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Titlebook: Neural Mechanisms; New Challenges in th Fabrizio Calzavarini,Marco Viola Book 2021 Springer Nature Switzerland AG 2021 Bayesian models in n

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樓主: lutein
21#
發(fā)表于 2025-3-25 06:05:56 | 只看該作者
22#
發(fā)表于 2025-3-25 10:59:50 | 只看該作者
Lena K?stnerpace contains the soil solution and the soil air. The components, to a first approximation, are water and the common atmospheric gases nitrogen, oxygen, water vapour and carbon dioxide. The solution in the pore space is of course not pure water but contains ions, some of which serve as nutrients for
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發(fā)表于 2025-3-25 12:33:25 | 只看該作者
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發(fā)表于 2025-3-25 17:35:31 | 只看該作者
Modelling Bayesian Computation in the Brain: Unification, Explanation, and Constraintsge of behavioral phenomena under a common mathematical framework, but can also place useful constraints on both mechanism discovery and confirmation among competing mechanistic models. After reviewing some reasons for decoupling unification and explanation, we raise two challenges for their view. Fi
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發(fā)表于 2025-3-25 21:31:08 | 只看該作者
26#
發(fā)表于 2025-3-26 01:44:32 | 只看該作者
Circuital and Developmental Explanations for the Cortexdespite its strikingly uniform structure. This work analyzes the phenomenon of the apparent clash between uniformity and variety of functions, and it pinpoints the sort of explanations that this phenomenon requests. A possible resolution of this tension has been proposed several times in terms of a
27#
發(fā)表于 2025-3-26 07:24:10 | 只看該作者
Data Mining the Brain to Decode the Mindues from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises wil
28#
發(fā)表于 2025-3-26 10:08:19 | 只看該作者
29#
發(fā)表于 2025-3-26 15:13:34 | 只看該作者
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發(fā)表于 2025-3-26 19:05:41 | 只看該作者
Saving Data Analysis: Epistemic Friction and Progress in Neuroimaging Researchs and critics of neuroscience alike. In this chapter I argue that of data analysis is epistemically challenged in part because data are causally separated from the events that they are intended to provide evidence for claims about. Experimental manipulations place researchers in epistemically advant
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