http://precedings.nature.com/tags/learning Recently posted documents tagged with 'learning' Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://precedings.nature.com/documents/3151/version/2 How coupled brain rhythms influence cortical information processing to support learning is unresolved. Local field potential and neuronal activity recordings from 64- electrode arrays in sheep inferotemporal cortex showed that visual discrimination learning increased the amplitude of theta oscillations during stimulus presentation. Coupling between theta and gamma oscillations, the theta/gamma ratio and the regularity of theta phase were also increased, but not neuronal firing rates. A neural network model with fast and slow inhibitory interneurons was developed which generated theta nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity similar learning-evoked changes could be produced. The model revealed that altered theta nested gamma could potentiate downstream neuron responses by temporal desynchronization of excitatory neuron output independent of changes in overall firing frequency. This learning-associated desynchronization was also exhibited by inferotemporal cortex neurons. Changes in theta nested gamma may therefore facilitate learning-associated potentiation by temporal modulation of neuronal firing. http://precedings.nature.com/documents/3151/version/2 Tue, 28 Apr 2009 18:37:22 UTC Learning alters theta-nested gamma oscillations in inferotemporal cortex hdl:10101/npre.2009.3151.2 2009-04-28 Keith M. Kendrick Nature Precedings 2009-04-28T18:37:22Z Manuscript Neuroscience http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/3151/version/1 How coupled brain rhythms influence cortical information processing to support learning is unresolved. Local field potential and neuronal activity recordings from 64- electrode arrays in sheep inferotemporal cortex showed that visual discrimination learning increased the amplitude of theta oscillations during stimulus presentation. Coupling between theta and gamma oscillations, the theta/gamma ratio and the regularity of theta phase were also increased, but not neuronal firing rates. A neural network model with fast and slow inhibitory interneurons was developed which generated theta nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity similar learning-evoked changes could be produced. The model revealed that altered theta nested gamma could potentiate downstream neuron responses by temporal desynchronization of excitatory neuron output independent of changes in overall firing frequency. This learning-associated desynchronization was also exhibited by inferotemporal cortex neurons. Changes in theta nested gamma may therefore facilitate learning-associated potentiation by temporal modulation of neuronal firing. http://precedings.nature.com/documents/3151/version/1 Fri, 24 Apr 2009 19:09:55 UTC Learning alters theta-nested gamma oscillations in inferotemporal cortex hdl:10101/npre.2009.3151.1 2009-04-24 Keith M. Kendrick Nature Precedings 2009-04-24T19:09:55Z Manuscript Neuroscience http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/2431/version/1 Recently, behavioural intelligence of the plasmodia of the true slime mold has been demonstrated. It was shown that a large amoeba-like cell Physarum polycephalum subject to a pattern of periodic environmental changes learns and changes its behaviour in anticipation of the next stimulus to come. Currently, it is not known what specific mechanisms are responsible for such behaviour. Here, we show that such behaviour can be mapped into the response of a simple electronic circuit consisting of an LC contour and a memory-resistor (a memristor) to a train of voltage pulses that mimic environment changes. We identify a possible microscopic origin of the memristive behaviour in the Physarum polycephalum, which together with the naturally occurring biological oscillators, forms the basis of the amoeba’s learning. These microscopic memristive features are likely to occur in other unicellular as well as multicellular organisms, albeit in different forms. Therefore, the above memristive circuit model, which has learning properties, is useful to better understand the origins of primitive intelligence. http://precedings.nature.com/documents/2431/version/1 Fri, 24 Oct 2008 16:28:54 UTC Memristive model of amoeba’s learning hdl:10101/npre.2008.2431.1 2008-10-24 Yuriy V. Pershin Nature Precedings 2008-10-24T16:28:54Z Manuscript Ecology Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/2343/version/1 Working memory, our ability to process and remember information, is linked to a range of cognitive activities from reasoning tasks to verbal comprehension. There is also extensive evidence of the relationship between working memory and learning outcomes. However, some researchers suggest that working memory is simply a proxy for IQ and does not make a unique contribution to learning outcomes. Here we show that children’s working memory skills at 5 years of age was the best predictor of reading, spelling, and math outcomes six years later. IQ, in contrast, accounted for a smaller portion of unique variance to reading and math skills, and was not a significant predictor of spelling performance. Our results demonstrate that working memory is not a proxy for IQ, but rather represents a dissociable cognitive skill with unique links to learning outcomes. Critically, we find that working memory at the start of formal education is a more powerful predictor of subsequent academic success than IQ. This result has important implications for education, particularly with respect to developing intervention and training. It appears that we should target our efforts in developing working memory skills in order to see gains in learning. http://precedings.nature.com/documents/2343/version/1 Fri, 24 Oct 2008 10:35:58 UTC Working memory: Is it the new IQ? hdl:10101/npre.2008.2343.1 2008-10-24 Tracy Packiam Alloway Nature Precedings 2008-10-24T10:35:58Z Manuscript Ecology Neuroscience http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2008.2171.1 Our past experience is one of the primary sources of information when faced with a choice. We ask ourselves: “what will happen if I do this?” Accurately predicting the consequences of our actions is usually modeled by operant (instrumental) learning experiments. These types of experiments are often contrasted with classical (Pavlovian) conditioning experiments in a dichotomy. And indeed, different brain circuits mediate the acquisition of skills and habits (via operant/instrumental learning) and the acquisition of facts (via classical/Pavlovian learning). However, realistic learning situations always comprise interactions of skill- and fact-learning components (composite learning). Fixed flying Drosophila melanogaster at the torque meter provide one of the very few systems where the relationship of operant and classical predictors in composite learning can be studied with sufficient rigor. The latest experiments show that the textbook operant/classical dichotomy is misleading and that instead composite learning consists of multiple interacting memory systems. These interactions between predictive stimuli (classical component) and goal-directed actions (operant component) make composite conditioning more effective than the operant and classical components alone (learning-by-doing, generation effect). Rutabaga (rut) mutants are impaired in learning about the (classical) stimuli, but show improved (operant) behavior learning. This is the first evidence that operant and classical conditioning differ not only at the circuit, but also at the molecular level. The interaction between operant and classical components is reciprocal and hierarchical, such that the classical suppresses the operant component. Experiments with transgenic flies demonstrate that this suppression of operant learning is mediated by the mushroom-bodies and serves to ensure that the classical memories can be generalized for access by other behaviors. Extended training can overcome this suppression and transforms goal-directed actions into habitual responses. This interaction leads to efficient learning, enables generalization and prevents premature habit-formation. http://dx.doi.org/10.1038/npre.2008.2171.1 Wed, 17 Sep 2008 12:49:48 UTC Mushroom-bodies regulate habit formation in Drosophila doi:10.1038/npre.2008.2171.1 2008-09-17 Björn Brembs Nature Precedings 2008-09-17T12:49:48Z Presentation Genetics & Genomics Neuroscience http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/1865/version/1 How is muscle force modulated during hand exercise? Oxygenation in the contralateral primary motor cortex (M1) has been observed to vary considerably across trials of repetitive handgrip exercise. No linear relationship was observed between the average value of oxygenation determined by a block design study and the force of the handgrip. We found reduced oxygenation in the ipsilateral M1 and unchanged oxygenation in the contralateral M1 during repetitive static handgrip exercises (40% and 60% maximal voluntary contraction; 10 s exercise/75 s rest; 5 sets), which might be due to short-term motor learning. These results support the hypothesis that the ipsilateral M1 might functionally compensate for the contralateral M1 in force modulation during unilateral exercises. http://precedings.nature.com/documents/1865/version/1 Thu, 08 May 2008 09:15:33 UTC Reduced contribution of the ipsilateral primary motor cortex to force modulation with short-term motor learning in humans: An NIRS study hdl:10101/npre.2008.1865.1 2008-05-08 Kenichi Shibuya Nature Precedings 2008-05-08T09:15:33Z Manuscript Neuroscience http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2007.61.1 The present study reveals the existence of an intrinsic spatial code within neuronal spikes that predicts behavior. As rats learnt a T-maze procedural task, simultaneous changes in temporal occurrence of spikes and spike directivity are evidenced in “expert” neurons. While the number of spikes between the tone delivery and the beginning of turn phase reduced with learning, the generated spikes between these two events acquired behavioral meaning that is of highest value for action selection. Spike directivity is thus a hidden feature that reveals the semantics of each spike and in the current experiment, predicts the correct turn that the animal would subsequently make to obtain reward. Semantic representation of behavior can then be revealed as modulations in spike directivity during the time. This predictability of observed behavior based on subtle changes in spike directivity represents an important step towards reading and understanding the underlying neural code. http://dx.doi.org/10.1038/npre.2007.61.1 Mon, 18 Jun 2007 12:20:14 UTC Reading the Neural Code: What do Spikes Mean for Behavior? doi:10.1038/npre.2007.61.1 2007-06-18 Dorian Aur Nature Precedings 2007-06-18T12:20:14Z Manuscript Neuroscience http://creativecommons.org/licenses/by/2.5/