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Dendritic error backpropagation in deep cortical microcircuits
[article]
2017
arXiv
pre-print
Preserved Fulltext
We demonstrate the learning capabilities of the model in a number of tasks and show that it approximates the classical error backpropagation algorithm. ...
Our framework makes several experimental predictions on the function of dendritic integration and cortical microcircuits, is consistent with recent observations of cross-area learning, and suggests a biological ...
WS thanks Matthew Larkum for many discussions on dendritic processing and the option of dendritic error representation. In addition, JS thanks Elena Kreutzer, Pascal Leimer and Martin T. ...
arXiv:1801.00062v1
fatcat:uruwi5mq3bgxdl7fgeokof55dm
Dendritic cortical microcircuits approximate the backpropagation algorithm
[article]
2018
arXiv
pre-print
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In contrast to previous work our model does not require separate phases and synaptic learning is driven by local dendritic prediction errors continuously in time. ...
Moreover, our framework is consistent with recent observations of learning between brain areas and the architecture of cortical microcircuits. ...
Supplementary Material: Dendritic cortical microcircuits approximate the backpropagation algorithm The dendritic cortical circuit learns to predict self-generated top-down input Figure S1 : Dendritic ...
arXiv:1810.11393v1
fatcat:jetulqdayrdrtnbh4ejzfqtusq
A Computational Framework of Cortical Microcircuits Approximates Sign-concordant Random Backpropagation
[article]
2022
arXiv
pre-print
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In this work, we answer the question of whether it is possible to realize random backpropagation solely based on mechanisms observed in neuroscience. ...
Comprising three types of cells and two types of synaptic connectivity, the proposed microcircuit architecture computes and propagates error signals through local feedback connections and supports the ...
Conclusion In this work, we construct a novel framework to implement bio-plausible random error backpropagation. ...
arXiv:2205.07292v1
fatcat:ewb5jxuw6bho3fyinfnkdxoydu
Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece
2022
Neurons, Behavior, Data analysis, and Theory
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We conclude with recommendations for both theoretical and experimental neuroscientists when designing new studies that could help bring clarity to these issues. a recurrent neural networks, backpropagation ...
The key issues discussed in this review include: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions. ...
Backpropagation through time and the brain. Current opinion in neurobiology 2019;55:82-89. [2] Jozefowicz R, Zaremba W, Sutskever I. An empirical exploration of recurrent network architectures. ...
doi:10.51628/001c.35302
fatcat:ahqqn4rpw5dw3mu3n5tzb2auqq
Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2105.05382v1 fatcat:4pruygtpn5gs3fn47qjf3sfxzu
The key issues discussed in this review include: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions. ...
We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent ...
We would also like to thank all other audience members from around the world who participated on the day in the chat, and everybody else who has watched the replay since it was posted online. ...
arXiv:2105.05382v2
fatcat:7vyo56k4rva7vdskumr6zxer4i
A simple normative network approximates local non-Hebbian learning in the cortex
[article]
2020
arXiv
pre-print
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We detail how, in our model, the calcium plateau potential can be interpreted as a backpropagating error signal. ...
Neuroscience experiments demonstrate that the processing of sensory inputs by cortical neurons is modulated by instructive signals which provide context and task-relevant information. ...
• We demonstrate within the confines of our model how the calcium plateau potential in cortical microcircuits encodes a backpropagating error signal. • We show numerically on a real-world dataset that ...
arXiv:2010.12660v1
fatcat:ly5hr4mr2zc2tkg2pwddzsowfm
Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons
[article]
2021
arXiv
pre-print
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The resulting model can be interpreted as a biologically plausible approximation of error backpropagation in deep cortical networks with continuous-time, leaky neuronal dynamics and continuously active ...
In hierarchical models of cortical networks each layer thus introduces a response lag. ...
Within the context of LE, the microcircuit model used to implement error backpropagation carries several implications for cortical phenomenology beyond specific connectivity patterns. ...
arXiv:2110.14549v1
fatcat:gbypwhc4rfcjbh5s5qlm7ds67a
Toward an Integration of Deep Learning and Neuroscience
2016
Frontiers in Computational Neuroscience
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Here we think about the brain in terms of these ideas. ...
In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively ...
Some cortical microcircuits 62 It would be interesting to study these questions in specific brain systems. ...
doi:10.3389/fncom.2016.00094
pmid:27683554
pmcid:PMC5021692
fatcat:yikwc4h5yvfj7gwzlimtw5n6ai
Towards an integration of deep learning and neuroscience
[article]
2016
arXiv
pre-print
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Here we think about the brain in terms of these ideas. ...
In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively ...
We thank Miles Brundage for an excellent Twitter feed of deep learning papers. ...
arXiv:1606.03813v1
fatcat:tmmholydqbcplbc5ihg76yip6e
Towards an integration of deep learning and neuroscience
[article]
2016
bioRxiv
pre-print
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Here we think about the brain in terms of these ideas. ...
In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively ...
Some cortical microcircuits could then, perhaps, compute the cost functions that are to be delivered to other cortical microcircuits. ...
doi:10.1101/058545
fatcat:4ryejpe2tnf7dgoaqhoastoiya
Linking canonical microcircuits and neuronal activity: Dynamic causal modelling of laminar recordings
2017
NeuroImage
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This is particularly interesting from the perspective of predictive coding, where neuromodulators are thought to boost prediction errors that ascend the cortical hierarchy. ...
) superficial layer activity relative to deep layers. ...
To ensure the prior constraints properly accommodate spatiotemporal dynamics within the cortical microcircuit and its neuronal compartments (e.g. delays due to spread of current throughout the dendritic ...
doi:10.1016/j.neuroimage.2016.11.041
pmid:27871922
pmcid:PMC5312791
fatcat:vgvd2vfkr5hj7lmpvkq6zwdcvy
Relaxing the Constraints on Predictive Coding Models
[article]
2020
arXiv
pre-print
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for training deep networks. ...
While motivated by high-level notions of variational inference, detailed neurophysiological models of cortical microcircuits which can implements its computations have been developed. ...
BM would additionally like to thank Mycah Banks for her invaluable contribution in preparing the figures for this manuscript. ...
arXiv:2010.01047v2
fatcat:armb7kpybfgjbeoba2y3apdraa
A biologically plausible neural network for multi-channel Canonical Correlation Analysis
[article]
2021
arXiv
pre-print
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Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. ...
We explore the possibility that cortical microcircuits implement Canonical Correlation Analysis (CCA), an unsupervised learning method that projects the inputs onto a common subspace so as to maximize ...
is in contrast to cortical microcircuits where lateral influence between cortical pyramidal neurons is often indirect and mediated by interneurons. ...
arXiv:2010.00525v4
fatcat:ttpnnb7kfnablmldvfh3wxs4t4
A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis
2021
Neural Computation
Preserved Fulltext
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Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. ...
We explore the possibility that cortical microcircuits implement canonical correlation analysis (CCA), an unsupervised learning method that projects the inputs onto a common subspace so as to maximize ...
is in contrast to cortical microcircuits where lateral influence between cortical pyramidal neurons is often indirect and mediated by interneurons. ...
doi:10.1162/neco_a_01414
pmid:34412114
fatcat:nqx6a4koovb6dg7nfq6cpzlttq
Anatomy and physiology of the thick-tufted layer 5 pyramidal neuron
2015
Frontiers in Cellular Neuroscience
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The thick-tufted layer 5 (TTL5) pyramidal neuron is one of the most extensively studied neuron types in the mammalian neocortex and has become a benchmark for understanding information processing in excitatory ...
By virtue of having the widest local axonal and dendritic arborization, the TTL5 neuron encompasses various local neocortical neurons and thereby defines the dimensions of neocortical microcircuitry. ...
Yun Wang for morphological reconstructions used in Figure 2 . This work was supported by the Blue Brain Project, EPFL, Switzerland. ...
doi:10.3389/fncel.2015.00233
pmid:26167146
pmcid:PMC4481152
fatcat:f5mmqcdfmjfflkqcqnjdczcbcu
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