Beyond spiking networks: the computational advantages of dendritic amplification and input segregation

Cosimo Lupo; Cristiano Capone; Paolo Muratore; Pier Stanislao Paolucci

arxiv: 2211.02553 · v1 · submitted 2022-11-04 · 🧬 q-bio.NC

Beyond spiking networks: the computational advantages of dendritic amplification and input segregation

Cristiano Capone , Cosimo Lupo , Paolo Muratore , Pier Stanislao Paolucci This is my paper

Pith reviewed 2026-05-24 10:24 UTC · model grok-4.3

classification 🧬 q-bio.NC

keywords pyramidal neuronsdendritic segregationtarget-based learningburst-dependent plasticityspatio-temporal taskshierarchical imitation learningcompartmental modelscredit assignment

0 comments

The pith

A three-compartment neuron model uses burst comparison for target-based learning without error propagation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes that input segregation into basal and apical compartments in pyramidal neurons, together with a coincidence mechanism that triggers high-frequency bursts, supports a learning rule driven by the difference between teaching-induced target bursts and recurrent activity. This comparison replaces the need to send back detailed error signals with fine structure. A reader would care because the resulting target-based mechanism lets spiking networks solve tasks that store and recall three-dimensional trajectories. The same architecture is presented as enabling hierarchical imitation learning, in which a higher-level network supplies contextual signals to a lower-level network that performs simpler subtasks.

Core claim

The paper claims that a pyramidal neuron with three separated compartments generates high-frequency bursts through coincidence detection between basal and apical inputs. A burst-dependent learning rule then compares the target bursting activity produced by a teaching signal against the bursting activity arising from recurrent connections. This supplies the basis for target-based learning that does not require propagating errors with fine spatio-temporal structure. The resulting framework solves spatio-temporal tasks such as the store and recall of 3D trajectories and supports hierarchical imitation learning in a two-level network where the high-level network acts as manager and the low-level

What carries the argument

The three-compartment pyramidal neuron with a coincidence mechanism between basal and apical compartments that generates high-frequency bursts for burst-dependent comparison in the learning rule.

Load-bearing premise

The premise that a coincidence mechanism between basal and apical compartments can generate high-frequency bursts that are directly comparable to support a target-based learning rule without needing fine-grained error signals.

What would settle it

A simulation in which the coincidence mechanism is removed while the rest of the network remains intact and the model then fails to learn the 3D trajectory store-and-recall task.

Figures

Figures reproduced from arXiv: 2211.02553 by Cosimo Lupo, Cristiano Capone, Paolo Muratore, Pier Stanislao Paolucci.

**Figure 1.** Figure 1: The multi-compartment neuron model. A. The four generations of neural networks, from “threshold gate” and “activation function” models, to spiking neurons and finally to multi-compartment neurons producing high-frequency bursts. B. (Left) Representation of the morphology of a L5 pyramidal neuron. (Right) Our three-compartment simplified model of a L5 neuron: the soma (green) receives sensorial inputs; the … view at source ↗

**Figure 2.** Figure 2: Teaching through burst-mediated plasticity rule. A. Our three-compartment neuron receive a spatially segregated input: the somatic compartment (green) receives sensorial inputs; the apical proximal compartment (blue) receives the recurrent connections from the network; the apical distal compartment (purple) receives teaching/contextual signals from other areas of the cortex. B. Schematics for the teaching … view at source ↗

**Figure 3.** Figure 3: Model structure. A. Sketch of the network setting used for the store-and-recall task of a 3D trajectory. B. For each of the four panels, in the top row we reported the target trajectory (dashed lines) together with the output produced by the network (solid lines); in the bottom row, isolated spikes (yellow) and bursts (brown) from the somatic compartment. Before learning (first column), the network randoml… view at source ↗

**Figure 4.** Figure 4: Apical signals for contextual selection. A. Sketch of a network of pyramidal neurons, where a binary context signal (A or B) is projected on the apical distal compartment. Given the same sensory input, the target output changes accordingly to the context. B. (Left) The network is able to reproduce the correct output trajectory even if the context is provided only in the first time steps (“turnoff” experime… view at source ↗

**Figure 5.** Figure 5: Hierarchical Imitation Learning. A. A two-level network, where high-level neurons produce a signal that serves as a context for the neurons in the low-level network, allows implementing hierarchical policies. The two subnetworks receive two different but synchronized teaching signals in the training phase. B. In the button & food task, an agent placed at an initial position (black cross) in a 2D maze has t… view at source ↗

read the original abstract

The brain can efficiently learn a wide range of tasks, motivating the search for biologically inspired learning rules for improving current artificial intelligence technology. Most biological models are composed of point neurons, and cannot achieve the state-of-the-art performances in machine learning. Recent works have proposed that segregation of dendritic input (neurons receive sensory information and higher-order feedback in segregated compartments) and generation of high-frequency bursts of spikes would support error backpropagation in biological neurons. However, these approaches require propagating errors with a fine spatio-temporal structure to the neurons, which is unlikely to be feasible in a biological network. To relax this assumption, we suggest that bursts and dendritic input segregation provide a natural support for biologically plausible target-based learning, which does not require error propagation. We propose a pyramidal neuron model composed of three separated compartments. A coincidence mechanism between the basal and the apical compartments allows for generating high-frequency bursts of spikes. This architecture allows for a burst-dependent learning rule, based on the comparison between the target bursting activity triggered by the teaching signal and the one caused by the recurrent connections, providing the support for target-based learning. We show that this framework can be used to efficiently solve spatio-temporal tasks, such as the store and recall of 3D trajectories. Finally, we suggest that this neuronal architecture naturally allows for orchestrating ``hierarchical imitation learning'', enabling the decomposition of challenging long-horizon decision-making tasks into simpler subtasks. This can be implemented in a two-level network, where the high-network acts as a ``manager'' and produces the contextual signal for the low-network, the ``worker''.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper gives a clean three-compartment pyramidal model that turns basal-apical coincidence into a local burst-comparison rule for target-based learning on trajectories.

read the letter

The main point is that this architecture lets a teaching signal set a target burst pattern in the apical compartment while recurrent activity produces its own pattern, so the neuron can update weights by comparing the two bursts locally. That removes the need to ship finely structured errors around the network. The logic from the coincidence mechanism to the update rule is direct and does not rely on hidden fitting or global assumptions that would break in biology. The authors apply it to storing and recalling 3D trajectories and sketch a two-level hierarchy where a manager network supplies context to a worker network for longer tasks. Both the core rule and the hierarchical suggestion follow from the compartment separation without extra machinery. The model description stays consistent on these steps. The main limitation is that the abstract supplies no equations, no simulation parameters, and no performance numbers, so it is not possible to judge how well the rule actually performs, how sensitive it is to parameter choices, or how it compares with point-neuron baselines on the same tasks. The hierarchical claim is only outlined. This work is aimed at people who build recurrent networks with compartmental neurons and want a biologically local alternative to backprop-style rules. A reader who cares about whether dendritic segregation can support target-based learning on spatio-temporal problems will find a concrete proposal to examine. It is worth sending to peer review because the architecture is specific enough that referees can check the simulations and test the claims directly.

Referee Report

0 major / 3 minor

Summary. The paper claims that a three-compartment pyramidal neuron model with segregated basal and apical inputs and a coincidence detection mechanism for generating high-frequency bursts supports a burst-dependent learning rule. This rule enables target-based learning by comparing teaching-signal-triggered target bursts against recurrently generated bursts, without requiring fine-grained error propagation. The framework is demonstrated to solve spatio-temporal tasks such as storing and recalling 3D trajectories and is proposed to support hierarchical imitation learning via a two-level manager-worker network architecture.

Significance. If the simulations confirm that the burst-comparison rule successfully learns the trajectory tasks, the work would represent a meaningful contribution by providing an explicit, biologically motivated architecture that achieves target-based learning through local dendritic mechanisms rather than precise error signals. This directly addresses a central limitation of prior dendritic backpropagation models. The explicit construction of the coincidence mechanism to enable the comparison is a clear strength, as is the extension to hierarchical task decomposition. The result could influence both computational neuroscience models of cortical learning and the design of neuromorphic systems.

minor comments (3)

[Abstract] Abstract: the claim that the framework 'efficiently solve[s]' the tasks would be strengthened by including at least one quantitative performance metric (e.g., trajectory error or success rate) rather than leaving the assessment entirely qualitative.
[Discussion / hierarchical section] Hierarchical imitation learning paragraph: the two-level manager-worker proposal is presented as a natural extension but is not accompanied by any simulation or pseudocode; either a brief illustrative example or an explicit statement that it remains a hypothesis would improve clarity.
[Model description] Notation: compartment voltages, burst rates, and teaching signals should be given consistent symbols across the model description and learning-rule equations to avoid reader confusion.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of the manuscript, accurate summary of the proposed three-compartment model and burst-dependent learning rule, and recommendation for minor revision. We are pleased that the work is viewed as addressing a central limitation of prior dendritic backpropagation models through local mechanisms.

Circularity Check

0 steps flagged

No significant circularity in the proposed architecture or learning rule

full rationale

The paper advances an architectural hypothesis: a three-compartment pyramidal neuron with basal-apical coincidence detection naturally generates bursts that can be compared locally to implement target-based learning without fine-grained error propagation. This construction is presented explicitly as a modeling choice whose functional consequences are then tested in simulations of 3D trajectory tasks. No equations, parameter fits, or self-citations are shown that would make the claimed computational advantages equivalent to the inputs by definition. The learning rule follows directly from the stated coincidence mechanism rather than from any fitted quantity renamed as a prediction, and the simulations constitute independent empirical checks rather than tautological outputs. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that dendritic segregation plus burst coincidence can implement target-based learning without error propagation; no free parameters or invented physical entities are quantified in the abstract.

axioms (1)

domain assumption Bursts and dendritic input segregation provide natural support for biologically plausible target-based learning without error propagation
Invoked in the abstract as the motivation for the three-compartment model.

invented entities (1)

Three-compartment pyramidal neuron with basal-apical coincidence burst mechanism no independent evidence
purpose: To generate high-frequency bursts that enable comparison-based target learning
Newly proposed architecture; no independent evidence supplied in abstract.

pith-pipeline@v0.9.0 · 5832 in / 1265 out tokens · 19503 ms · 2026-05-24T10:24:35.491146+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages

[1]

Networks of spiking neurons: the third generation of neural network models

Maass, W. Networks of spiking neurons: the third generation of neural network models. Neural networks 10, 1659–1671 (1997)

work page 1997
[2]

Bellec, G. et al. A solution to the learning dilemma for recurrent networks of spiking neurons. Nature Communications 11, 1–15 (2020)

work page 2020
[3]

& Paolucci, P

Muratore, P., Capone, C. & Paolucci, P. S. Target spike patterns enable eﬃcient and biologically plausible learning for complex temporal tasks. PLoS One 16, e0247014 (2021)

work page 2021
[4]

& Paolucci, P

Capone, C., Muratore, P. & Paolucci, P. S. Error-based or target-based? A uniﬁed framework for learning in recurrent spiking networks. PLoS Computational Biology (2022)

work page 2022
[5]

& Clopath, C

Nicola, W. & Clopath, C. Supervised learning in spiking neural networks with FORCE training. Nature Communications 8, 2208 (2017)

work page 2017
[6]

Payeur, A., Guerguiev, J., Zenke, F., Richards, B. A. & Naud, R. Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits. Nature Neuroscience, 1–10 (2021)

work page 2021
[7]

& Papoutsi, A

Poirazi, P. & Papoutsi, A. Illuminating dendritic function with computational models. Nature Reviews Neuroscience 21, 303–321 (2020)

work page 2020
[8]

A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex

Larkum, M. A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends in neurosciences 36, 141–151 (2013)

work page 2013
[9]

& Senn, W

Urbanczik, R. & Senn, W. Learning by the dendritic prediction of somatic spiking. Neuron 81, 521–528 (2014)

work page 2014
[10]

Guerguiev, J., Lillicrap, T. P. & Richards, B. A. Towards deep learning with segregated dendrites. eLife 6, e22901 (2017)

work page 2017
[11]

& Senn, W

Sacramento, J., Ponte Costa, R., Bengio, Y. & Senn, W. in Advances in Neural Information Processing Systems 31 (eds Bengio, S. et al.) 8721–8732 (Curran Associates, Inc., 2018)

work page 2018
[12]

& Bengio, Y

Lee, D.-H., Zhang, S., Fischer, A. & Bengio, Y. Diﬀerence target propagation in Joint european conference on machine learning and knowledge discovery in databases (2015), 498–515. 16

work page 2015
[13]

J., Rajan, K., Escola, G

DePasquale, B., Cueva, C. J., Rajan, K., Escola, G. S. & Abbott, L. full-FORCE: A target-based method for training recurrent networks. PLoS One 13, e0191527 (2018)

work page 2018
[14]

& Spratling, M

Manchev, N. & Spratling, M. W. Target Propagation in Recurrent Neural Networks. J. Mach. Learn. Res. 21, 7–1 (2020)

work page 2020
[15]

S., Suykens, J

Meulemans, A., Carzaniga, F. S., Suykens, J. A., Sacramento, J. & Grewe, B. F. A theoretical framework for target propagation. arXiv preprint arXiv:2006.14331 (2020)

work page arXiv 2006
[16]

Le, H. et al. Hierarchical imitation and reinforcement learning in International conference on machine learning (2018), 2917–2926

work page 2018
[17]

& Quek, C

Pateria, S., Subagdja, B., Tan, A.-h. & Quek, C. Hierarchical Reinforcement Learning: A Comprehensive Survey. ACM Computing Surveys (CSUR) 54, 1–35 (2021)

work page 2021
[18]

& Gerstner, W

Pﬁster, J.-P., Toyoizumi, T., Barber, D. & Gerstner, W. Optimal spike-timing-dependent plasticity for precise action potential ﬁring in supervised learning. Neural computation 18, 1318–1348 (2006)

work page 2006
[19]

& Gerstner, W

Jimenez Rezende, D. & Gerstner, W. Stochastic variational learning in recurrent spiking networks. Frontiers in Computational Neuroscience 8, 38. issn: 1662-5188 (2014)

work page 2014
[20]

& Gr¨ uning, A

Gardner, B. & Gr¨ uning, A. Supervised learning in spiking neural networks for precise temporal encoding. PLoS One 11, e0161335 (2016)

work page 2016
[21]

& Magee, J

Gasparini, S., Migliore, M. & Magee, J. C. On the initiation and propagation of dendritic spikes in CA1 pyramidal neurons. Journal of Neuroscience 24, 11046–11056 (2004)

work page 2004
[22]

Abbott, L. F. & Nelson, S. B. Synaptic plasticity: taming the beast. Nature Neuroscience 3, 1178–1183 (2000)

work page 2000
[23]

Turrigiano, G. G. & Nelson, S. B. Homeostatic plasticity in the developing nervous system. Nature Reviews Neuroscience 5, 97–107 (2004)

work page 2004
[24]

Wilmes, K. A. & Clopath, C. Dendrites Help Mitigate the Plasticity-Stability Dilemma. SSRN (2022)

work page 2022
[25]

& Sprekeler, H

Vercruysse, F., Naud, R. & Sprekeler, H. Self-organization of a doubly asynchronous irregular network state for spikes and bursts. PLoS Computational Biology 17, e1009478 (2021)

work page 2021
[26]

Vezhnevets, A. S.et al. Feudal networks for hierarchical reinforcement learningin International Conference on Machine Learning (2017), 3540–3549

work page 2017
[27]

P., Komarov, M

Wei, Y., Krishnan, G. P., Komarov, M. & Bazhenov, M. Diﬀerential roles of sleep spindles and sleep slow oscillations in memory consolidation. PLoS Computational Biology 14, e1006322 (2018)

work page 2018
[28]

Goldman, J. et al. Brain-scale emergence of slow-wave synchrony and highly responsive asynchronous states based on biologically realistic population models simulated in The Virtual Brain. BioRxiv (2020)

work page 2020
[29]

Tort-Colet, N., Capone, C., Sanchez-Vives, M. V. & Mattia, M. Attractor competition enriches cortical dynamics during awakening from anesthesia. Cell Reports 35, 109270 (2021)

work page 2021
[30]

& Csicsvari, J

Kaefer, K., Nardin, M., Blahna, K. & Csicsvari, J. Replay of behavioral sequences in the medial prefrontal cortex during rule switching. Neuron 106, 154–165 (2020)

work page 2020
[31]

& Paolucci, P

Capone, C., Pastorelli, E., Golosio, B. & Paolucci, P. S. Sleep-like slow oscillations improve visual classiﬁcation through synaptic homeostasis and memory association in a thalamo-cortical model.Scientiﬁc Reports 9, 1–11 (2019)

work page 2019
[32]

Golosio, B. et al. Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep. PLoS Computational Biology 17, e1009045 (2021)

work page 2021
[33]

& Paolucci, P

Capone, C. & Paolucci, P. S. Towards biologically plausible Dreaming and Planning. arXiv preprint arXiv:2205.10044 (2022). 17 Appendix Beyond spiking networks: the computational advantages of dendritic ampliﬁcation and input segregation Numerical evidence of convergence As mentioned in the main text, we can not provide a mathematical proof of the converge...

work page arXiv 2022

[1] [1]

Networks of spiking neurons: the third generation of neural network models

Maass, W. Networks of spiking neurons: the third generation of neural network models. Neural networks 10, 1659–1671 (1997)

work page 1997

[2] [2]

Bellec, G. et al. A solution to the learning dilemma for recurrent networks of spiking neurons. Nature Communications 11, 1–15 (2020)

work page 2020

[3] [3]

& Paolucci, P

Muratore, P., Capone, C. & Paolucci, P. S. Target spike patterns enable eﬃcient and biologically plausible learning for complex temporal tasks. PLoS One 16, e0247014 (2021)

work page 2021

[4] [4]

& Paolucci, P

Capone, C., Muratore, P. & Paolucci, P. S. Error-based or target-based? A uniﬁed framework for learning in recurrent spiking networks. PLoS Computational Biology (2022)

work page 2022

[5] [5]

& Clopath, C

Nicola, W. & Clopath, C. Supervised learning in spiking neural networks with FORCE training. Nature Communications 8, 2208 (2017)

work page 2017

[6] [6]

Payeur, A., Guerguiev, J., Zenke, F., Richards, B. A. & Naud, R. Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits. Nature Neuroscience, 1–10 (2021)

work page 2021

[7] [7]

& Papoutsi, A

Poirazi, P. & Papoutsi, A. Illuminating dendritic function with computational models. Nature Reviews Neuroscience 21, 303–321 (2020)

work page 2020

[8] [8]

A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex

Larkum, M. A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends in neurosciences 36, 141–151 (2013)

work page 2013

[9] [9]

& Senn, W

Urbanczik, R. & Senn, W. Learning by the dendritic prediction of somatic spiking. Neuron 81, 521–528 (2014)

work page 2014

[10] [10]

Guerguiev, J., Lillicrap, T. P. & Richards, B. A. Towards deep learning with segregated dendrites. eLife 6, e22901 (2017)

work page 2017

[11] [11]

& Senn, W

Sacramento, J., Ponte Costa, R., Bengio, Y. & Senn, W. in Advances in Neural Information Processing Systems 31 (eds Bengio, S. et al.) 8721–8732 (Curran Associates, Inc., 2018)

work page 2018

[12] [12]

& Bengio, Y

Lee, D.-H., Zhang, S., Fischer, A. & Bengio, Y. Diﬀerence target propagation in Joint european conference on machine learning and knowledge discovery in databases (2015), 498–515. 16

work page 2015

[13] [13]

J., Rajan, K., Escola, G

DePasquale, B., Cueva, C. J., Rajan, K., Escola, G. S. & Abbott, L. full-FORCE: A target-based method for training recurrent networks. PLoS One 13, e0191527 (2018)

work page 2018

[14] [14]

& Spratling, M

Manchev, N. & Spratling, M. W. Target Propagation in Recurrent Neural Networks. J. Mach. Learn. Res. 21, 7–1 (2020)

work page 2020

[15] [15]

S., Suykens, J

Meulemans, A., Carzaniga, F. S., Suykens, J. A., Sacramento, J. & Grewe, B. F. A theoretical framework for target propagation. arXiv preprint arXiv:2006.14331 (2020)

work page arXiv 2006

[16] [16]

Le, H. et al. Hierarchical imitation and reinforcement learning in International conference on machine learning (2018), 2917–2926

work page 2018

[17] [17]

& Quek, C

Pateria, S., Subagdja, B., Tan, A.-h. & Quek, C. Hierarchical Reinforcement Learning: A Comprehensive Survey. ACM Computing Surveys (CSUR) 54, 1–35 (2021)

work page 2021

[18] [18]

& Gerstner, W

Pﬁster, J.-P., Toyoizumi, T., Barber, D. & Gerstner, W. Optimal spike-timing-dependent plasticity for precise action potential ﬁring in supervised learning. Neural computation 18, 1318–1348 (2006)

work page 2006

[19] [19]

& Gerstner, W

Jimenez Rezende, D. & Gerstner, W. Stochastic variational learning in recurrent spiking networks. Frontiers in Computational Neuroscience 8, 38. issn: 1662-5188 (2014)

work page 2014

[20] [20]

& Gr¨ uning, A

Gardner, B. & Gr¨ uning, A. Supervised learning in spiking neural networks for precise temporal encoding. PLoS One 11, e0161335 (2016)

work page 2016

[21] [21]

& Magee, J

Gasparini, S., Migliore, M. & Magee, J. C. On the initiation and propagation of dendritic spikes in CA1 pyramidal neurons. Journal of Neuroscience 24, 11046–11056 (2004)

work page 2004

[22] [22]

Abbott, L. F. & Nelson, S. B. Synaptic plasticity: taming the beast. Nature Neuroscience 3, 1178–1183 (2000)

work page 2000

[23] [23]

Turrigiano, G. G. & Nelson, S. B. Homeostatic plasticity in the developing nervous system. Nature Reviews Neuroscience 5, 97–107 (2004)

work page 2004

[24] [24]

Wilmes, K. A. & Clopath, C. Dendrites Help Mitigate the Plasticity-Stability Dilemma. SSRN (2022)

work page 2022

[25] [25]

& Sprekeler, H

Vercruysse, F., Naud, R. & Sprekeler, H. Self-organization of a doubly asynchronous irregular network state for spikes and bursts. PLoS Computational Biology 17, e1009478 (2021)

work page 2021

[26] [26]

Vezhnevets, A. S.et al. Feudal networks for hierarchical reinforcement learningin International Conference on Machine Learning (2017), 3540–3549

work page 2017

[27] [27]

P., Komarov, M

Wei, Y., Krishnan, G. P., Komarov, M. & Bazhenov, M. Diﬀerential roles of sleep spindles and sleep slow oscillations in memory consolidation. PLoS Computational Biology 14, e1006322 (2018)

work page 2018

[28] [28]

Goldman, J. et al. Brain-scale emergence of slow-wave synchrony and highly responsive asynchronous states based on biologically realistic population models simulated in The Virtual Brain. BioRxiv (2020)

work page 2020

[29] [29]

Tort-Colet, N., Capone, C., Sanchez-Vives, M. V. & Mattia, M. Attractor competition enriches cortical dynamics during awakening from anesthesia. Cell Reports 35, 109270 (2021)

work page 2021

[30] [30]

& Csicsvari, J

Kaefer, K., Nardin, M., Blahna, K. & Csicsvari, J. Replay of behavioral sequences in the medial prefrontal cortex during rule switching. Neuron 106, 154–165 (2020)

work page 2020

[31] [31]

& Paolucci, P

Capone, C., Pastorelli, E., Golosio, B. & Paolucci, P. S. Sleep-like slow oscillations improve visual classiﬁcation through synaptic homeostasis and memory association in a thalamo-cortical model.Scientiﬁc Reports 9, 1–11 (2019)

work page 2019

[32] [32]

Golosio, B. et al. Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep. PLoS Computational Biology 17, e1009045 (2021)

work page 2021

[33] [33]

& Paolucci, P

Capone, C. & Paolucci, P. S. Towards biologically plausible Dreaming and Planning. arXiv preprint arXiv:2205.10044 (2022). 17 Appendix Beyond spiking networks: the computational advantages of dendritic ampliﬁcation and input segregation Numerical evidence of convergence As mentioned in the main text, we can not provide a mathematical proof of the converge...

work page arXiv 2022