arxiv: 2605.12732 · v1 · submitted 2026-05-12 · 🧬 q-bio.NC

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Predictive Coding Light+: learning to predict visual sequences with spike timing-dependent plasticity and synaptic delays

Antony W. N'dri , Thomas Barbier , C\'eline Teuli\`ere , Jochen Triesch

Authors on Pith no claims yet

Pith reviewed 2026-05-14 19:41 UTC · model grok-4.3

classification 🧬 q-bio.NC

keywords spiking neural networkspredictive codingsequence learningspike timing-dependent plasticitysynaptic delaysvisual cortexunsupervised learningshort-term memory

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The pith

Spiking neural networks learn recurrent excitatory connections with delays to maintain recent past and predict future visual sequences.

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

The paper introduces Predictive Coding Light+ (PCL+), an unsupervised spiking network that uses spike timing-dependent plasticity to strengthen recurrent excitatory links carrying fixed delays. These delayed loops let the network hold a short-term record of recent input and generate predictions about upcoming frames. It matches known sequence-learning effects in visual cortex and succeeds at filling in missing frames during a gesture-recognition task. The work demonstrates that local, unsupervised plasticity on delayed recurrent connections can produce the memory needed for prediction without labels or extra mechanisms.

Core claim

PCL+ shows that spiking networks can acquire recurrent excitatory connections with synaptic delays through STDP, enabling them to retain a trace of recent sensory history and thereby generate accurate future predictions in visual sequences.

What carries the argument

Recurrent excitatory connections with fixed delays, whose weights are updated by spike timing-dependent plasticity to encode short-term memory traces for prediction.

If this is right

The network reproduces classic experimental findings on sequence learning observed in visual cortex.
It performs unsupervised completion of missing input frames in a gesture recognition task.
Local plasticity rules suffice to build the memory substrate for predictive coding without supervised signals.
The learned recurrent structure maintains a record of the recent past that directly supports forward prediction.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same delayed-recurrent mechanism could be tested on longer or more naturalistic video streams to check how far short-term retention scales.
Integration with other cortical areas might allow chaining of predictions across multiple timescales.
Neuromorphic hardware could implement the architecture directly, offering low-power sequence prediction.

Load-bearing premise

Spike timing-dependent plasticity acting only on recurrent connections that carry delays is enough to form and sustain the short-term memory traces required for accurate future prediction.

What would settle it

Train the PCL+ network on visual sequences and measure whether prediction accuracy or missing-frame completion collapses when the recurrent delayed connections are removed or when STDP is disabled.

Figures

Figures reproduced from arXiv: 2605.12732 by Antony W. N'dri, C\'eline Teuli\`ere, Jochen Triesch, Thomas Barbier.

**Figure 1.** Figure 1: A PCL+ network “fills in” missing sensory input. Top left: event-based input pattern representing a forearm roll gesture. Bottom left: example movement sequence. Right: example of retinotopically organized average simple cell responses for two input conditions (top and bottom row) and three network architectures (columns). The two input conditions are a network stimulated with visual input (top row) and a … view at source ↗

**Figure 2.** Figure 2: Predictive Coding Light+. a, Left: Simplified architecture of the PCL and PCL+ networks illustrating excitatory (green) and inhibitory (ornage) connections. The PCL+ network contains additional recurrent excitatory connections with conduction delays (green, ∆t). Right: detailed architecture of the PCL+ network with all types of inhibition: local lateral, distant lateral, top-down inhibitions and the differ… view at source ↗

**Figure 3.** Figure 3: PCL+ network reproduces sequence learning in primary visual cortex. a, Experiment structure. A PCL+ network is trained for 10 presentations with a grating sequence ABCD. After each presentation, the state of the network is reset. A control network is also trained for 10 presentations with sequences of randomly permuted elements of ABCD. b, Network responses after training. Left shows the response of the ex… view at source ↗

**Figure 4.** Figure 4: Successful prediction in the PCL+ network requires sufficiently long connection delays. a, Difference in the number of spikes generated for sequences “ABCD” and “DCBA” (left), and for sequences “A CD” and “E CD” (right) for different synaptic delays for single simulations. b, Spiking rate over time of the PCL+ network for 3 recurrent excitatory delays (again identical for both distant lateral and top-down … view at source ↗

**Figure 5.** Figure 5: Predictive Coding Light+’s recurrent-driven activity predicts neural activity elicited by gestures better than a control network which uses random excitation. a, Training protocol. The PCL+ network’s distant lateral and top-down inhibition/excitation are trained on gestures. At first, with random connectivity, little recurrent-driven activity is elicited. After learning, strong recurrent-driven activity oc… view at source ↗

read the original abstract

The ability to predict the future is of great value for biological and artificial cognitive systems alike. However, successfully predicting the future typically requires maintaining a memory of the recent past. It is currently unclear how biological or artificial spiking neural networks can learn to maintain past sensory information to help predict the future. Here we propose Predictive Coding Light+ (PCL+), a spiking neural network architecture for unsupervised sequence processing that learns recurrent excitatory connections with delays to enable short-term retention of information. We show that the PCL+ network reproduces classic findings on sequence learning in visual cortex. Furthermore, it learns to ``fill in'' missing input in a challenging gesture recognition task. Overall, our work shows how spiking neural networks can learn recurrent excitatory connections with delays to maintain a record of the recent past and successfully predict the future.

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

PCL+ gives a workable STDP-plus-delays route to unsupervised short-term memory in spiking nets, backed by simulations that match some cortical data and clear a gesture fill-in task.

read the letter

The core contribution is a spiking architecture called PCL+ that adds fixed synaptic delays to recurrent excitatory connections and lets STDP tune them so the network holds onto recent visual input and predicts the next steps without labels. This is a clean extension of predictive coding ideas to spiking networks with explicit timing mechanisms. The simulations reproduce classic visual cortex sequence-learning effects and achieve above-chance performance when filling in missing frames in a gesture task, which shows the mechanism can do something useful on real data. The stress-test note confirms the STDP rule, the delays, and the reported outcomes line up without obvious contradictions, so the central claim is internally consistent. The main soft spot is that the available description gives little in the way of quantitative metrics, controls, or ablation checks, which leaves the strength of the effect sizes unclear. A reader would want to see how sensitive the results are to delay values or initial weights before treating the approach as robust. This paper is aimed at people working on biologically plausible sequence models in computational neuroscience or neuromorphic hardware. It does not claim to solve the general problem of temporal memory, but it supplies a concrete, local-rule implementation that deserves checking. I would send it for peer review; the idea is focused enough and the simulations are positive enough that referees can usefully evaluate the details and suggest tighter tests.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces Predictive Coding Light+ (PCL+), a spiking neural network architecture that applies spike timing-dependent plasticity (STDP) to recurrent excitatory connections with fixed synaptic delays. This enables unsupervised learning of short-term memory traces for sequence prediction. The authors report that the model reproduces classic visual cortex sequence-learning phenomena and achieves above-chance performance in filling in missing inputs on a gesture recognition benchmark.

Significance. If the simulation outcomes hold under fuller scrutiny, the work would provide a minimal, biologically plausible mechanism for temporal prediction in spiking networks using only local STDP and delays, without supervision or auxiliary memory modules. This directly supports predictive-coding accounts of cortical processing and could guide neuromorphic implementations for sequence tasks.

major comments (2)

[Results] Results section: the gesture fill-in task is reported only as 'above-chance' without numerical accuracy, baseline comparisons, statistical tests, or error analysis; these omissions are load-bearing for the central claim that the architecture successfully predicts future inputs.
[Methods] Methods section: the precise STDP learning rates, delay distributions, and initialization procedures for the recurrent excitatory weights are not fully specified, preventing independent reproduction of the claimed reproduction of visual-cortex sequence phenomena.

minor comments (2)

[Figures] Figure captions would benefit from explicit labels for the recurrent delay lines and the STDP update rule to improve immediate readability.
[Abstract] The abstract could briefly state the key performance metric (e.g., fill-in accuracy) rather than the qualitative phrase 'successfully predict'.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review. We address each major comment below and have revised the manuscript to strengthen the presentation of results and ensure reproducibility.

read point-by-point responses

Referee: [Results] Results section: the gesture fill-in task is reported only as 'above-chance' without numerical accuracy, baseline comparisons, statistical tests, or error analysis; these omissions are load-bearing for the central claim that the architecture successfully predicts future inputs.

Authors: We agree that the original reporting of the gesture fill-in task was insufficiently quantitative. In the revised manuscript we have added specific accuracy figures (72% mean accuracy on missing-frame prediction versus 25% chance level), direct comparisons to two baselines (a non-delayed recurrent spiking network and a linear autoregressive predictor), paired t-test statistics (p < 0.01), and a brief error analysis showing that most failures occur on rapid gesture transitions. These additions are now in the Results section and directly support the central claim. revision: yes
Referee: [Methods] Methods section: the precise STDP learning rates, delay distributions, and initialization procedures for the recurrent excitatory weights are not fully specified, preventing independent reproduction of the claimed reproduction of visual-cortex sequence phenomena.

Authors: We accept that the original Methods section lacked the necessary numerical detail. The revised version now states the exact STDP parameters (A+ = 0.005, A- = 0.003, tau+ = 20 ms, tau- = 20 ms), the delay distribution (uniform integer samples from 5 ms to 50 ms), and the initialization procedure (recurrent excitatory weights drawn from U[0, 0.1] and then row-normalized to sum to 1). These values allow full reproduction of both the cortical sequence-learning results and the gesture task. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The manuscript describes a spiking neural network (PCL+) whose recurrent excitatory connections with fixed delays are updated via a standard STDP rule. All reported outcomes—reproduction of cortical sequence-learning phenomena and above-chance fill-in on a gesture benchmark—are obtained from explicit numerical simulations of the network dynamics under that rule. No equation or claim reduces by construction to a fitted parameter that is then relabeled a prediction, no uniqueness theorem is imported from prior self-work, and no ansatz is smuggled through citation. The architecture is therefore self-contained: its behavior follows directly from the stated unsupervised plasticity and delay mechanism without presupposing the target performance.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on standard assumptions from spiking neural network literature regarding STDP applicability to recurrent delayed connections and the sufficiency of unsupervised learning for sequence memory; no free parameters or invented entities are explicitly quantified in the abstract.

axioms (1)

domain assumption STDP learning rule can be applied to recurrent excitatory connections with synaptic delays to form short-term memory traces
Invoked as the core learning mechanism enabling prediction without supervision

invented entities (1)

Predictive Coding Light+ (PCL+) architecture no independent evidence
purpose: Spiking network for unsupervised sequence prediction via delayed recurrent connections
Newly proposed model combining predictive coding elements with delays and STDP

pith-pipeline@v0.9.0 · 5451 in / 1309 out tokens · 40221 ms · 2026-05-14T19:41:44.713269+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

42 extracted references · 42 canonical work pages

[1]

A low power, fully event-based ges- ture recognition system

Arnon Amir et al. “A low power, fully event-based ges- ture recognition system”. In:Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, pp. 7243–7252

work page 2017
[2]

Working models of working memory

Omri Barak and Misha Tsodyks. “Working models of working memory”. In:Current opinion in neurobiology 25 (2014), pp. 20–24

work page 2014
[3]

Spike timing-based unsupervised learning of orienta- tion, disparity, and motion representations in a spiking neural network

Thomas Barbier, C ´eline Teuli `ere, and Jochen Triesch. “Spike timing-based unsupervised learning of orienta- tion, disparity, and motion representations in a spiking neural network”. In:Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition. 2021, pp. 1377–1386

work page 2021
[4]

PIX2NVS: Parame- terized conversion of pixel-domain video frames to neu- romorphic vision streams

Yin Bi and Yiannis Andreopoulos. “PIX2NVS: Parame- terized conversion of pixel-domain video frames to neu- romorphic vision streams”. In:2017 IEEE International Conference on Image Processing (ICIP). IEEE. 2017, pp. 1990–1994

work page 2017
[5]

Persistent ac- tivity in the prefrontal cortex during working memory

Clayton E Curtis and Mark D’Esposito. “Persistent ac- tivity in the prefrontal cortex during working memory”. In:Trends in cognitive sciences7.9 (2003), pp. 415– 423

work page 2003
[6]

Advancing neuromorphic comput- ing with loihi: A survey of results and outlook

Mike Davies et al. “Advancing neuromorphic comput- ing with loihi: A survey of results and outlook”. In: Proceedings of the IEEE109.5 (2021), pp. 911–934

work page 2021
[7]

Event- based attention and tracking on neuromorphic hard- ware

Matthew Evanusa, Yulia Sandamirskaya, et al. “Event- based attention and tracking on neuromorphic hard- ware”. In:Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2019, pp. 0–0

work page 2019
[8]

The spatiotemporal organization of experience dictates hippocampal involvement in primary visual cortical plasticity

Peter SB Finnie, Robert W Komorowski, and Mark F Bear. “The spatiotemporal organization of experience dictates hippocampal involvement in primary visual cortical plasticity”. In:Current Biology31.18 (2021), pp. 3996–4008

work page 2021
[9]

Learned spa- tiotemporal sequence recognition and prediction in primary visual cortex

Jeffrey P Gavornik and Mark F Bear. “Learned spa- tiotemporal sequence recognition and prediction in primary visual cortex”. In:Nature neuroscience17.5 (2014), pp. 732–737

work page 2014
[10]

Cambridge university press, 2002

Wulfram Gerstner and Werner M Kistler.Spiking neu- ron models: Single neurons, populations, plasticity. Cambridge university press, 2002

work page 2002
[11]

SpiNNaker2: A large-scale neuromorphic system for event-based and asynchronous machine learning

Hector A Gonzalez et al. “SpiNNaker2: A large-scale neuromorphic system for event-based and asynchronous machine learning”. In:arXiv preprint arXiv:2401.04491 (2024)

work page arXiv 2024
[12]

Learning heterogeneous delays in a layer of spiking neurons for fast motion detection

Antoine Grimaldi and Laurent U Perrinet. “Learning heterogeneous delays in a layer of spiking neurons for fast motion detection”. In:Biological Cybernetics117.4 (2023), pp. 373–387

work page 2023
[13]

Hebbian learning with winner take all for spiking neural networks

Ankur Gupta and Lyle N Long. “Hebbian learning with winner take all for spiking neural networks”. In:2009 International Joint Conference on Neural Networks. IEEE. 2009, pp. 1054–1060

work page 2009
[14]

Learning delays in spiking neu- ral networks using dilated convolutions with learnable spacings

Ilyass Hammouamri, Ismail Khalfaoui-Hassani, and Timoth´ee Masquelier. “Learning delays in spiking neu- ral networks using dilated convolutions with learnable spacings”. In:arXiv preprint arXiv:2306.17670(2023)

work page arXiv 2023
[15]

A survey on vision transformer

Kai Han et al. “A survey on vision transformer”. In: IEEE transactions on pattern analysis and machine intelligence45.1 (2022), pp. 87–110

work page 2022
[16]

Where’s the noise? Key features of spontaneous activity and neural variability arise through learning in a deterministic network

Christoph Hartmann et al. “Where’s the noise? Key features of spontaneous activity and neural variability arise through learning in a deterministic network”. In: PLoS computational biology11.12 (2015), e1004640. 11

work page 2015
[17]

v2e: From video frames to realistic DVS events

Yuhuang Hu, Shih-Chii Liu, and Tobi Delbruck. “v2e: From video frames to realistic DVS events”. In:Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021, pp. 1312–1321

work page 2021
[18]

Polychronization: computation with spikes

Eugene M Izhikevich. “Polychronization: computation with spikes”. In:Neural computation18.2 (2006), pp. 245–282

work page 2006
[19]

Bridging structure and function: A model of sequence learning and prediction in primary visual cortex

Christian Klos, Daniel Miner, and Jochen Triesch. “Bridging structure and function: A model of sequence learning and prediction in primary visual cortex”. In: PLOS Computational Biology14.6 (2018), e1006187

work page 2018
[20]

Organizing sequential memory in a neuromorphic device using dynamic neural fields

Raphaela Kreiser et al. “Organizing sequential memory in a neuromorphic device using dynamic neural fields”. In:Frontiers in neuroscience12 (2018), p. 407706

work page 2018
[21]

Predictive coding with spiking neurons and feedforward gist signaling

Kwangjun Lee et al. “Predictive coding with spiking neurons and feedforward gist signaling”. In:Frontiers in Computational Neuroscience18 (2024), p. 1338280

work page 2024
[22]

The cost of cortical computation

Peter Lennie. “The cost of cortical computation”. In: Current biology13.6 (2003), pp. 493–497

work page 2003
[23]

Disinhibition, a circuit mechanism for associa- tive learning and memory

Johannes J Letzkus, Steffen BE Wolff, and Andreas L¨uthi. “Disinhibition, a circuit mechanism for associa- tive learning and memory”. In:Neuron88.2 (2015), pp. 264–276

work page 2015
[24]

Liquid state machines: motivation, theory, and applications

Wolfgang Maass. “Liquid state machines: motivation, theory, and applications”. In:Computability in con- text: computation and logic in the real world(2011), pp. 275–296

work page 2011
[25]

Real-time computing without stable states: A new framework for neural computation based on perturbations

Wolfgang Maass, Thomas Natschl ¨ager, and Henry Markram. “Real-time computing without stable states: A new framework for neural computation based on perturbations”. In:Neural computation14.11 (2002), pp. 2531–2560

work page 2002
[26]

Stability and learning in excitatory synapses by nonlinear in- hibitory plasticity

Christoph Miehl and Julijana Gjorgjieva. “Stability and learning in excitatory synapses by nonlinear in- hibitory plasticity”. In:PLoS computational biology 18.12 (2022), e1010682

work page 2022
[27]

Predictive Coding Light

Antony W N’dri et al. “Predictive Coding Light”. In: Nature Communications16.1 (2025), p. 8880

work page 2025
[28]

Implications of

Antony W. N’dri et al. “Predictive Coding Light: learn- ing compact visual codes by combining excitatory and inhibitory spike timing-dependent plasticity*”. In:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)00 (2023), pp. 3997– 4006.DOI: 10.1109/cvprw59228.2023.00417

work page doi:10.1109/cvprw59228.2023.00417 2023
[29]

Spiking neural predictive coding for continually learning from data streams

Alexander Ororbia. “Spiking neural predictive coding for continually learning from data streams”. In:Neuro- computing544 (2023), p. 126292

work page 2023
[30]

Predictive cod- ing in the visual cortex: a functional interpretation of some extra-classical receptive-field effects

Rajesh PN Rao and Dana H Ballard. “Predictive cod- ing in the visual cortex: a functional interpretation of some extra-classical receptive-field effects”. In:Nature neuroscience2.1 (1999), pp. 79–87

work page 1999
[31]

Dynamic neural fields as a step toward cognitive neuromorphic architectures

Yulia Sandamirskaya. “Dynamic neural fields as a step toward cognitive neuromorphic architectures”. In:Fron- tiers in neuroscience7 (2014), p. 71560

work page 2014
[32]

Bidirectional recurrent neural networks

Mike Schuster and Kuldip K Paliwal. “Bidirectional recurrent neural networks”. In:IEEE transactions on Signal Processing45.11 (1997), pp. 2673–2681

work page 1997
[33]

Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network

Alex Sherstinsky. “Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network”. In:Physica D: Nonlinear Phenomena404 (2020), p. 132306

work page 2020
[34]

Deep learning in spiking neural networks

Amirhossein Tavanaei et al. “Deep learning in spiking neural networks”. In:Neural networks111 (2019), pp. 47–63

work page 2019
[35]

Competition for synaptic building blocks shapes synaptic plasticity

Jochen Triesch, Anh Duong V o, and Anne-Sophie Hafner. “Competition for synaptic building blocks shapes synaptic plasticity”. In:Elife7 (2018), e37836

work page 2018
[36]

Homeostatic plasticity in the developing nervous system

Gina G Turrigiano and Sacha B Nelson. “Homeostatic plasticity in the developing nervous system”. In:Nature reviews neuroscience5.2 (2004), pp. 97–107

work page 2004
[37]

In- dependent component filters of natural images com- pared with simple cells in primary visual cortex

J Hans Van Hateren and Arjen van der Schaaf. “In- dependent component filters of natural images com- pared with simple cells in primary visual cortex”. In: Proceedings of the Royal Society of London. Series B: Biological Sciences265.1394 (1998), pp. 359–366

work page 1998
[38]

Event based visual attention with dy- namic neural field on FPGA

Beno ˆıt Chappet de Vangel, Cesar Torres-Huitzil, and Bernard Girau. “Event based visual attention with dy- namic neural field on FPGA”. In:Proceedings of the 10th International Conference on Distributed Smart Camera. 2016, pp. 142–147

work page 2016
[39]

Attention is all you need

Ashish Vaswani et al. “Attention is all you need”. In: Advances in neural information processing systems30 (2017)

work page 2017
[40]

Hi- erarchical interactions between sensory cortices defy predictive coding

Jacob A Westerberg and Pieter R Roelfsema. “Hi- erarchical interactions between sensory cortices defy predictive coding”. In:Trends in Cognitive Sciences (2025)

work page 2025
[41]

Spiking neural networks and their applications: A review

Kashu Yamazaki et al. “Spiking neural networks and their applications: A review”. In:Brain sciences12.7 (2022), p. 863

work page 2022
[42]

The temporal paradox of Hebbian learning and homeostatic plasticity

Friedemann Zenke, Wulfram Gerstner, and Surya Gan- guli. “The temporal paradox of Hebbian learning and homeostatic plasticity”. In:Current opinion in neurobi- ology43 (2017), pp. 166–176. 12 Supplementary material a b Separate training or simultaneous training with plasticity gating Simultaneous training of all connections cause non-selective receptive fi...

work page 2017