Equine learning behaviour

Scientists and equestrians continually seek to achieve a clearer understanding of equine learning behaviour and its implications for training. Behavioural and learning processes in the horse are likely to influence not only equine athletic success but also the usefulness of the horse as a domesticated species. However given the status and commercial importance of the animal, equine learning behaviour has received only limited investigation. Indeed most experimental studies on equine cognitive function to date have addressed behaviour, learning and conceptualization processes at a moderately basic cognitive level compared to studies in other species. It is however, likely that the horses with the greatest ability to learn and form/understand concepts are those, which are better equipped to succeed in terms of the human-horse relationship and the contemporary training environment. Within equitation generally, interpretation of the behavioural processes and training of the desired responses in the horse are normally attempted using negative reinforcement strategies. On the other hand, experimental designs to actually induce and/or measure equine learning rely almost exclusively on primary positive reinforcement regimes. Employing two such different approaches may complicate interpretation and lead to difficulties in identifying problematic or undesirable behaviours in the horse. The visual system provides the horse with direct access to immediate environmental stimuli that affect behaviour but vision in the horse is of yet not fully investigated or understood. Further investigations of the equine visual system will benefit our understanding of equine perception, cognitive function and the subsequent link with learning and training. More detailed comparative investigations of feral or free-ranging and domestic horses may provide useful evidence of attention, stress and motivational issues affecting behavioural and learning processes in the horse. The challenge for scientists is, as always, to design and commission experiments that will investigate and provide insight into these processes in a manner that withstands scientific scrutiny.     

Pattern formation in associative neural networks with weak lateral interaction

A model is proposed for the formation of singularities in the director field of orientation-selective cells in the visual cortex. The model consists of a two-layer associative neural net. The forward connections converge to a continuum of steady states. The degeneracy is removed by lateral interactions. It is shown that in many cases the director field contains critical points which are identified as the vortex locations. Received: 1 January 1993/Accepted in revised form: 8 September 1993     

Structural determinants of lateral gate opening in the protein translocon

The heterotrimeric SecY/Sec61 complex is a protein-conducting channel that provides a passage for proteins across the membrane as well as a means to integrate nascent proteins into the membrane. While the first function is common among membrane protein channels and transporters, the latter is unique. Insertion of nascent membrane proteins, one transmembrane segment at a time, by SecY likely occurs through a lateral gate in the channel. Molecular dynamics simulations have been used to investigate the mechanism of gate opening. Opening and closing the gate under different conditions allowed us to identify structural elements that resist opening as well as those that aid closure. SecE, considered to act as a clamp keeping the lateral gate closed, was found to play no such role. Loosening of the plug by lateral gate opening, a potential step in channel gating, was also observed. The simulations revealed that lipids on time scales of up to 1 micros do not flood channels with an open lateral gate.     

A lateral look at olfactory bulb lateral inhibition

Contrast enhancement in sensory systems often relies on spatial filters implemented by lateral inhibition. However, in this issue of Neuron, Fantana et al. provide evidence that lateral inhibition in the olfactory bulb selectively acts between sparse populations of principal neurons without regard to spatial relations.     

Associative learning in biochemical networks

It has been recently suggested that there are likely generic features characterizing the emergence of systems constructed from the self-organization of self-replicating agents acting under one or more selection pressures. Therefore, structures and behaviors at one length scale may be used to infer analogous structures and behaviors at other length scales. Motivated by this suggestion, we seek to characterize various "animate" behaviors in biochemical networks, and the influence that these behaviors have on genomic evolution. Specifically, in this paper, we develop a simple, chemostat-based model illustrating how a process analogous to associative learning can occur in a biochemical network. Associative learning is a form of learning whereby a system "learns" to associate two stimuli with one another. Associative learning, also known as conditioning, is believed to be a powerful learning process at work in the brain (associative learning is essentially "learning by analogy"). In our model, two types of replicating molecules, denoted as A and B, are present in some initial concentration in the chemostat. Molecules A and B are stimulated to replicate by some growth factors, denoted as G(A) and G(B), respectively. It is also assumed that A and B can covalently link, and that the conjugated molecule can be stimulated by either the G(A) or G(B) growth factors (and can be degraded). We show that, if the chemostat is stimulated by both growth factors for a certain time, followed by a time gap during which the chemostat is not stimulated at all, and if the chemostat is then stimulated again by only one of the growth factors, then there will be a transient increase in the number of molecules activated by the other growth factor. Therefore, the chemostat bears the imprint of earlier, simultaneous stimulation with both growth factors, which is indicative of associative learning. It is interesting to note that the dynamics of our model is consistent with certain aspects of Pavlov's original series of conditioning experiments in dogs. We discuss how associative learning can potentially be performed in vitro within RNA, DNA, or peptide networks. We also describe how such a mechanism could be involved in genomic evolution, and suggest relevant bioinformatics studies that could potentially resolve these issues.     

Improved backpropagation learning in neural networks with windowed momentum

Backpropagation, which is frequently used in Neural Network training, often takes a great deal of time to converge on an acceptable solution. Momentum is a standard technique that is used to speed up convergence and maintain generalization performance. In this paper we present the Windowed Momentum algorithm, which increases speedup over Standard Momentum. Windowed Momentum is designed to use a fixed width history of recent weight updates for each connection in a neural network. By using this additional information, Windowed Momentum gives significant speedup over a set of applications with same or improved accuracy. Windowed Momentum achieved an average speedup of 32% in convergence time on 15 data sets, including a large OCR data set with over 500,000 samples. In addition to this speedup, we present the consequences of sample presentation order. We show that Windowed Momentum is able to overcome these effects that can occur with poor presentation order and still maintain its speedup advantages.     

Boolean analysis of lateral inhibition

Journal of Mathematical Biology - We study Boolean networks which are simple spatial models of the highly conserved Delta–Notch system. The models assume the inhibition of Delta in each cell...     

Endocannabinoids gate state-dependent plasticity of synaptic inhibition in feeding circuits

Changes in food availability alter the output of hypothalamic nuclei that underlie energy homeostasis. Here, we asked whether food deprivation impacts the ability of GABA synapses in the dorsomedial hypothalamus (DMH), an important integrator of satiety signals, to undergo activity-dependent changes. GABA synapses in DMH slices from satiated rats exhibit endocannabinoid-mediated long-term depression (LTD(GABA)) in response to high-frequency stimulation of afferents. When CB1Rs are blocked, however, the same stimulation elicits long-term potentiation (LTP(GABA)), which manifests presynaptically and requires heterosynaptic recruitment of NMDARs and nitric oxide (NO). Interestingly, NO signaling is required for eCB-mediated LTD(GABA). Twenty-four hour food deprivation results in a CORT-mediated loss of CB1R signaling and, consequently, GABA synapses only exhibit LTP(GABA). These observations indicate that CB1R signaling promotes LTD(GABA) and gates LTP(GABA). Furthermore, the satiety state of an animal, through regulation of eCB signaling, determines the polarity of activity-dependent plasticity at GABA synapses in the DMH.     

Protein complex detection with semi-supervised learning in protein interaction networks

Background

Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes. The systematic analysis of PPI networks can enable a great understanding of cellular organization, processes and function. In this paper, we investigate the problem of protein complex detection from noisy protein interaction data, i.e., finding the subsets of proteins that are closely coupled via protein interactions. However, protein complexes are likely to overlap and the interaction data are very noisy. It is a great challenge to effectively analyze the massive data for biologically meaningful protein complex detection.

Results

Many people try to solve the problem by using the traditional unsupervised graph clustering methods. Here, we stand from a different point of view, redefining the properties and features for protein complexes and designing a “semi-supervised” method to analyze the problem. In this paper, we utilize the neural network with the “semi-supervised” mechanism to detect the protein complexes. By retraining the neural network model recursively, we could find the optimized parameters for the model, in such a way we can successfully detect the protein complexes. The comparison results show that our algorithm could identify protein complexes that are missed by other methods. We also have shown that our method achieve better precision and recall rates for the identified protein complexes than other existing methods. In addition, the framework we proposed is easy to be extended in the future.

Conclusions

Using a weighted network to represent the protein interaction network is more appropriate than using a traditional unweighted network. In addition, integrating biological features and topological features to represent protein complexes is more meaningful than using dense subgraphs. Last, the “semi-supervised” learning model is a promising model to detect protein complexes with more biological and topological features available.

     

Threshold learning dynamics in social networks

Social learning is defined as the ability of a population to aggregate information, a process which must crucially depend on the mechanisms of social interaction. Consumers choosing which product to buy, or voters deciding which option to take with respect to an important issue, typically confront external signals to the information gathered from their contacts. Economic models typically predict that correct social learning occurs in large populations unless some individuals display unbounded influence. We challenge this conclusion by showing that an intuitive threshold process of individual adjustment does not always lead to such social learning. We find, specifically, that three generic regimes exist separated by sharp discontinuous transitions. And only in one of them, where the threshold is within a suitable intermediate range, the population learns the correct information. In the other two, where the threshold is either too high or too low, the system either freezes or enters into persistent flux, respectively. These regimes are generally observed in different social networks (both complex or regular), but limited interaction is found to promote correct learning by enlarging the parameter region where it occurs.     

Two-stage lateral inhibition for auditory selectivity

It is shown that the narrow selectivity of the auditory system can be explained by neural processing that yields 20y−d ² y/dz ², wherey is the normalized single-tone response of the basilar membrane andz is the natural logarithm of normalized stapes distance. The desired response is synthesized via a two-stage lateral inhibition process. Stage 1 is the result of interaction between inner and outer hair cells; stage 2 takes place in the auditory cortex.     

On the theory of lateral inhibition

Zusammenfassung Das mathematische Modell für das Prinzip der lateralen Inhibition in der Theorie der optischen Perzeption führt auf ein System nichtlinearer Gleichungen für n reelle Variable. Dieses System wird auf Lösbarkeit und eindeutige Lösbarkeit untersucht. Es zeigt sich, daß die Gleichung als Bedingung für die stationären Zustände eines geeigneten zeitabhängigen Systems zu deuten ist. Hier kann man ein diskretes und ein kontinuierliches Modell einführen. In beiden Fällen kann die Frage der Existenz der Lösungen und der Stabilität einigermaßen vollständig geklärt werden. Eine Verallgemeinerung auf kontinuierlich viele Raumvariable ist möglich.     

Optimizing foraging behaviour through learning

Manifestation of life-history strategy is through the allocation of resources acquired by foraging. Foraging efficiency can be improved by learning, as fishes adjust their behaviour to changing circumstances. We briefly review the influence of learning on the foraging behaviour of fishes and make recommendations for further research. We stress the importance of quantifying learning and memory in relation to ontogeny and life history.     

Specificity of serotoninergic inhibition in Limulus lateral eye

The receptor specificity for synaptically mediated lateral inhibition in Limulus lateral eye retina was studied by structure-activity correlations of the action of the putative indoleaminergic neurotransmitter, serotonin (5-HT), and its isomers and structural analogs, tryptamine (TRYP), 6-hydroxytryptamine (6HT), 5,6-dihydroxytryptamine (5,6-DHT), 5-hydroxydimethyltryptamine (5-HDMT), and 5-hydroxytryptophan (5-HTP). The 5-HT blockers, lysergic acid diethylamide (LSD), bromo-LSD (BOL), and cinanserin, were also tested. The inhibitory action of the indoleaminergic agonists is highly structure-specific. An hydroxyl group in the 5 position of the indole nucleus, sterically unencumbered by hydroxyls in neighboing positions, is essential. In order of decreasing potency, 5-HT, 5-HDMT, and 5-HTP are active agonists; TRYP, 6-HT, and 5,6-DHT are inactive. Configuration and mobility of the side chains of the active agonists also affect the interaction, and these side-chain characteristics correlate with agonist potency. The receptors for inhibitory action and for transmembranal transport in reuptake are different. Both active agonists and inactive analogs appear to be taken up (Adolph and Ehinger, 1975. Cell Tissue Res. 163:1-14). LSD and BOL have bimodal actions: direct inhibition and agonist blockade. These actions may be mediated via low-specificity presynaptic uptake receptor sites rather than highly specific, postsynaptic, agonist receptor sites.     

The role of learning in fish behaviour

Summary The behavioural patterns of fish are the result of innate (built-in) patterns of maturation (developmental changes) and of learning processes (imprinting and trial-and-error learning). Innate behavioural patterns are considered to be hard-wired and inflexible. However, through learning, fish can adapt to environmental change. For instance, the homing behaviour of fish may be partly the result of the development of specific parts of the brain and partly because of changes in behaviour with experience. Similarly, one can assume that the feeding mode of fish involving snap-responses is innate, but learning enables fish to modify their foraging behaviour in response to a fluctuating environment. By reviewing these and other examples, such as the role of recognition learning and socially transmitted behaviour, one can illustrate the importance of learning in the everyday life of fishes. Although learning plays a large role in the behaviour of fishes, the learning capacity of fishes may also be useful to fisheries research and hatchery operations.     

Identifying essential genes in bacterial metabolic networks with machine learning methods

Background  

Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.     

Adaptive networks using learning matrices

Summary The performance of the Learning Matrix (LM) is suitable for the design of adaptive networks of higher complexity. It has been published, how to connect a LM with a generator of patterns (binary or nonbinary) and a ring-counter to result in an automatic classification of the presented patterns. This paper describes, how to connect two LM's to form an Autonomous Learning Matrix Dipole (ALD) and how to organize it, so that it adapts itself to an environment according to a given evaluation scale. For this purpose, a third type of input (beside e and b), namely h seems to be useful. This h-input controls the rate of adaptation of the LM.Using such ALD's, one may design adaptive structures of even higher complexity, for example with an adaptive internal model.The principle of Learning Matrices has been explained in detail (see e.g. IEEE Transactions on Electronic Computers, Vol. EC-12, No. 6, December, 1963, pp. 846–862). Using such learning matrices (LM), one may build up adaptive networks with rather interesting functions. Perhaps they are interesting for the physiologist and psychologist as well as for the engineer. Let us first recall the most essential details of the LM's.

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Zusammenfassung Die Funktion der Lernmatrix (LM) erlaubt den Entwurf adaptiver Netzwerke höherer Komplexität. Es wurde an anderer Stelle schon beschrieben, wie eine LM (binär oder nichtbinär) mit einem Generator für Eigenschaftssätze und einem Ringzähler zusammengeschaltet werden kann, um eine selbsttätige Klassifikation der angebotenen Eigenschaftssätze zu bewirken. Im vorliegenden Aufsatz wird erklärt, wie zwei LM so zusammengeschaltet werden können, dacß sich ein Autonomer Lernmatrix-Dipol (ALD) ergibt, und wie dieser zu organisieren ist, daß er sich einer gegebenen Außenwelt nach Maßgabe einer vorgegebenen Werteskala anpaßt. Zu diesem Zweck erweist sich außer den bisher beschriebenen beiden Zugangen zur LM (nämlich e und b) ein dritter sehr zweckmäßig, nämlich h. Dieser h-Eingang beeinflußt die Lerngeschwindigkeit der LM.Unter Verwendung solcher ALD's kann man adaptive Strukturen noch höherer Komplexität aufbauen, beispielsweise solche mit adaptivem innerem Modell.

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Visiting Professor of Electrical Engineering Stanford University.