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In the past decades, computational neuroscience has seen a burgeoning of normative approaches. These studies made significant advances in formulating formal theories of optimality, and optimal computations, identifying relevant physical and computational constraints under which those computations need to be implemented, developing analytical methods and numerical algorithms to solve the resulting constrained optimization problems, and relating these solutions to biological substrates. However, only a relatively small fraction of these studies attempted to make specific predictions about, and thus interpret in normative terms, the cellular-level electrophysiological properties of individual neurons or synapses. Small in numbers it may be, the potential impact of this particular line of research cannot be ignored as such theories may provide a way to bridge the gap between the cellular-molecular and the systems-level branches of neuroscience by connecting low-level properties of the nervous system to its high-level functions. Our workshop aims to highlight and discuss recent work in this field. Since much of the theoretical background in this field has been adopted from information theory, machine learning, and related fields, we expect that not only experimental and computational neuroscientists, but also machine learning researchers will be interested in the general topic and the specific talks.
Author Information
Jean-Pascal Pfister (Cambridge University)
Mate Lengyel (University of Cambridge)
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2022 Poster: Training stochastic stabilized supralinear networks by dynamics-neutral growth »
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2021 Poster: A universal probabilistic spike count model reveals ongoing modulation of neural variability »
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2018 Poster: Exact natural gradient in deep linear networks and its application to the nonlinear case »
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2016 Poster: Efficient state-space modularization for planning: theory, behavioral and neural signatures »
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2014 Poster: Analog Memories in a Balanced Rate-Based Network of E-I Neurons »
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2014 Poster: A Dual Algorithm for Olfactory Computation in the Locust Brain »
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2014 Oral: Analog Memories in a Balanced Rate-Based Network of E-I Neurons »
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2014 Poster: Fast Sampling-Based Inference in Balanced Neuronal Networks »
Guillaume Hennequin · Laurence Aitchison · Mate Lengyel -
2013 Poster: Correlations strike back (again): the case of associative memory retrieval »
Cristina Savin · Peter Dayan · Mate Lengyel -
2013 Oral: Correlations strike back (again): the case of associative memory retrieval »
Cristina Savin · Peter Dayan · Mate Lengyel -
2011 Session: Oral Session 11 »
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2011 Poster: Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories »
Cristina Savin · Peter Dayan · Mate Lengyel -
2011 Poster: Sequence learning with hidden units in spiking neural networks »
Johanni Brea · Walter Senn · Jean-Pascal Pfister -
2011 Spotlight: Sequence learning with hidden units in spiking neural networks »
Johanni Brea · Walter Senn · Jean-Pascal Pfister -
2011 Poster: Active dendrites: adaptation to spike-based communication »
Balazs B Ujfalussy · Mate Lengyel -
2011 Spotlight: Active dendrites: adaptation to spike-based communication »
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2009 Poster: Know Thy Neighbour: A Normative Theory of Synaptic Depression »
Jean-Pascal Pfister · Peter Dayan · Mate Lengyel -
2009 Oral: Know Thy Neighbour: A Normative Theory of Synaptic Depression »
Jean-Pascal Pfister · Peter Dayan · Mate Lengyel -
2007 Oral: Hippocampal Contributions to Control: The Third Way »
Mate Lengyel · Peter Dayan -
2007 Poster: Hippocampal Contributions to Control: The Third Way »
Mate Lengyel · Peter Dayan -
2006 Poster: Uncertainty, phase and oscillatory hippocampal recall »
Mate Lengyel · Peter Dayan -
2006 Talk: Uncertainty, phase and oscillatory hippocampal recall »
Mate Lengyel · Peter Dayan