the same task while head fixed. Pre-training significantly reduced the time needed to relearn the discrimination task while head fixed. Freely moving and head fixed mice displayed similar behavioral patterns, however, head fixation significantly slowed movement speed. The speed of movement in the head fixed mice depended on the weight of the platform. We conclude that home-cage pre-training improves learning performance of head fixed mice and that while head fixation obviously limits some aspects of movement, the patterns of behavior observed in head fixed and freely moving mice are similar. [ABSTRACT FROM AUTHOR]

Associative aversive learning enables animals to predict and avoid threats and thus is critical for survival and adaptive behavior. Anxiety disorders are characterized with deficits in normal aversive learning mechanisms and hence understanding the neural circuits underlying aversive learning and memory has high clinical relevance. Recent studies have revealed the dopamine system as one of the key modulators of aversive learning. In this review, we highlight recent advances that provide insights into how distinct dopaminergic circuits contribute to aversive learning and memory. [ABSTRACT FROM AUTHOR]

Advances in artificial intelligence, machine learning, and deep neural networks have led to new discoveries in human and animal learning and intelligence. A recent artificial intelligence agent in the DeepMind family, muZero, can complete a variety of tasks with limited information about the world in which it is operating and with high uncertainty about features of current and future space. To perform, muZero uses only three functions that are general yet specific enough to allow learning across a variety of tasks without overgeneralization across different contexts. Similarly, humans and animals are able to learn and improve in complex environments while transferring learning from other contexts and without overgeneralizing. In particular, the mammalian extrahippocampal system (eHPCS) can guide spatial decision making while simultaneously encoding and processing spatial and contextual information. Like muZero, the eHPCS is also able to adjust contextual representations depending on the degree and significance of environmental changes and environmental cues. In this opinion, we will argue that the muZero functions parallel those of the hippocampal system. We will show that the different components of the muZero model provide a framework for thinking about generalizable learning in the eHPCS, and that the evaluation of how transitions in cell representations occur between similar and distinct contexts can be informed by advances in artificial intelligence agents such as muZero. We additionally explain how advances in AI agents will provide frameworks and predictions by which to investigate the expected link between state changes and neuronal firing. Specifically, we will discuss testable predictions about the eHPCS, including the functions of replay and remapping, informed by the mechanisms behind muZero learning. We conclude with additional ways in which agents such as muZero can aid in illuminating prospective questions about neural functioning, as well as how these agents may shed light on potential expected answers. [ABSTRACT FROM AUTHOR]

In this paper we examined the Withania somnifera extract (WSE) improve cognitive, behavioral and mood disorder in animal model of bipolar disorder. The present study was undertaken to investigate cognitive, behavioral and mood disorder effects of WSE on bipolar disorder of the male/female Wistar albino rats' brain. We have observed after 24 hours of last dosing the behavioral, memory and test were carried out followed by estimation of markers of oxidative stress under six groups using continuous sub-anesthetic dose of Amphetamine (Amph) (1.5 mg/kg, i.p) with WSE (300 mg/kg, i.p) and LiCl (1.5 mEq/kg, i.p.) administered daily for 21 days. We have found the percentage alteration was decreased in rat treated with WSE as compared to control indicate that it also improved spatial memory and learning. We have examined various oxidative parameters such as levels of SOD, GSH and TBARS level as compared to control groups. The reduced level of oxidative stress parameters upon administration of WSE and LiCl indicates ameliorating effects of WSE in oxidative stress. We had concluded that WSE showed improvement in learning and memory as animal treated with WSE spent more time in open arm as compared to Amph with WSE and LiCl treated rats. Hence, we can say that WSE improve cognitive, behavioral and mood disorder in animal model of bipolar disorder. [ABSTRACT FROM AUTHOR]

Animal sentience, in a broad sense, refers to the capacity of an animal to have feelings with a subjective character. Biology & Philosophy, 28, 1-30. https://doi.org/10.1007/s10539-012-9351-1 This paper discusses the role of sentience in moral status and animal welfare policy, summarising the current research on the sentience of a range of nonhuman animals. Animal Sentience - An Interdisciplinary Journal on Animal Feeling https://www.wellbeingintlstudiesrepository.org/animsent/ An open-access journal containing target articles and commentaries on a variety of issues in animal sentience. [Extracted from the article]

It is known that humans and animals can learn and utilize category information quickly and efficiently to adapt to changing environments, and several brain areas are involved in learning and encoding category information.However, it is unclear that how the brain system learns and forms categorical representations from the view of neural circuits. In order to investigate this issue from the network level, we combine a recurrent neural network with reinforcement learning to construct a deep reinforcement learning model to demonstrate how the category is learned and represented in the network. The model consists of a policy network and a value network. The policy network is responsible for updating the policy to choose actions, while the value network is responsible for evaluating the action to predict rewards. The agent learns dynamically through the information interaction between the policy network and the value network. This model was trained to learn six stimulus-stimulus associative chains in a sequential paired-association task that was learned by the monkey. The simulated results demonstrated that our model was able to learn the stimulus-stimulus associative chains, and successfully reproduced the similar behavior of the monkey performing the same task. Two types of neurons were found in this model: one type primarily encoded identity information about individual stimuli; the other type mainly encoded category information of associated stimuli in one chain. The two types of activity- patterns were also observed in the primate prefrontal cortex after the monkey learned the same task. Furthermore, the ability of these two types of neurons to encode stimulus or category information was enhanced during thismodel was learning the task. Our results suggest that the neurons in the recurrent neural network have the ability to form categorical representations through deep reinforcement learning during learning stimulus-stimulus associations. It might provide a new approach for understanding neuronal mechanisms underlying how the prefrontal cortex learns and encodes category information. [ABSTRACT FROM AUTHOR]

Domain adaptation techniques learn transferable knowledge from a source domain to a target domain and train models that generalize well in the target domain. Unfortunately, a majority of the existing techniques are only applicable to scenarios that the target-domain data in the task of interest is available for training, yet this is not often true in practice. In general, human beings are experts in generalization across domains. For example, a baby can easily identify the bear from a clipart image after learning this category of animal from the photo images. To reduce the gap between the generalization ability of human and that of machines, we propose a new solution to the challenging zero-shot domain adaptation (ZSDA) problem, where only a single source domain is available and the target domain for the task of interest is not accessible. Inspired by the observation that the knowledge about domain correlation can improve our generalization ability, we explore the correlation between source domain and target domain in an irrelevant knowledge task ($ \mathbb {K}$ K -task), where dual-domain samples are available. We denote the task of interest as the question task ($ \mathbb {Q}$ Q -task) and synthesize its non-accessible target-domain as such that these two tasks have the shared domain correlation. In order to realize our idea, we introduce a new network structure, i.e., conditional coupled generative adversarial networks (CoCoGAN), by extending the coupled generative adversarial networks (CoGAN) into a conditioning model. With a pair of coupling GANs, our CoCoGAN is able to capture the joint distribution of data samples across two domains and two tasks. For CoCoGAN training in a ZSDA task, we introduce three supervisory signals, i.e., semantic relationship consistency across domains, global representation alignment across tasks, and alignment consistency across domains. Experimental results demonstrate that our method can learn a suitable model for the non-accessible target domain and outperforms the existing state of the arts in both image classification and semantic segmentation. [ABSTRACT FROM AUTHOR]