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Learning by the Dendritic Prediction of Somatic Spiking
Open accessAbstract
Recent modeling of spike-timing-dependent plasticity indicates that plasticity involves as a third factor a local dendritic potential, besides pre- and postsynaptic firing times. We present a simple compartmental neuron model together with a non-Hebbian, biologically plausible learning rule for dendritic synapses where plasticity is modulated by these three factors. In functional terms, the rule seeks to minimize discrepancies between somatic firings and a local dendritic potential. Such prediction errors can arise in our model from stochastic fluctuations as well as from synaptic input, which directly targets the soma. Depending on the nature of this direct input, our plasticity rule subserves supervised or unsupervised learning. When a reward signal modulates the learning rate, reinforcement learning results. Hence a single plasticity rule supports diverse learning paradigms.
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“One way to biologically implement the hippocampal replay as a target or 'teaching signal' for the FF neurons is to 'nudge' these neurons in the soma, while the synapses projecting from the FF pathway to their dendrites learn to reproduce the somatic nudging [59].”
From How Adversarial REM Dreams May Facilitate Creativity, and Why We Become Aware of Them (2024) · cites this paper as [59]
In the citing paper
“Interestingly, such sub-networks might even be part of the same neuron and map onto “apical” vs. “basal” parts of the dendritic tree (Körding and König, 2001; Urbanczik and Senn, 2014).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Urbanczik and Senn, 2014
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Synaptic Plasticity Forms and Functions
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“, 2010), a Hebbian two-factor rule if used in unsupervised learning (Urbanczik and Senn, 2014), but the same rule must be seen as a three-factor rule with a neuron-specific (non-global) third factor in the supervised setting.”
BackgroundMethodInfluentialIlluminating dendritic function with computational models
Poirazi, P., & Papoutsi, A. (2020) · Nature reviews. Neuroscience · doi:10.1038/s41583-020-0301-7 · Cited by 313
Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)
Kaiser, J., Mostafa, H., & Neftci, E. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00424 · Cited by 331
“These types of rules are often termed three-factor rules, which have been shown to be consistent with biology [19] while being compatible with a wide number of unsupervised, supervised and reinforcement learning paradigms [25].”
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Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware
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“Reference [235] proposes to incorporate a third factor of local dendritic potential besides pre-and postsynaptic activity to modulated plasiticity.”
BackgroundControl of synaptic plasticity in deep cortical networks
Roelfsema, P. R., & Holtmaat, A. J. D. G. (2018) · Nature reviews. Neuroscience · doi:10.1038/nrn.2018.6 · Cited by 254
Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines
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“The two compartment neuron model used in this work is motivated from conductance-based dynamics in [Urbanczik and Senn, 2014] and previous neuromorphic realizations of two compartment mixed signal spiking neurons [Park et al., 2014].”
BackgroundMethodInfluentialFunctional clustering of dendritic activity during decision-making
Kerlin, A., Mohar, B., Flickinger, D., MacLennan, B. J., Dean, M. B., Davis, C., Spruston, N., & Svoboda, K. (2019) · eLife · doi:10.7554/elife.46966 · Cited by 186
Dendritic solutions to the credit assignment problem
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Efficient Spike-Driven Learning With Dendritic Event-Based Processing
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“Recent work shows how spiking neural networks can implement feedback structures that allow efficient solving of the credit assignment problem by dendritic computation (Urbanczik and Senn, 2014; Wilmes et al., 2016; Bono and Clopath, 2017; Guerguiev et al., 2017).”
BackgroundThe Brain as an Efficient and Robust Adaptive Learner
Denève, S., Alemi, A., & Bourdoukan, R. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.016 · Cited by 143
Where is the error? Hierarchical predictive coding through dendritic error computation
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InfluentialAn integrative, multiscale view on neural theories of consciousness
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Self-citationToward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119
“…a single complex neuron (in effect backpropagating from the net somatic output, through nonlinearities at the dendritic branch points, all the way back to the individual input synaptic weights) and that generalize to a reinforcement learning context (Urbanczik and Senn, 2014; Schiess et al., 2016).”
BackgroundEvent-driven random backpropagation: Enabling neuromorphic deep learning machines
Neftci, E. O., Augustine, C., Paul, S., & Detorakis, G. (2017) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2017.8050529 · Cited by 118
Biologically plausible deep learning — But how far can we go with shallow networks?
Illing, B., Wulfram Gerstner, B., & Brea, J. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.06.001 · Cited by 106
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“, predictive coding), with implications in supervised, unsupervised, and reinforcement learning setups [191].”
BackgroundModeling somatic and dendritic spike mediated plasticity at the single neuron and network level
Bono, J., & Clopath, C. (2017) · Nature Communications · doi:10.1038/s41467-017-00740-z · Cited by 113
Brain-Inspired Computing: A Systematic Survey and Future Trends
Li, G., Deng, L., Tang, H., Pan, G., Tian, Y., Roy, K., & Maass, W. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2024.3429360 · Cited by 113
“H) model) [50], even the dynamics existing not only in the soma but also in dendrites [51], [52], [53].”
InfluentialMulti-scale account of the network structure of macaque visual cortex
Schmidt, M., Bakker, R., Hilgetag, C. C., Diesmann, M., & van Albada, S. J. (2017) · Brain Structure and Function · doi:10.1007/s00429-017-1554-4 · Cited by 93
“This segregation can occur even in single cells that combine feedback and feedforward processing on their apical and basal dendrites (Körding and König 2001; Urbanczik and Senn 2014), again stressing the importance of taking cell morphologies into account.”
BackgroundA synaptic learning rule for exploiting nonlinear dendritic computation
Bicknell, B. A., & Häusser, M. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.044 · Cited by 90
“More recent models that incorporate additional coarse-grained compartments to represent a dendritic tree have begun to reveal a variety of enhancements that may be accessible with a spatially extended morphology (Wu and Mel, 2009; Legenstein and Maass, 2011; O’Donnell and Sejnowski, 2014; Urbanczik and Senn, 2014; Sch…”
BackgroundPedersen, J. E., Abreu, S., Jobst, M., Lenz, G., Fra, V., Bauer, F. C., Muir, D. R., Zhou, P., Vogginger, B., Heckel, K., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52259-9 · Cited by 82
“ThecurrentversionofNIRexcludesotherimportantmechanismssuch as adaptive threshold mechanisms 69 , gating 70,71 , resonate-and-fi re 62,72 , and multicompartmental 73,74 neuron models.”
BackgroundPredicting non-linear dynamics by stable local learning in a recurrent spiking neural network
Gilra, A., & Wulfram Gerstner, B. (2017) · eLife · doi:10.7554/elife.28295 · Cited by 95
“(Burbank, 2015), or the output readout weights are learned, starting with random encoding weights, via a biological perceptron-like learning rule (D’Souza et al., 2010; Urbanczik and Senn, 2014).”
BackgroundMethodInfluentialAre Dendrites Conceptually Useful?
Larkum, M. E. (2022) · Neuroscience · doi:10.1016/j.neuroscience.2022.03.008 · Cited by 84
Deep Learning without Weight TransportPreprint
Akrout, M., Wilson, C., Humphreys, P. C., Lillicrap, T., & Tweed, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.05391 · Cited by 101
Yang, S., Gao, T. Y., Wang, J., Deng, B., Azghadi, M. R., Lei, T., & Linares-Barranco, B. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.850945 · Cited by 78
“Urbanczik and Senn (2014) presented an SNN model based on learning with dendritic spike prediction.”
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Frenkel, C., Lefebvre, M., & Bol, D. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.629892 · Cited by 92
“…backward information in somatic and dendritic compartments, a property that is highlighted in the formulation of three-factor synaptic plasticity rules (Urbanczik and Senn, 2014): pre-synaptic and post-synaptic activities are modulated by a third factor corresponding to a local dendritic voltage.”
BackgroundClasses of dendritic information processing
Payeur, A., Béïque, J.-C., & Naud, R. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.07.006 · Cited by 85
“This apparent paradox can be resolved if nonlinear mecha-nismsselectpart ofthecompartmentalized informationand transiently facilitate its communication to the cell body [6,9,10,30,31,32,33,34,35,36 ].”
BackgroundDynamic compartmental computations in tuft dendrites of layer 5 neurons during motor behavior
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Spike-driven multi-scale learning with hybrid mechanisms of spiking dendrites
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Spike-based local synaptic plasticity: a survey of computational models and neuromorphic circuits
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“In this case, there is never a zero weight change [68].”
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Neftci, E. (2018) · iScience · doi:10.1016/j.isci.2018.06.010 · Cited by 52
Redundancy in synaptic connections enables neurons to learn optimally
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“Previous computational studies on dendritic computation have emphasized the importance of active dendritic process (24), especially for performing inference from correlated inputs (59), or for computation at terminal tufts of cortical layer 5 or CA1 neurons (40).”
BackgroundResultSupervised Learning in Spiking Neural Networks for Precise Temporal Encoding
Brian, G., & Grüning, A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0161335 · Cited by 49
“A detailed model of supervised learning in SNN has recently been proposed by [35], providing a strong mechanistic explanation for how such referent activity templates might be used to drive the learning of desired postsynaptic activity patterns.”
BackgroundDendritic Computing: Branching Deeper into Machine Learning
Acharya, J., Basu, A., Legenstein, R. A., Limbacher, T., Poirazi, P., & Wu, X. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.10.001 · Cited by 54
Spiking neural networks for inference and learning: a memristor-based design perspective
Fouda, M. E., Kurdahi, F. J., Eltawil, A. M., & Neftci, E. (2020) · Elsevier eBooks · doi:10.1016/b978-0-08-102782-0.00019-8 · Cited by 45
Ebner, C., Clopath, C., Jedlička, P., & Cuntz, H. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.11.068 · Cited by 43
Learning probabilistic neural representations with randomly connected circuits
Maoz, O., Tkačik, G., Esteki, M. S., Kiani, R., & Schneidman, E. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912804117 · Cited by 44
“Updating the synaptic weights to the output neuron would require a back-propagating signal from the cell body [4] and a mechanism that would allow comparing between the synapses’ current and recent activities [5], short-term memory within cells [6, 7], or more complicated local synaptic computations [8].”
Predictive coding networks for temporal prediction
Millidge, B., Tang, M., Osanlouy, M., Harper, N. S., & Bogacz, R. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011183 · Cited by 44
Emulating Dendritic Computing Paradigms on Analog Neuromorphic Hardware
Kaiser, J., Billaudelle, S., Müller, E., Tetzlaff, C., Schemmel, J., & Schmitt, S. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.08.013 · Cited by 47
“…factors such as their morphology, ion channel distribution and the spatio-temporal distribution of the inputs themselves and therefore offers the possibility to perform a large set of different computations (Vetter et al., 2001; Schaefer et al., 2003; Williams and Stuart, 2002; Polsky et al., 2004).”
BackgroundDendritic processing of spontaneous neuronal sequences for single-trial learning
Haga, T., & Fukai, T. (2018) · Scientific Reports · doi:10.1038/s41598-018-33513-9 · Cited by 43
Predictive olfactory learning in Drosophila
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“1 is the nudging strength of the postsynaptic teaching signal [44]).”
BackgroundSelf-citationProspective Coding by Spiking Neurons
Brea, J., Gaál, A. T., Urbanczik, R., & Senn, W. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005003 · Cited by 48
“These terms would cancel each other in the case of Urbanczik & Senn [18] where α = λ = 1 and g PSPi 1⁄4 PSPi.”
BackgroundMethodResultInfluentialSelf-citationLocal plasticity rules can learn deep representations using self-supervised contrastive predictionsPreprint
Illing, B., Ventura, J., Bellec, G., & Wulfram Gerstner, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.08262 · Cited by 42
A GPU-based computational framework that bridges neuron simulation and artificial intelligence
Zhang, Y., He, G., Ма, Л., Liu, X., Hjorth, J. J. J., Kozlov, A., He, Y., Zhang, S., Kotaleski, J. H., Tian, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41553-7 · Cited by 40
“Our open-source learning framework can bereadily integrated with other dendritic learning rules, such as learning rules for nonlinear (full-active) dendrites 21 , burst-dependent synaptic plasticity 20 , and learning with spike prediction 36 .”
Electrical Compartmentalization in Neurons
Wybo, W. A. M., Torben-Nielsen, B., Nevian, T., & Gewaltig, M.-O. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.01.074 · Cited by 41
“…of feature associations (Bono and Clopath, 2017), represent a powerful mechanism for coincidence detection (Chua and Morrison, 2016; Larkum et al., 1999), and support the back-prop algorithm to train neural networks (Guergiuev et al., 2017; Sacramento et al., 2017; Urbanczik and Senn, 2014).”
A differential memristive synapse circuit for on-line learning in neuromorphic computing systems
Nair, M. V., Müller, L. K., & Indiveri, G. (2017) · Nano Futures · doi:10.1088/2399-1984/aa954a · Cited by 39
Brivio, S., Ly, D. R. B., Vianello, E., & Spiga, S. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.580909 · Cited by 37
“Among the spike-based training procedures, supervised learning rules inspired by the back-propagation exist (Urbanczik and Senn, 2014; Müller et al., 2017; Donati et al., 2019), which are seldom investigated for systems including realistic simulations of memristive devices (Nair et al., 2017;…”
Does computational neuroscience need new synaptic learning paradigms?
Brea, J., & Wulfram Gerstner, B. (2016) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2016.05.012 · Cited by 39
Somatodendritic consistency check for temporal feature segmentation
Asabuki, T., & Fukai, T. (2020) · Nature Communications · doi:10.1038/s41467-020-15367-w · Cited by 36
Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell
Moldwin, T., & Segev, I. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00033 · Cited by 37
“…treat clustering of nearby synapses, and “structural plasticity,” or the relocation of synaptic inputs within and between branches as crucial for learning (Trachtenberg et al., 2002; Larkum and Nevian, 2008; Losonczy et al., 2008; Kastellakis et al., 2015; Weber et al., 2016; Mel et al., 2017).”
BackgroundData-driven reduction of dendritic morphologies with preserved dendro-somatic responses
Wybo, W. A. M., Jordan, J., Ellenberger, B., Mengual, U. M., Nevian, T., & Senn, W. (2021) · eLife · doi:10.7554/elife.60936 · Cited by 35
“Finally, we change the average voltage level achieved by the combined AMPA and GABA input by changing the ‘nudging potential’ en (Urbanczik and Senn, 2014)”
MethodSelf-citationDendritic error backpropagation in deep cortical microcircuitsPreprint
Sacramento, J., Costa, R. P., Bengio, Y., & Senn, W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.00062 · Cited by 54
“ough weights WPI k,k (see Fig. 1A). Additionally, cross-area feedback onto SST cells originating at the next higher brain area k+1 provide a weak nudging signal for these interneurons, modelled after Urbanczik and Senn (2014) as a conductance-based so4 matic input current. For computational simplicity, we modelled thi…”
InfluentialSelf-citationNeural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
Detorakis, G., Sheik, S., Augustine, C., Paul, S., Pedroni, B. U., Dutt, N. D., Krichmar, J. L., Cauwenberghs, G., & Neftci, E. O. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00583 · Cited by 33
“On the other hand, three factor rules solve this problem by adding a factor indicative of reward, error, gradients provided extrinsically or through other neural states (Clopath et al., 2010; Urbanczik and Senn, 2014).”
BackgroundMethodInfluentialSpike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons
Covi, E., George, R., Frascaroli, J., Brivio, S., Mayr, C., Mostafa, H., Indiveri, G., & Spiga, S. (2018) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/aad361 · Cited by 33
“New learning schemes involving spike rate [32, 33] and new plasticity models taking the rate of the synaptic activity into account [34 – 36] have therefore been proposed.”
BackgroundQuantum superposition inspired spiking neural network
Sun, Y., Zeng, Y., & Zhang, T. (2021) · iScience · doi:10.1016/j.isci.2021.102880 · Cited by 37
“Parameter Θ controls the proportion of original image and reverse image in background color inverse images with Learning procedure of the QS-SNN algorithm Dendrite prediction (Urbanczik and Senn, 2014) and proximal gradient methods are used for tuning of QS-SNN.”
BackgroundMethodContinual Learning in a Multi-Layer Network of an Electric Fish
Muller, S. Z., Zadina, A. N., Abbott, L. F., & Sawtell, N. B. (2019) · Cell · doi:10.1016/j.cell.2019.10.020 · Cited by 33
“Related twocompartment neuronal models have been proposed on theoretical grounds as biologically plausible implementations of machine-learning schemes in cerebral cortical circuits (Guerguiev et al., 2017; Kording and Konig, 2001; Marblestone et al., 2016; Schiess et al., 2016; Urbanczik and Senn, 2014).”
BackgroundPredictive Processing: A Circuit Approach to Psychosis
Keller, G. B., & Sterzer, P. (2024) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-100223-121214 · Cited by 45
Attractor network dynamics enable preplay and rapid path planning in maze-like environments
Corneil, D., & Wulfram Gerstner, B. (2015) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 30
Evolving interpretable plasticity for spiking networks
Jordan, J., Schmidt, M., Senn, W., & Petrovici, M. A. (2021) · eLife · doi:10.7554/elife.66273 · Cited by 30
“For the purpose of this study, we implemented a reward-driven (Urbanczik and Senn, 2009) and an errordriven learning rule (Equation 7; Urbanczik and Senn, 2014), as well as a homeostatic STDP rule (Equation 17; Masquelier, 2018) via custom C++ code.”
Self-citationLearning cortical representations through perturbed and adversarial dreaming
Deperrois, N., Petrovici, M. A., Senn, W., & Jordan, J. (2022) · eLife · doi:10.7554/elife.76384 · Cited by 28
“…in cortical networks through different classes of pyramidal neurons with a biological version of supervised learning based on a dendritic prediction of somatic activity (Urbanczik and Senn, 2014), and a corresponding global modulation of synaptic plasticity by state-specific neuromodulators.”
BackgroundMethodSelf-citationCapone, C., Lupo, C., Muratore, P., & Paolucci, P. S. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2220743120 · Cited by 27
Learning accurate path integration in ring attractor models of the head direction system
Vafidis, P., Owald, D., D’Albis, T., & Kempter, R. (2022) · eLife · doi:10.7554/elife.69841 · Cited by 29
Omission responses in local field potentials in rat auditory cortex
Auksztulewicz, R., Rajendran, V. G., Peng, F., Schnupp, J. W. H., & Harper, N. S. (2023) · BMC Biology · doi:10.1186/s12915-023-01592-4 · Cited by 26
Functional architecture of intracellular oscillations in hippocampal dendrites
Liao, Z., Gonzalez, K. C., Li, D. M., Yang, C. M., Holder, D. J., McClain, N. E., Zhang, G., Evans, S. W., Chavarha, M., Simko, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50546-z · Cited by 31
Remme, M. W. H., Bergmann, U., Alevi, D., Schreiber, S., Sprekeler, H., & Kempter, R. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009681 · Cited by 28
“This can be achieved by STDP in combination with longer transmission delays in the indirect pathway, as suggested here, but other neural implementations of supervised learning may be equally suitable [42, 43, 70].”
BackgroundOn-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor
Stewart, K. M., Orchard, G., Shrestha, S. B., & Neftci, E. O. (2020) · IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) · doi:10.1109/aicas48895.2020.9073948 · Cited by 24
“Such rules are compatible with a wide number of unsupervised, supervised, and reinforcement learning paradigms [11], and implementations can have scaling properties comparable to that of STDP [12].”
BackgroundMercier, M. S., Magloire, V., Cornford, J. H., & Kullmann, D. M. (2022) · The Journal of Physiology · doi:10.1113/jp282753 · Cited by 23
“Payeur et al., 2021; Richards & Lillicrap, 2019; Sacramento et al., 2018; Urbanczik & Senn, 2014).”
Structural plasticity on an accelerated analog neuromorphic hardware system
Billaudelle, S., Cramer, B., Petrovici, M. A., Schreiber, K., Kappel, D., Schemmel, J., & Meier, K. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.09.024 · Cited by 29
Frenkel, C., Bol, D., & Indiveri, G. (2021) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-217499 · Cited by 24
Learning cortical representations through perturbed and adversarial dreaming.
Deperrois, N., Petrovici, M. A., Senn, W., & Jordan, J. (2022) · doi:10.48350/169088 · Cited by 24
Self-citationCredit Assignment in Neural Networks through Deep Feedback Control
Meulemans, A., Farinha, M. T., Ordóñez, J. G., Aceituno, P. V., Sacramento, J., & Grewe, B. F. (2021) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000529286 · Cited by 24
A neuronal least-action principle for real-time learning in cortical circuits
Senn, W., Dold, D., Kungl, Á. F., Ellenberger, B., Jordan, J., Bengio, Y., Sacramento, J., & Petrovici, M. A. (2024) · eLife · doi:10.1101/2023.03.25.534198 · Cited by 25
“Each network neuron performs gradient descent on its own dendritic prediction error (Urbanczik & Senn, 2014), and with this eventually also contributes to a reduction of the output errors.”
InfluentialSelf-citationBhasin, B. J., Raymond, J. L., & Goldman, M. S. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2406010121 · Cited by 21
Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity
Albers, C., Westkott, M., & Pawelzik, K. R. (2016) · PLoS ONE · doi:10.1371/journal.pone.0148948 · Cited by 21
“Also, it is worth noting that the model of Urbanczik and Senn requires a one-way barrier to prevent somatic voltage invading the dendrites; in contrast, MPDP requires a strong two-way coupling between somatic and dendritic/synaptic voltage.”
BackgroundInfluentialOn the relationship between predictive coding and backpropagation
Rosenbaum, R. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266102 · Cited by 20
“Backpropagation can be performed using local updates if gradients of neurons’ activations are passed upstream through feedback connections, but this interpretation implies other biologically implausible properties of the”
BackgroundDendrites and efficiency: Optimizing performance and resource utilization
Makarov, R., Pagkalos, M., & Poirazi, P. (2023) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2023.102812 · Cited by 24
“A mismatch between the apical prediction signal and somatic spiking results in top-down prediction error [47].”
Sun, Y., Zhao, F., Zhao, Z., & Zeng, Y. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106898 · Cited by 24
“…employ multi-compartment neuron (MCN) models for tasks involving simple stimulus classification and image recognition, such as MNIST images (Urbanczik and Senn, 2014; Sacramento, Ponte Costa, Bengio and Senn, 2018) and neuron spiking sequences (Zhang, Yang, Ma, Wu, Li and Tan, 2024; Li,…”
Noise Gated by Dendrosomatic Interactions Increases Information Transmission
Naud, R., Payeur, A., & Longtin, A. (2017) · Physical Review X · doi:10.1103/physrevx.7.031045 · Cited by 18
Frenkel, C., Lefebvre, M., & Bol, D. (2019) · Cited by 16
Geng, H.-Y., Arbuthnott, G. W., Yung, W., & Ke, Y. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab460 · Cited by 19
Gao, T. Y., Deng, B., Wang, J., & Yi, G. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.929644 · Cited by 16
“Urbanczik and Senn (2014) minimize the discrepancies between somatic firings and local dendritic potential to train their SNN.”
BackgroundSpecific connectivity optimizes learning in thalamocortical loops
Lakshminarasimhan, K. J., Xie, M., Cohen, J. D., Sauerbrei, B., Hantman, A. W., Litwin-Kumar, A., & Escola, G. S. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114059 · Cited by 18
“…in the cortex might undergo more sophisticated forms of plasticity by leveraging the complex dendritic machinery involving distinct apical and basal compartments, 49,70 such structures are absent in the thalamus and therefore cannot be used to perform credit assignment in corticothalamic synapses.”
BackgroundLearning efficient backprojections across cortical hierarchies in real time
Max, K., Kriener, L., García, G. P., Nowotny, T., Jaras, I., Senn, W., & Petrovici, M. A. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00845-3 · Cited by 20
“In this model, weights are adapted using local dendritic plasticity rules in the form ̇ W = η [ φ ( u ) − φ ( v )] r T , where W represents lateral or feed-forwards weights, η is a learning rate, u and v denote different compartmental voltages and r the presynaptic rate 63,64 .”
Self-citationLearning to represent signals spike by spike
Brendel, W., Bourdoukan, R., Vertechi, P., Machens, C. K., & Denève, S. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007692 · Cited by 25
“In the future, these ideas may be combined with explicit single-neuron models [53] to turn local learning rules into global functions [21,52,54].”
Embodied Synaptic Plasticity With Online Reinforcement Learning
Kaiser, J., Hoff, M., Konle, A., Tieck, J. C. V., Kappel, D., Reichard, D., Subramoney, A., Legenstein, R. A., Roennau, A., Maass, W., et al. (2019) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00081 · Cited by 14
“2 Closed-Loop Embodiment Implementation Usually, synaptic learning rules are solely evaluated on open-loop pattern classification tasks [46, 32, 35, 41].”
BackgroundInfluentialNeural circuit mechanisms of hierarchical sequence learning tested on large-scale recording data
Asabuki, T., Kokate, P., & Fukai, T. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010214 · Cited by 22
A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis
Lipshutz, D., Bahroun, Y., Golkar, S., Sengupta, A. M., & Chklovskii, D. B. (2021) · Neural Computation · doi:10.1162/neco_a_01414 · Cited by 17
“Multi-compartmental models of pyramidal neurons have been invoked previously in the context of biological implementation of the backpropagation algorithm [26, 48, 21, 22, 44, 43].”
BackgroundMethodParallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation
Harkin, E. F., Shen, P. R., Goel, A., Richards, B. A., & Naud, R. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.07.026 · Cited by 15
Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausiblePreprint
Bengio, Y., Scellier, B., Bilaniuk, O., Sacramento, J., & Senn, W. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.01651 · Cited by 22
“23 can also be seen in the context of dendritic predictions of somatic spiking (Urbanczik and Senn, 2014).”
BackgroundMethodInfluentialSelf-citationAlternating Excitation–Inhibition Dendritic Computing for Classification
Li, J., Lei, Z., Zhang, Z., Li, H., Todo, Y., & Gao, S. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3416236 · Cited by 15
Moldwin, T., Kalmenson, M., & Segev, I. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009015 · Cited by 16
Predictive coding with spiking neurons and feedforward gist signaling
Lee, K., Dora, S., Mejias, J. F., Bohte, S. M., & Pennartz, C. M. A. (2024) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2024.1338280 · Cited by 14
Metadata
- Authors
- Urbanczik, R., & Senn, W.
- Affiliations
- University of Bern (CH)
- Corresponding author
- Senn, Walter
- ORCID
- Senn, Walter
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2014-02
- Volume
- 81
- Issue
- 3
- Pages
- 521-528
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W2006814548
- Cited by
- 230
- References
- 31
- Citations by year
- 2026: 14 · 2025: 19 · 2024: 44 · 2023: 19 · 2022: 23 · 2021: 30 · 2020: 29 · 2019: 11 · 2018: 12 · 2017: 13 · 2016: 11 · 2015: 3 · 2014: 2
- FWCI
- 5.38
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Bern Open Repository and Information System (University of Bern) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Keywords
- spike-timing-dependent plasticity, dendritic potentials, compartmental neuron model, dendritic synapses, prediction error, stochastic fluctuations, supervised learning, unsupervised learning, reinforcement learning
- MeSH terms
- Action Potentials, Animals, Computer Simulation, Dendrites, Learning, Models, Neurological, Neuronal Plasticity, Neurons, Synapses, Time Factors
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