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Poincaré return mapping for models of elliptic neurons
Poincaré return mapping for models of elliptic neurons

... bursting, between quiescence and tonic spiking, bifurcations of bursting, the emergence of various mixed mode oscillations, and their interactions with bursting and quiescence etc. The examination of nonlocal bifurcation at these transitions is accomplished through reduction of the multidimensional ...
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數位訊號處理概論: Biomedical Signal Processing

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... integrative architectures D. Vernon, Dagstuhl 2003 ...
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... 1993: Meeting on Neural Modeling and Functional Brain Imaging • Brought together modelers and functional brain imagers for the first time. • Tried to determine what research questions modelers could address • The four questions: – Relation between neural activity and imaging signals – Effective con ...
Unimodal or Bimodal Distribution of Synaptic Weights?
Unimodal or Bimodal Distribution of Synaptic Weights?

... Other plasticity models, however, that exhibit always [2] or for certain inputs [3] a unimodal distribution of synaptic weights have the problem that they do not lead to long-term stability of the weights. In particular, if, after learing, the input pattern changes back to ‘weak’ correlations, the n ...
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...  Autonomous and active learners  Processes take time  The mind is a limited-capacity processor  Learn a second language is to learn a skill  Learning is a cognitive process ...
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Axial vs. Appendicular Skeleton

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Na + - Tufts

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November 2000 Volume 3 Number Supp p 1168

... makes very surprising errors when damaged, and these errors are remarkably similar to those made by adults with dyslexia4. The practical success of backpropagation led researchers to look for an alternative performance measure that did not involve a teacher and that could easily be optimized using i ...
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Firing Rate Models

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Recognizing Human Activity from Sensor Data

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Machine Learning - Dipartimento di Informatica

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... or sensory receptors. They are connected to the cell body (the control centre). The impulse travels from the cell body along the axon, where is stops at the axon terminal. Myelin sheaths allow nerve impulses to transmit more quickly along the axon. Sensory neurons – carry nerve impulses (e.g. vision ...
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ch. 48 Nervous System notes

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Bump attractors and the homogeneity assumption

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Nervous System

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P - Research Group of Vision and Image Processing

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Sequential effects: Superstition or rational behavior?

... Possibility of different learning rates in response to slower and faster changes Different levels of sequential effects take place at different time scales, engage different neural areas Current model: adaptation may be happening at different levels of processing and different time scales/rate of ch ...
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The Role of IT in Meteorology

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PNS Study Guide

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Syllabus P140C (68530) Cognitive Science

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Interaural Phase Difference (degree)

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Quiz - Web Adventures
Quiz - Web Adventures

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Neural modeling fields

Neural modeling field (NMF) is a mathematical framework for machine learning which combines ideas from neural networks, fuzzy logic, and model based recognition. It has also been referred to as modeling fields, modeling fields theory (MFT), Maximum likelihood artificial neural networks (MLANS).This framework has been developed by Leonid Perlovsky at the AFRL. NMF is interpreted as a mathematical description of mind’s mechanisms, including concepts, emotions, instincts, imagination, thinking, and understanding. NMF is a multi-level, hetero-hierarchical system. At each level in NMF there are concept-models encapsulating the knowledge; they generate so-called top-down signals, interacting with input, bottom-up signals. These interactions are governed by dynamic equations, which drive concept-model learning, adaptation, and formation of new concept-models for better correspondence to the input, bottom-up signals.
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