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Theta Coordinated Error-Driven Learning in the Hippocampus  [PDF]
Nicholas Ketz,Srinimisha G. Morkonda,Randall C. O'Reilly
PLOS Computational Biology , 2013, DOI: 10.1371/journal.pcbi.1003067
Abstract: The learning mechanism in the hippocampus has almost universally been assumed to be Hebbian in nature, where individual neurons in an engram join together with synaptic weight increases to support facilitated recall of memories later. However, it is also widely known that Hebbian learning mechanisms impose significant capacity constraints, and are generally less computationally powerful than learning mechanisms that take advantage of error signals. We show that the differential phase relationships of hippocampal subfields within the overall theta rhythm enable a powerful form of error-driven learning, which results in significantly greater capacity, as shown in computer simulations. In one phase of the theta cycle, the bidirectional connectivity between CA1 and entorhinal cortex can be trained in an error-driven fashion to learn to effectively encode the cortical inputs in a compact and sparse form over CA1. In a subsequent portion of the theta cycle, the system attempts to recall an existing memory, via the pathway from entorhinal cortex to CA3 and CA1. Finally the full theta cycle completes when a strong target encoding representation of the current input is imposed onto the CA1 via direct projections from entorhinal cortex. The difference between this target encoding and the attempted recall of the same representation on CA1 constitutes an error signal that can drive the learning of CA3 to CA1 synapses. This CA3 to CA1 pathway is critical for enabling full reinstatement of recalled hippocampal memories out in cortex. Taken together, these new learning dynamics enable a much more robust, high-capacity model of hippocampal learning than was available previously under the classical Hebbian model.
Hippocampus-dependent learning influences hippocampal neurogenesis  [PDF]
Jonathan R. Epp,Carmen Chow,Liisa A. M. Galea
Frontiers in Neuroscience , 2013, DOI: 10.3389/fnins.2013.00057
Abstract: The structure of the mammalian hippocampus continues to be modified throughout life by continuous addition of neurons in the dentate gyrus. Although the existence of adult neurogenesis is now widely accepted the function that adult generated granule cells play is a topic of intense debate. Many studies have argued that adult generated neurons, due to unique physiological characteristics, play a unique role in hippocampus-dependent learning and memory. However, it is not currently clear whether this is the case or what specific capability adult generated neurons may confer that developmentally generated neurons do not. These questions have been addressed in numerous ways, from examining the effects of increasing or decreasing neurogenesis to computational modeling. One particular area of research has examined the effects of hippocampus dependent learning on proliferation, survival, integration and activation of immature neurons in response to memory retrieval. Within this subfield there remains a range of data showing that hippocampus dependent learning may increase, decrease or alternatively may not alter these components of neurogenesis in the hippocampus. Determining how and when hippocampus-dependent learning alters adult neurogenesis will help to further clarify the role of adult generated neurons. There are many variables (such as age of immature neurons, species, strain, sex, stress, task difficulty, and type of learning) as well as numerous methodological differences (such as marker type, quantification techniques, apparatus size etc.) that could all be crucial for a clear understanding of the interaction between learning and neurogenesis. Here, we review these findings and discuss the different conditions under which hippocampus-dependent learning impacts adult neurogenesis in the dentate gyrus.
Managing Self-instructed Learning within the IS Curriculum: Teaching Learners to Learn  [PDF]
Felix Tan,Hazel Chan
Informing Science The International Journal of an Emerging Transdiscipline , 1997,
Abstract: A significant number of students are enrolled in introductory level information systems courses at New Zealand universities. Some of these institutions require their students to acquire their applications software skills in a self-instructional mode of learning. Most of these students have only experienced teacher-directed learning and when placed in a self-instructional environment may have very limited strategies in their learning. The purpose of this study is to determine if teaching "learners to learn" enhances the acquisition of application software skills. This study considers some of the literature on self-instruction and learner autonomy. The experiment compares two groups of students in self-instructional mode of learning. The control group works independently and the treatment group attends classes that teach the students to manage their own learning. The treatment group is consistent in averaging higher scores demonstrating an overall enhanced learning outcome. This paper challenges IS educators to include learning strategies in courses that require self-instruction. An introduction to working within a new framework should be built in as part of the course. This can prove to be need fulfilling to learners unfamiliar with self-instruction.
Machine learning for neuroimaging with scikit-learn  [PDF]
Alexandre Abraham,Fabian Pedregosa,Michael Eickenberg,Jean Kossaifi,Alexandre Gramfort,Bertrand Thirion,Ga?l Varoquaux
Frontiers in Neuroinformatics , 2014, DOI: 10.3389/fninf.2014.00014
Abstract: Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.
Machine Learning for Neuroimaging with Scikit-Learn  [PDF]
Alexandre Abraham,Fabian Pedregosa,Michael Eickenberg,Philippe Gervais,Andreas Muller,Jean Kossaifi,Alexandre Gramfort,Bertrand Thirion,G?el Varoquaux
Computer Science , 2014,
Abstract: Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.
Scikit-learn: Machine Learning in Python  [PDF]
Fabian Pedregosa,Ga?l Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,édouard Duchesnay
Computer Science , 2012,
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.
The Effects of Theta Precession on Spatial Learning and Simplicial Complex Dynamics in a Topological Model of the Hippocampal Spatial Map  [PDF]
Mamiko Arai,Vicky Brandt,Yuri Dabaghian
PLOS Computational Biology , 2014, DOI: doi/10.1371/journal.pcbi.1003651
Abstract: Learning arises through the activity of large ensembles of cells, yet most of the data neuroscientists accumulate is at the level of individual neurons; we need models that can bridge this gap. We have taken spatial learning as our starting point, computationally modeling the activity of place cells using methods derived from algebraic topology, especially persistent homology. We previously showed that ensembles of hundreds of place cells could accurately encode topological information about different environments (“learn” the space) within certain values of place cell firing rate, place field size, and cell population; we called this parameter space the learning region. Here we advance the model both technically and conceptually. To make the model more physiological, we explored the effects of theta precession on spatial learning in our virtual ensembles. Theta precession, which is believed to influence learning and memory, did in fact enhance learning in our model, increasing both speed and the size of the learning region. Interestingly, theta precession also increased the number of spurious loops during simplicial complex formation. We next explored how downstream readout neurons might define co-firing by grouping together cells within different windows of time and thereby capturing different degrees of temporal overlap between spike trains. Our model's optimum coactivity window correlates well with experimental data, ranging from ~150–200 msec. We further studied the relationship between learning time, window width, and theta precession. Our results validate our topological model for spatial learning and open new avenues for connecting data at the level of individual neurons to behavioral outcomes at the neuronal ensemble level. Finally, we analyzed the dynamics of simplicial complex formation and loop transience to propose that the simplicial complex provides a useful working description of the spatial learning process.
Unsupervised learning and adaptation in a model of adult neurogenesis  [PDF]
Guillermo A. Cecchi,Leopoldo T. Petreanu,Arturo Alvarez-Buylla,Marcelo O. Magnasco
Quantitative Biology , 2001,
Abstract: Adult neurogenesis has long been documented in the vertebrate brain, and recently even in humans. Although it has been conjectured for many years that its functional role is related to the renewing of memories, no clear mechanism as to how this can be achieved has been proposed. We present a scheme in which incorporation of new neurons proceeds at a constant rate, while their survival is activity-dependent and thus contingent upon new neurons establishing suitable connections. We show that a simple mathematical model following these rules organizes its activity so as to maximize the difference between its responses, and can adapt to changing environmental conditions in unsupervised fashion.
Peer assessment enhances student learning  [PDF]
Dennis L. Sun,Naftali Harris,Guenther Walther,Michael Baiocchi
Physics , 2014,
Abstract: Feedback has a powerful influence on learning, but it is also expensive to provide. In large classes, it may even be impossible for instructors to provide individualized feedback. Peer assessment has received attention lately as a way of providing personalized feedback that scales to large classes. Besides these obvious benefits, some researchers have also conjectured that students learn by peer assessing, although no studies have ever conclusively demonstrated this effect. By conducting a randomized controlled trial in an introductory statistics class, we provide evidence that peer assessment causes significant gains in student achievement. The strength of our conclusions depends critically on the careful design of the experiment, which was made possible by a web-based platform that we developed. Hence, our study is also a proof of concept of the high-quality experiments that are possible with online tools.
Perinatal exposure to methoxychlor enhances adult cognitive responses and hippocampal neurogenesis in mice  [PDF]
Mariangela Martini,Ludovic Calandreau,Sakina Mhaouty-Kodja,Matthieu Keller
Frontiers in Behavioral Neuroscience , 2014, DOI: 10.3389/fnbeh.2014.00202
Abstract: During perinatal life, sex steroids, such as estradiol, have marked effects on the development and function of the nervous system. Environmental estrogens or xenoestrogens are man-made chemicals, which animal and human population encounter in the environment and which are able to disrupt the functioning of the endocrine system. Scientific interest in the effects of exposure to xenoestrogens has focused more on fertility and reproductive behaviors, while the effects on cognitive behaviors have received less attention. Therefore, the present study explored whether the organochlorine insecticide Methoxychlor (MXC), with known xenoestrogens properties, administered during the perinatal period (from gestational day 11 to postnatal day 8) to pregnant-lactating females, at an environmentally relevant dose (20 μg/kg (body weight)/day), would also affect learning and memory functions depending on the hippocampus of male and female offspring mice in adulthood. When tested in adulthood, MXC perinatal exposure led to an increase in anxiety-like behavior and in short-term spatial working memory in both sexes. Emotional learning was also assessed using a contextual fear paradigm and MXC treated male and female mice showed an enhanced freezing behavior compared to controls. These results were correlated with an increased survival of adult generated cells in the adult hippocampus. In conclusion, our results show that perinatal exposure to an environmentally relevant dose of MXC has an organizational effect on hippocampus-dependent memory and emotional behaviors.
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