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[Olfactory perception and learning in the honey bee (Apis mellifera): calcium imaging in the antenna lobe].

Honey bees are a key-model in the study of learning and memory, because they show considerable learning abilities, their brain is well described and is accessible to a wide range of physiological recordings and treatments. We use in vivo calcium imaging to study olfactory perception in the bee brain, and combine this method to appetitive olfactory conditioning to unravel the neural substrates of olfactory learning. Odours are detected by receptor neurons on the antennae. Each receptor neuron projects to the first-order neuropile of the olfactory pathway, the antennal lobe, connecting to projection neurons in one of its 160 functional units, the glomeruli. In calcium imaging experiments, each odour elicits a particular activity pattern of antennal lobe glomeruli, according to a code conserved between individuals. The antennal lobe is also a site where the olfactory memory is formed. Using optical imaging, two studies have shown modulations of odour representation in the antennal lobe after learning, with different effects depending on the type of conditioning used. While simple differential conditioning (A + B- training) showed an increased calcium response to the reinforced odour, side-specific conditioning (A + B-/B + A- training) decorrelated the calcium responses of odours between brain sides. This difference may owe to the formation of different memories, which will be addressed in future work. By specifically staining antennal lobe neuronal subpopulations, we hope to be able in the future to study synaptic plasticity in the honey bee.

Animals↗

Categorization skills in infants.

The prototypical category-learning experiences with which infants are confronted are ones in which they have had prior experience with and have clearly noted the differences among a set of objects. Their task, then, is to notice also the similarities among the objects and to form a category based on these similarities. The goals of the current experiments were to create a laboratory situation in which it was clear that 10-month-old infants were distinguishing one category exemplar from another, and then to determine the structure of the category representation the infants developed under these learning conditions. A set of artificially created face stimuli was used in which the mean and modal prototypes were distinct. The infants' performance could not be explained by the context theory; they appear to have retained a count of the feature values observed during study. These data give support to the claim that prior to language, infants have available strategies for abstracting category-level information from their perceptual experiences with discriminable objects and events.

Attention↗

Rule extraction with fuzzy neural network.

This paper deals with the learning of understandable decision rules with connectionist systems. Our approach consists of extracting fuzzy control rules with a new fuzzy neural network. Whereas many other works on this area propose to use combinations of nonlinear neurons to approximate fuzzy operations, we use a fuzzy neuron that computes max-min operations. Thus, this neuron can be interpreted as a possibility estimator, just as sigma-pi neurons can support a probabilistic interpretation. Within this context, possibilistic inferences can be drawn through the multi-layered network, using a distributed representation of the information. A new learning procedure has been developed in order that each part of the network can be learnt sequentially, while other parts are frozen. Each step of the procedure is based on the same kind of learning scheme: the backpropagation of a well-chosen cost function with appropriate derivatives of max-min function. An appealing result of the learning phase is the ability of the network to automatically reduce the number of the condition-parts of the rules, if needed. The network has been successfully tested on the learning of a control rule base for an inverted pendulum.

Artificial Intelligence↗

Theory-based Bayesian models of inductive learning and reasoning.

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.

Association Learning↗

[Connectionist models of social learning: a case of learning by observing a simple task].

This article proposes a connectionist model of the social learning theory developed by Bandura (1977). The theory posits that an individual in an interactive situation is capable of learning new behaviours merely by observing them in others. Such learning is acquired through an initial phase in which the individual memorizes what he has observed (observation phase), followed by a second phase where he puts the recorded observations to use as a guide for adjusting his own behaviour (reproduction phase). We shall refer to the two above-mentioned phases to demonstrate that it is conceivable to simulate learning by observation otherwise than through the recording of perceived information using symbolic representation. To this end we shall rely on the formalism of ecological neuron networks (Parisi, Cecconi, & Nolfi, 1990) to implement an agent provided with the major processes identified as essential to learning through observation. The connectionist model so designed shall implement an agent capable of recording perceptive information and producing motor behaviours. The learning situation we selected associates an agent demonstrating goal-achievement behaviour and an observer agent learning the same behaviour by observation. Throughout the acquisition phase, the demonstrator supervises the observer's learning process based on association between spatial information (input) and behavioural information (output). Representation thus constructed then serves as an adjustment guide during the production phase, involving production by the observer of a sequence of actions which he compares to the representation stored in distributed form as constructed through observation. An initial simulation validates model architecture by confirming the requirement for both phases identified in the literature (Bandura, 1977) to simulate learning through observation. The representation constructed over the observation phase evidences acquisition of observed behaviours, although this phase alone is not sufficient to ensure accurate reproduction and must be made functional through the production phase (Deakin & Proteau, 2000). Results obtained through a second simulation replicate those produced by Bandura & Jeffery (1973), who observed that the individual tested following the retention phase recalled recorded information better than he realized in the production phase. The outcome of a third simulation shows that, when performing the transfer task, agents performed the task all the more effectively when they were required to learn a simple path which facilitated knowledge transfer to an adjacent situation. New explanatory assumptions of the mechanics of learning through observation may be produced through OLEANNet. Thus, observed deterioration between memorization and production is caused by successive approximations which occur in the acquisition phase then in the production phase. Further, depending on the type of learning undergone by agents, use of representation as a production guide induces a more or less stringent constraint in the approximation of actual behaviour. This results, during the transfer task, in the ability to effectively generalize acquired knowledge where such knowledge is not specifically related to the task at hand. In conclusion, connectionist model architecture appears valid for modeling learning through observation as defined by Bandura (1977). However, certain limitations appear during implementation, especially in terms of the observed behaviour's availability and the planning of produced behaviours that future developments are liable to counter.

Humans↗

Dissociable retrosplenial and hippocampal contributions to successful formation of survey representations.

During everyday navigation, humans encounter complex environments predominantly from a first-person perspective. Behavioral evidence suggests that these perceptual experiences can be used not only to acquire route knowledge but also to directly assemble map-like survey representations. Most studies of human navigation focus on the retrieval of previously learned environments, and the neural foundations of integrating sequential views into a coherent representation are not yet fully understood. We therefore used our recently introduced virtual-reality paradigm, which provides accuracy and reaction-time measurements precisely indicating the emergence of survey knowledge, and functional magnetic resonance imaging while participants repeatedly encoded a complex environment from a first-person ground-level perspective. Before the experiment, we gave specific instructions to induce survey learning, which, based on the clear evidence for emerging survey knowledge in the behavioral data from 11 participants, proved successful. Neuroimaging data revealed increasing activation across sessions only in bilateral retrosplenial cortices, thus paralleling behavioral measures of map expertise. In contrast, hippocampal activation did not follow absolute performance but rather reflected the amount of knowledge acquired in a given session. In other words, hippocampal activation was most prominent during the initial learning phase and decayed after performance had approached ceiling level. We therefore conclude that, during navigational learning, retrosplenial areas mainly serve to integrate egocentric spatial information with cues about self-motion, whereas the hippocampus is needed to incorporate new information into an emerging memory representation.

Adult↗

Automatic learning strategies and their application to electrophoresis analysis.

Automatic learning plays an important role in image analysis and pattern recognition. A taxonomy of automatic learning strategies is presented; this categorization is based on the amount of inferences the learning element must perform to bridge the gap between environmental and system knowledge representation level. Four main categories are identified and described: rote learning, learning by deduction, learning by induction, and learning by analogy. An application of learning by induction to medical image analysis is then exposed. It consists in the classification of two-dimensional gel electrophoretograms into meaningful distinct classes, as well in their conceptual description.

Electrophoresis, Gel, Two-Dimensional↗

Words in a sea of sounds: the output of infant statistical learning.

One of the first problems confronting infant language learners is word segmentation: discovering the boundaries between words. Prior research suggests that 8-month-old infants can detect the statistical patterns that serve as a cue to word boundaries. However, the representational structure of the output of this learning process is unknown. This research assessed the extent to which statistical learning generates novel word-like units, rather than probabilistically-related strings of sounds. Eight-month-old infants were familiarized with a continuous stream of nonsense words with no acoustic cues to word boundaries. A post-familiarization test compared the infants' responses to words versus part-words (sequences spanning a word boundary) embedded either in simple English contexts familiar to the infants (e.g. "I like my tibudo"), or in matched nonsense frames (e.g. "zy fike ny tibudo"). Listening preferences were affected by the context (English versus nonsense) in which the items from the familiarization phase were embedded during testing. A second experiment confirmed that infants can discriminate the simple English contexts and the matched nonsense frames used in Experiment 1. The third experiment replicated the results of Experiment 1 by contrasting the English test frames with non-linguistic frames generated from tone sequences. The results support the hypothesis that statistical learning mechanisms generate word-like units with some status relative to the native language.

Auditory Perception↗

A sensorimotor map: modulating lateral interactions for anticipation and planning.

Experimental studies of reasoning and planned behavior have provided evidence that nervous systems use internal models to perform predictive motor control, imagery, inference, and planning. Classical (model-free) reinforcement learning approaches omit such a model; standard sensorimotor models account for forward and backward functions of sensorimotor dependencies but do not provide a proper neural representation on which to realize planning. We propose a sensorimotor map to represent such an internal model. The map learns a state representation similar to self-organizing maps but is inherently coupled to sensor and motor signals. Motor activations modulate the lateral connection strengths and thereby induce anticipatory shifts of the activity peak on the sensorimotor map. This mechanism encodes a model of the change of stimuli depending on the current motor activities. The activation dynamics on the map are derived from neural field models. An additional dynamic process on the sensorimotor map (derived from dynamic programming) realizes planning and emits corresponding goal-directed motor sequences, for instance, to navigate through a maze.

Action Potentials↗

Detection and analysis of statistical differences in anatomical shape.

We present a computational framework for image-based analysis and interpretation of statistical differences in anatomical shape between populations. Applications of such analysis include understanding developmental and anatomical aspects of disorders when comparing patients versus normal controls, studying morphological changes caused by aging, or even differences in normal anatomy, for example, differences between genders. Once a quantitative description of organ shape is extracted from input images, the problem of identifying differences between the two groups can be reduced to one of the classical questions in machine learning of constructing a classifier function for assigning new examples to one of the two groups while making as few misclassifications as possible. The resulting classifier must be interpreted in terms of shape differences between the two groups back in the image domain. We demonstrate a novel approach to such interpretation that allows us to argue about the identified shape differences in anatomically meaningful terms of organ deformation. Given a classifier function in the feature space, we derive a deformation that corresponds to the differences between the two classes while ignoring shape variability within each class. Based on this approach, we present a system for statistical shape analysis using distance transforms for shape representation and the support vector machines learning algorithm for the optimal classifier estimation and demonstrate it on artificially generated data sets, as well as real medical studies.

Algorithms↗

The acquisition of plural marking in English and German revisited: schemata versus rules.

This article contributes to a debate in the linguistic and psychological literature that centres around the representation of morphologically complex words in the grammar and in the lexicon. The issue is whether inflectional morphology is rule-based (i.e. symbolically represented), or whether the assumption of pattern association is more adequate to account for the facts. On the basis of the analysis of acquisitional data the article strongly argues for the latter alternative. In a classic experiment that helped shape the development of acquisition theory Berko (1958) reported substantial support for ITEM-AND-PROCESS rules in the acquisition of plural morphology in English. A large part of her results were zero responses (repetition of the stimulus). A reinterpretation of these zero responses in light of schema theory and the cue strength hypothesis shows a striking departure from randomness. Berko's subjects tended to repeat stimuli just to the extent that these already resembled a plural schema. A reinterpretation of data reported in Innes (1974) achieved compatible results. This data set is far more extensive than Berko's and is used in the present study to put the schema model to a more stringent test. A reinterpretation of a parallel experiment with German children, using the cue strength analysis of the more complex plural morphology of German yielded parallel results. Finally, natural acquisitional data obtained from seven German speaking children aged between 2;1 and 2;9 are analysed. Again, strong support is found for the schema model. It is suggested that a schema-learning mechanism may underlie the acquisition of morphology, even when the end product of the learning process involves item-and-process rules, as in the case of English plural formation. In a schema-learning model, the child builds schematic representations for possible singular and plural lexical items as whole gestalts, and attempts to map concrete forms onto these schemata in deciding whether the forms have singular or plural value.

Child↗

A multi-aspect comparison study of supervised word sense disambiguation.

OBJECTIVE: The aim of this study was to investigate relations among different aspects in supervised word sense disambiguation (WSD; supervised machine learning for disambiguating the sense of a term in a context) and compare supervised WSD in the biomedical domain with that in the general English domain. METHODS: The study involves three data sets (a biomedical abbreviation data set, a general biomedical term data set, and a general English data set). The authors implemented three machine-learning algorithms, including (1) naïve Bayes (NBL) and decision lists (TDLL), (2) their adaptation of decision lists (ODLL), and (3) their mixed supervised learning (MSL). There were six feature representations (various combinations of collocations, bag of words, oriented bag of words, etc.) and five window sizes (2, 4, 6, 8, and 10). RESULTS: Supervised WSD is suitable only when there are enough sense-tagged instances with at least a few dozens of instances for each sense. Collocations combined with neighboring words are appropriate selections for the context. For terms with unrelated biomedical senses, a large window size such as the whole paragraph should be used, while for general English words a moderate window size between 4 and 10 should be used. The performance of the authors' implementation of decision list classifiers for abbreviations was better than that of traditional decision list classifiers. However, the opposite held for the other two sets. Also, the authors' mixed supervised learning was stable and generally better than others for all sets. CONCLUSION: From this study, it was found that different aspects of supervised WSD depend on each other. The experiment method presented in the study can be used to select the best supervised WSD classifier for each ambiguous term.

Abbreviations as Topic↗

Evidence for a specific internal representation of motion-force relationships during object manipulation.

Human subjects learned a tracking task which required them to point at a moving target with the free end of an inverted pendulum object. In order to determine how subjects represented this object internally, we studied learning interference between variants of this task in which the pendulum object had either stable or unstable dynamics. Using a novel method, agreement between possible internal representations of the two tasks was estimated by analysis of the motion-to-torque relationships experienced by each subject as they manipulated each object. It was possible to predict retention of the primary task on day 2 from our measure of agreement between primary and interfering tasks on day 1. This result suggests that the subjects learned the correct torque patterns to use to produce specific desired patterns of motion as they learned the balancing task. Surprisingly, the analyses indicate that retention was not impaired when similar motions of the two objects required retrieval of incompatible torque responses, but retention was impaired when similar patterns of motion in the two tasks required similar patterns of applied torque. These findings can be accounted for by a simple model of how multiple similar torque responses are selected and retrieved from memory when responses are freely chosen.

Adult↗

Modulation of neural activity during observational learning of actions and their sequential orders.

How does the brain transform perceptual representations of others' actions into motor representations that can be used to guide behavior? Here we used functional magnetic resonance imaging to record human brain activity while subjects watched others construct multipart objects under varied task demands. We find that relative to resting baseline, passive action observation increases activity within inferior frontal and parietal cortices implicated in action encoding (mirror system) and throughout a distributed network of areas involved in motor representation, including dorsal premotor cortex, pre-supplementary motor area, cerebellum, and basal ganglia (experiments 1 and 2). Relative to passive observation, these same areas show increased activity when subjects observe with the intention to subsequently reproduce component actions using the demonstrated sequential procedures (experiment 1). Observing the same actions with the intention of reproducing component actions, but without the requirement to use the demonstrated sequential procedure, increases activity in the same regions, although to a lesser degree (experiment 2). These findings demonstrate that when attempting to learn behaviors through observation, the observers' intentions modulate responses in a widely distributed network of cortical and subcortical regions implicated previously in action encoding and/or motor representation. Among these regions, only activity within the right intraparietal sulcus predicts the accuracy with which observed procedures are subsequently performed. Successful formation of motor representations of sequential procedures through observational learning is dependent on computations implemented within this parietal region.

Female↗

Stable behavior in a recurrent neural network for a finite state machine.

For the learning of a finite state machine (FSM) by a recurrent neural network (RNN), we think about how to train an RNN so as to stably mimic an FSM even for sequences having a long length. First, we consider the relationship between the stable behavior and the internal representation of states, that is, clusters of the internal units' outputs. As for this relationship, we prove that an RNN can get the stable cluster transitions when a neuron activation parameter is larger than a certain finite value micro0. Secondly, to acquire the stable behavior, we regard the internal representation for the stable behavior as prior knowledge. This produces a new target function of learning with internal representation term. We derive a Bayesian style method to estimate coefficients of the terms in the function, corresponding to hyperparameters. Finally, experiments show that RNNs readily acquire stable behavior by using our proposed method.

Artificial Intelligence↗

Translating cognition into action: the role of visual guidance in observational learning.

This experiment examined the role of two forms of visual guidance in facilitating the translation of cognitive representations into action. Subjects matched a modeled action pattern either concurrently with the model or after the modeled display. They then either did or did not visually monitor their actions during tests of production accuracy in the model's absence. Acquisition of the cognitive representation was assessed periodically. Concurrent matching of modeled actions or visual monitoring of productions both increased the level of observational learning. The more accurate the cognitive representation, the more skilled were subsequent reproductions of the modeled actions. After acquiring proficiency in converting cognition to action, subjects maintained their level of performance accuracy even though modeled and visual-monitoring guidance were withdrawn. These results are in accordance with the theory that cognitive representation mediates response production and that corrective adjustments through visual guidance aid in the translation of conception into action.

Journal Article↗

Landmark stability is a prerequisite for spatial but not discrimination learning.

Neurons sensitive to both place and direction from distinct regions of the hippocampal formation, allometric relationships between spatial learning and hippocampal structure and pronounced impairments in spatial learning after lesions in this area, indicate that the hippocampal formation subserves allocentric spatial learning. To learn more about the process of spatial representation, we have developed a task that provides independent control of both landmark and directional cues. On the basis of physiological and behavioural work, this task also makes it possible to investigate the relevance of associative learning principles, such as predictability, to the spatial domain. We report here that although rats learn to discriminate between landmarks on the basis of their proximity to a reliably predicted food reward, they will only learn to use them to represent its location if they maintain stable locations within a geometric frame of reference.

Animals↗

Reinforcement learning signals predict future decisions.

Optimal behavior in a competitive world requires the flexibility to adapt decision strategies based on recent outcomes. In the present study, we tested the hypothesis that this flexibility emerges through a reinforcement learning process, in which reward prediction errors are used dynamically to adjust representations of decision options. We recorded event-related brain potentials (ERPs) while subjects played a strategic economic game against a computer opponent to evaluate how neural responses to outcomes related to subsequent decision-making. Analyses of ERP data focused on the feedback-related negativity (FRN), an outcome-locked potential thought to reflect a neural prediction error signal. Consistent with predictions of a computational reinforcement learning model, we found that the magnitude of ERPs after losing to the computer opponent predicted whether subjects would change decision behavior on the subsequent trial. Furthermore, FRNs to decision outcomes were disproportionately larger over the motor cortex contralateral to the response hand that was used to make the decision. These findings provide novel evidence that humans engage a reinforcement learning process to adjust representations of competing decision options.

Adult↗