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References on Neural Population Geometry, Motor Learning, and Cerebellar Computation
<p class="c-article-references__intro">
This reference list includes peer-reviewed research on neural population geometry,
brain representations, motor learning, recurrent neural networks, hippocampal
pattern separation, and cerebellar contributions to cognition and movement.
</p>
<ol class="c-article-references__list">
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Bernardi, S. et al. The geometry of abstraction in the hippocampus and prefrontal cortex.
<i>Cell</i> <b>183</b>, 954–967 (2020).
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Gallego, J. A., Perich, M. G., Miller, L. E. & Solla, S. A.
Neural manifolds for the control of movement. <i>Neuron</i>
<b>94</b>, 978–984 (2017).
</p>
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<a href="https://doi.org/10.1016/j.neuron.2017.05.025"
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Sadtler, P. T. et al. Neural constraints on learning. <i>Nature</i>
<b>512</b>, 423–426 (2014).
</p>
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content=”neural population geometry, motor learning, cerebellar computation, neural manifolds, brain representations, pattern separation, recurrent neural networks”>
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Source: www.nature.com


