Hebbian Learning
Mostrando 1-12 de 17 artigos, teses e dissertações.
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1. Cell assemblies para expansão de consultas / Cell assemblies for query expansion
Uma das principais tarefas de Recuperação de Informações é encontrar documentos que sejam relevantes a uma consulta. Esta tarefa é difícil porque, em muitos casos os termos de busca escolhidos pelo usuário são diferentes dos termos utilizados pelos autores dos documentos. Ao longo dos anos, várias abordagens foram propostas para lidar com este prob
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 2011
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2. Formation of temporal-feature maps by axonal propagation of synaptic learning
Computational maps are of central importance to a neuronal representation of the outside world. In a map, neighboring neurons respond to similar sensory features. A well studied example is the computational map of interaural time differences (ITDs), which is essential to sound localization in a variety of species and allows resolution of ITDs of the ord
The National Academy of Sciences.
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3. Experience-dependent, asymmetric expansion of hippocampal place fields
Theories of sequence learning based on temporally asymmetric, Hebbian long-term potentiation predict that during route learning the spatial firing distributions of hippocampal neurons should enlarge in a direction opposite to the animal’s movement. On a route AB, increased synaptic drive from cells representing A would cause cells representing B to fire ea
The National Academy of Sciences of the USA.
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4. Brain localization for arbitrary stimulus categories: a simple account based on Hebbian learning.
A central theme of cognitive neuroscience is that different parts of the brain perform different functions. Recent evidence from neuropsychology suggests that even the processing of arbitrary stimulus categories that are defined solely by cultural conventions (e.g., letters versus digits) can become spatially segregated in the cerebral cortex. How could the
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5. Dual synaptic plasticity in the hippocampus: Hebbian and spatiotemporal learning dynamics
We assume that Hebbian learning dynamics (HLD) and spatiotemporal learning dynamics (SLD) are involved in the mechanism of synaptic plasticity in the hippocampal neurons. While HLD is driven by pre- and postsynaptic spike timings through the backpropagating action potential, SLD is evoked by presynaptic spike timings alone. Since the backpropagation attenuat
Springer Netherlands.
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6. Self-organized phase transitions in neural networks as a neural mechanism of information processing.
Transitions between dynamically stable activity patterns imposed on an associative neural network are shown to be induced by self-organized infinitesimal changes in synaptic connection strength and to be a kind of phase transition. A key event for the neural process of information processing in a population coding scheme is transition between the activity pa
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7. Hebbian synapses in hippocampus.
A combination of current- and voltage-clamp techniques applied to hippocampal brain slices was used to evaluate the role of postsynaptic electrogenesis in the induction of associative synaptic enhancement. In accordance with Hebb's postulate for learning, repetitive postsynaptic spiking enabled enhancement in just those synapses that were eligible to change
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8. A new form of cerebellar long-term potentiation is postsynaptic and depends on nitric oxide but not cAMP
Long-term depression (LTD) at cerebellar parallel fiber (PF)-Purkinje cell synapses must be balanced by long-term potentiation (LTP) to prevent saturation and allow reversal of motor learning. The only previously analyzed form of cerebellar LTP is induced by 4–8 Hz PF stimulation and requires cAMP but not nitric oxide. It is a poor candidate to reverse LTD
The National Academy of Sciences.
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9. The role of competitive learning in the generation of DG fields from EC inputs
We follow up on a suggestion by Rolls and co-workers, that the effects of competitive learning should be assessed on the shape and number of spatial fields that dentate gyrus (DG) granule cells may form when receiving input from medial entorhinal cortex (mEC) grid units. We consider a simple non-dynamical model where DG units are described by a threshold-lin
Springer Netherlands.
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10. Neural networks that learn temporal sequences by selection.
A model for formal neural networks that learn temporal sequences by selection is proposed on the basis of observations on the acquisition of song by birds, on sequence-detecting neurons, and on allosteric receptors. The model relies on hypothetical elementary devices made up of three neurons, the synaptic triads, which yield short-term modification of synapt
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11. Input Specificity and Dependence of Spike Timing–Dependent Plasticity on Preceding Postsynaptic Activity at Unitary Connections between Neocortical Layer 2/3 Pyramidal Cells
Layer 2/3 (L2/3) pyramidal cells receive excitatory afferent input both from neighbouring pyramidal cells and from cortical and subcortical regions. The efficacy of these excitatory synaptic inputs is modulated by spike timing–dependent plasticity (STDP). Here we report that synaptic connections between L2/3 pyramidal cell pairs are located proximal to the
Oxford University Press.
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12. Spatial interactions in human vision: from near to far via experience-dependent cascades of connections.
Perceptual learning has been shown to affect early visual processes. Here, we show that learning induces an increase in the spatial range of lateral interactions. Using a lateral masking/facilitation paradigm and bandpass-localized stimuli, we measured the interaction range before and after extensive training on a threshold detection task. For naive observer