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The issue documents the recent developments in computational models of single neurons that closely match the input-output relationship of real neurons recorded in vitro or in vivo.
This collection of survey and research articles on Hebbian learning brings together experimental and theoretical aspects of synaptic plasticity in neocortex and hippocampus, with particular emphasis on spike-timing dependent plasticity.
Selection of papers from the 5th International Workshop on Neural Coding. Aula, Italy. September 2003.
Tutorials presented at the 1st Conference of the World Association of Modelers: Biologically Accurate Modeling Meeting (WAM-BAMM '05).
In the last years, the simple picture of additive STDP models was modified and several nonlinear aspects and biophysical details have been added to the models. Theoretical predictions of functional consequences of STDP in simple or even large, neuronal networks have appeared. This Research Topic will review these trends.
Dedicated to the memory of Rolf Kötter, this second research topic on Python in Neuroscience seeks to showcase developments since 2009.
This special section provides a representative overview of existing mature Python modules for neuroscience and neuroinformatics, to demonstrate a critical mass and show that Python is an appropriate choice of interpreter interface for future neuroscience software development.
Issue focuses on computational network models of hippocampal function that derrive from behavioral studies of learning and memory. It provides non-mathematical comparison between different computational models as tools for explaining and motivating related physiological, anatomical, and behavioral research.
We present a special issue focusing on recent advances in computation- and theory-driven approaches to neuroscience that inform a host of biophysical and mechanistic models.
Along with commissioned perspectives, the focus contains primary research articles featuring the best work presented at Cosyne.
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