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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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.
“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]
Such sub-networks might even be part of the same neuron and map onto apical vs. basal parts of the dendritic tree.
“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
Cites
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270 · see citing works
Self-organized formation of topologically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529 · see citing works
Short-Term Synaptic Plasticity
Zucker, R. S., & Regehr, W. G. (2002) · Annual Review of Physiology · doi:10.1146/annurev.physiol.64.092501.114547 · Cited by 5,068 · see citing works
Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs
Markram, H., Lübke, J., Frotscher, M., & Sakmann, B. (1997) · Science · doi:10.1126/science.275.5297.213 · Cited by 3,832 · see citing works
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
Jaeger, H., & Haas, H. (2004) · Science · doi:10.1126/science.1091277 · Cited by 3,811 · see citing works
Self-organized formation of topographically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · Cited by 2,995 · see citing works
Competitive Hebbian learning through spike-timing-dependent synaptic plasticity
Song, S., Miller, K. D., & Abbott, L. F. (2000) · Nature Neuroscience · doi:10.1038/78829 · Cited by 2,738 · see citing works
Phase relationship between hippocampal place units and the EEG theta rhythm
O'Keefe, J. M., & Recce, M. L. (1993) · Hippocampus · doi:10.1002/hipo.450030307 · Cited by 2,561 · see citing works
The distinct modes of vision offered by feedforward and recurrent processing
Lamme, V. A. F., & Roelfsema, P. R. (2000) · Trends in Neurosciences · doi:10.1016/s0166-2236(00)01657-x · Cited by 2,338 · cited 1× · see citing works
“This provides an angle on longstanding discussions on the relative importance of fast feedforward versus slower recurrent processing in the brain (Lamme and Roelfsema, 2000; Ganguli and Latham, 2009).”
Synaptic plasticity: taming the beast
Abbott, L. F., & Nelson, S. B. (2000) · Nature Neuroscience · doi:10.1038/81453 · Cited by 2,263 · cited 1× · see citing works
“Theoretical studies on the function of STDP have mostly assumed point neurons (Abbott and Nelson, 2000; Song et al., 2000; Kempter et al., 2001; Gütig et al., 2003).”
Artola, A., Bröcher, S., & Singer, W. (1990) · Nature · doi:10.1038/347069a0 · Cited by 880 · see citing works
Somogyi, P., Tamás, G., Luján, R., & Buhl, E. H. (1998) · Brain Research Reviews · doi:10.1016/s0165-0173(97)00061-1 · Cited by 860 · see citing works
Storage of spatial information by the maintenance mechanism of LTP
Pastalkova, E., Serrano, P., Pinkhasova, D., Wallace, E., Fenton, A. A., & Sacktor, T. C. (2006) · Science · doi:10.1126/science.1128657 · Cited by 848 · see citing works
Riesenhuber, M., & Poggio, T. (2000) · Nature Neuroscience · doi:10.1038/81479 · Cited by 658 · see citing works
Claudia Clopath, A., Buesing, L., Vasilaki, E., & Gerstner, W. (2009) · Nature Neuroscience · doi:10.1038/nn.2479 · Cited by 605 · see citing works
Independent rate and temporal coding in hippocampal pyramidal cells
Huxter, J. R., Burgess, N., & O'Keefe, J. M. (2003) · Nature · doi:10.1038/nature02058 · Cited by 594 · see citing works
Learning Input Correlations through Nonlinear Temporally Asymmetric Hebbian Plasticity
Gütig, R., Aharonov, R., Rotter, S., & Sompolinsky, H. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-09-03697.2003 · Cited by 401 · see citing works
Tanaka, K. (2003) · Cerebral Cortex · doi:10.1093/cercor/13.1.90 · Cited by 329 · see citing works
Behavioral tagging is a general mechanism of long-term memory formation
Ballarini, F., Moncada, D., Martinez, M. C., Alen, N., & Viola, H. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0907078106 · Cited by 301 · see citing works
Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning
Pfister, J.-P., Toyoizumi, T., Barber, D., & Gerstner, W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.6.1318 · Cited by 246 · see citing works
Perceptual decision making in less than 30 milliseconds
Stanford, T. R., Shankar, S., Massoglia, D. P., Costello, M. G., & Salinas, E. (2010) · Nature Neuroscience · doi:10.1038/nn.2485 · Cited by 228 · see citing works
Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning
Kempter, R., Gerstner, W., & van Hemmen, J. L. (2001) · Neural Computation · doi:10.1162/089976601317098501 · Cited by 166 · see citing works
Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity
Frémaux, N., Sprekeler, H., & Gerstner, W. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.6249-09.2010 · Cited by 141 · see citing works
Frey, S., & Frey, J. U. (2008) · Progress in brain research · doi:10.1016/s0079-6123(07)00007-6 · Cited by 139 · see citing works
Voltage and spike timing interact in STDP - a unified model
Clopath (2010) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2010.00025 · Cited by 112 · see citing works
Reinforcement learning in populations of spiking neurons
Urbanczik, R., & Senn, W. (2008) · Nature Neuroscience · doi:10.1038/nn.2264 · Cited by 94 · see citing works
Self-citationPerceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity
D’Souza, P., Liu, S., & Hahnloser, R. H. R. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0909394107 · Cited by 30 · see citing works
Ganguli, S., & Latham, P. E. (2009) · Neuron · doi:10.1016/j.neuron.2009.02.006 · Cited by 16 · see citing works
Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation
Castro, D. D., Volkinshtein, D., & Meir, R. (2008) · neural information processing systems · Cited by 15 · see citing works
Using Spatiotemporal Correlations to Learn Topographic Maps for Invariant Object Recognition
Michler, F., Eckhorn, R., & Wächtler, T. (2009) · Journal of Neurophysiology · doi:10.1152/jn.90651.2008 · Cited by 13 · see citing works
Flagged first, then by citations
1 of the 31 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Synaptic Plasticity Forms and Functions
Magee, J. C., & Grienberger, C. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-090919-022842 · Cited by 806 · see citing works
“…of network innervate the distal dendritic regions in relative isolation from the feedforward input, allow error-like signals to be locally calculated within the dendrites of individual hidden-layer neurons (Körding & König 2001, Urbanczik & Senn 2014, Guerguiev et al. 2017, Sacramento et al. 2018).”
BackgroundRandom synaptic feedback weights support error backpropagation for deep learning
Lillicrap, T., Cownden, D., Tweed, D., & Akerman, C. J. (2016) · Nature Communications · doi:10.1038/ncomms13276 · Cited by 879 · see citing works
Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0277-3 · Cited by 908 · see citing works
Towards deep learning with segregated dendrites
Guerguiev, J., Lillicrap, T., & Richards, B. A. (2017) · eLife · doi:10.7554/elife.22901 · Cited by 373 · see citing works
“Our model builds on previous neural networks research (Lee et al., 2015; Lillicrap et al., 2016) as well as computational studies of supervised learning in multi-compartment neurons (Urbanczik and Senn, 2014; Körding and König, 2001; Spratling and Johnson, 2006).”
BackgroundMethodResultInfluentialGerstner, W., Lehmann, M., Liakoni, V., Corneil, D., & Brea, J. (2018) · Frontiers in Neural Circuits · doi:10.3389/fncir.2018.00053 · Cited by 321 · see citing works
“, 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 303 · see citing works
Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)
Kaiser, J., Mostafa, H., & Neftci, E. O. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00424 · Cited by 293 · see citing works
“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].”
BackgroundSupervised learning in spiking neural networks: A review of algorithms and evaluations
Wang, X., Lin, X., & Dang, X. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.02.011 · Cited by 276 · see citing works
Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware
Rathi, N., Chakraborty, I., Kosta, A. K., Sengupta, A., Ankit, A., Panda, P., & Roy, K. (2022) · ACM Computing Surveys · doi:10.1145/3571155 · Cited by 274 · see citing works
“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 245 · see citing works
Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines
Neftci, E., Augustine, C., Paul, S., & Detorakis, G. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00324 · Cited by 235 · see citing works
“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 181 · see citing works
Dendritic solutions to the credit assignment problem
Richards, B. A., & Lillicrap, T. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.08.003 · Cited by 148 · see citing works
Efficient Spike-Driven Learning With Dendritic Event-Based Processing
Yang, S., Gao, T. Y., Wang, J., Deng, B., Lansdell, B., & Linares-Barranco, B. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.601109 · Cited by 148 · see citing works
“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 140 · see citing works
Where is the error? Hierarchical predictive coding through dendritic error computation
Mikulasch, F. A., Rudelt, L., Wibral, M., & Priesemann, V. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.09.007 · Cited by 128 · see citing works
“Therefore, these dendritic potentials can easily represent the prediction errors that are required for the learning of bottom-up and top-down connections (Infobox 2.1), which has been successfully applied in [13, 14].”
InfluentialAn integrative, multiscale view on neural theories of consciousness
Storm, J. F., Klink, P. C., Aru, J., Senn, W., Goebel, R., Pigorini, A., Avanzini, P., Vanduffel, W., Roelfsema, P. R., Massimini, M., et al. (2024) · Neuron · doi:10.1016/j.neuron.2024.02.004 · Cited by 114 · see citing works
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 · see citing works
“…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 · see citing works
Biologically plausible deep learning — But how far can we go with shallow networks?
Illing, B., Gerstner, W., & Brea, J. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.06.001 · Cited by 107 · see citing works
Zhang, X., Lü, J., Wang, Z., Wang, R., Wei, J., Shi, T., Dou, C., Wu, Z., Zhu, J., Shang, D., et al. (2021) · 中国科学通报：英文版 · doi:10.1016/j.scib.2021.04.014 · Cited by 106 · see citing works
Frenkel, C., Bol, D., & Indiveri, G. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3273520 · Cited by 139 · see citing works
“, 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 108 · see citing works
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 98 · see citing works
“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 94 · see citing works
“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 87 · see citing works
“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 · see citing works
“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., & Gerstner, W. (2017) · eLife · doi:10.7554/elife.28295 · Cited by 89 · see citing works
“(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 81 · see citing works
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 80 · see citing works
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 · see citing works
“Urbanczik and Senn (2014) presented an SNN model based on learning with dendritic spike prediction.”
BackgroundIndiveri, G., & Sandamirskaya, Y. (2019) · IEEE Signal Processing Magazine · doi:10.1109/msp.2019.2928376 · Cited by 77 · see citing works
Frenkel, C., Lefebvre, M., & Bol, D. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.629892 · Cited by 76 · see citing works
“…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 75 · see citing works
“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
Otor, Y., Achvat, S., Cermak, N., Benisty, H., Abboud, M., Barak, O., Schiller, Y., Poleg-Polsky, A., & Schiller, J. (2022) · Science · doi:10.1126/science.abn1421 · Cited by 67 · see citing works
Spike-driven multi-scale learning with hybrid mechanisms of spiking dendrites
Yang, S., Pang, Y., Wang, H., Lei, T., Pan, J., Wang, J., & Jin, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126240 · Cited by 64 · see citing works
Zendrikov, D., Solinas, S. M. G., & Indiveri, G. (2023) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ace64c · Cited by 63 · see citing works
Spike-based local synaptic plasticity: a survey of computational models and neuromorphic circuits
Khacef, L., Klein, P., Cartiglia, M., Rubino, A., Indiveri, G., & Chicca, E. (2023) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ad05da · Cited by 64 · see citing works
“In this case, there is never a zero weight change [68].”
BackgroundMethodInfluentialData and Power Efficient Intelligence with Neuromorphic Learning Machines
Neftci, E. O. (2018) · iScience · doi:10.1016/j.isci.2018.06.010 · Cited by 50 · see citing works
Redundancy in synaptic connections enables neurons to learn optimally
Hiratani, N., & Fukai, T. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1803274115 · Cited by 52 · see citing works
“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 · see citing works
“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 47 · see citing works
Spiking neural networks for inference and learning: a memristor-based design perspective
Fouda, M. E., Kurdahi, F. J., Eltawil, A. M., & Neftci, E. O. (2020) · Elsevier eBooks · doi:10.1016/b978-0-08-102782-0.00019-8 · Cited by 45 · see citing works
Ebner, C., Claudia Clopath, A., Jedlička, P., & Cuntz, H. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.11.068 · Cited by 46 · see citing works
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 · see citing works
“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].”
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 42 · see citing works
“…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 · see citing works
Predictive olfactory learning in Drosophila
Zhao, C., Widmer, Y. F., Diegelmann, S., Petrovici, M. A., Sprecher, S. G., & Senn, W. (2021) · Scientific Reports · doi:10.1038/s41598-021-85841-y · Cited by 45 · see citing works
“1 is the nudging strength of the postsynaptic teaching signal [44]).”
BackgroundSelf-citationPredictive 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 · see citing works
Prospective 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 45 · see citing works
“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., & Gerstner, W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.08262 · Cited by 40 · see citing works
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 38 · see citing works
“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 38 · see citing works
“…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 38 · see citing works
Brivio, S., Ly, D. R. B., Vianello, E., & Spiga, S. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.580909 · Cited by 37 · see citing works
“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., & Gerstner, W. (2016) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2016.05.012 · Cited by 38 · see citing works
Somatodendritic consistency check for temporal feature segmentation
Asabuki, T., & Fukai, T. (2020) · Nature Communications · doi:10.1038/s41467-020-15367-w · Cited by 36 · see citing works
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 35 · see citing works
“…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 · see citing works
“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 52 · see citing works
“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 · see citing works
“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 · see citing works
“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 35 · see citing works
“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 · see citing works
“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 47 · see citing works
Attractor network dynamics enable preplay and rapid path planning in maze-like environments
Corneil, D., & Gerstner, W. (2015) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 31 · see citing works
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 · see citing works
“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 · see citing works
“…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 · see citing works
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 25 · see citing works
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 · see citing works
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 30 · see citing works
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 27 · see citing works
“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 · see citing works
“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 22 · see citing works
“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 24 · see citing works
Frenkel, C., Bol, D., & Indiveri, G. (2021) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-217499 · Cited by 24 · see citing works
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 · see citing works
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 22 · see citing works
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 22 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
“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 23 · see citing works
“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 20 · see citing works
“…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 · see citing works
Frenkel, C., Lefebvre, M., & Bol, D. (2019) · Cited by 17 · see citing works
Geng, H.-Y., Arbuthnott, G. W., Yung, W., & Ke, Y. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab460 · Cited by 26 · see citing works
Gao, T. Y., Deng, B., Wang, J., & Yi, G. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.929644 · Cited by 16 · see citing works
“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 17 · see citing works
“…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 16 · see citing works
“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 20 · see citing works
“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 · see citing works
“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 15 · see citing works
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 15 · see citing works
“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 · see citing works
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 15 · see citing works
“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 14 · see citing works
Moldwin, T., Kalmenson, M., & Segev, I. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009015 · Cited by 14 · see citing works
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 · see citing works
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
