ArticleNature neuroscience2023
The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks.
Article in Nature neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Noisy models of the ventral stream reveal the impact of recurrence and learned representations on information processing timescales.PLoS computational biology · 2026Article
- Hierarchical optimization predicts plasticity in the macaque inferior temporal cortex following object training.Nature communications · 2026Article
- Neural timescales from a computational perspective.Nature neuroscience · 2026Review
- Flexible navigation with neuromodulated cognitive maps.PLoS computational biology · 2026Article
- Overvalued Ideas: Conceptual Analysis and Literature Review.Behavioral sciences (Basel, Switzerland) · 2026Review
- Cellular and subcellular specialization enables biology-constrained deep learning.Cell reports · 2026Article
- Intrinsic stabilization of synaptic plasticity improves learning and robustness in artificial neural networks.Nature communications · 2026Article
- Global error signal guides local optimization in mismatch calculation.Nature communications · 2026Article
- Voltage imaging as a window into neural computation.Neurophotonics · 2026Article
- Slow synaptic plasticity from the hippocampus underlies gradual mapping and fragmentation of novel spaces by grid cells.bioRxiv : the preprint server for biology · 2025Article
- Neuromorphic computing for robotic vision: algorithms to hardware advances.Communications engineering · 2025Review
- Self-supervised predictive learning accounts for cortical layer-specificity.Nature communications · 2025Article
- Self-Contrastive Forward-Forward algorithm.Nature communications · 2025Article
- Article
- Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks.PLoS computational biology · 2025Article
- Article
- A biological model of nonlinear dimensionality reduction.Science advances · 2025Article
- Actor-critic networks with analogue memristors mimicking reward-based learning.Nature machine intelligence · 2025Article
- Vagus nerve stimulation as a predictive coding modulator that enhances feedforward over feedback transmission.Frontiers in neural circuits · 2025Review
- Helpless infants are learning a foundation model.Trends in cognitive sciences · 2024Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Recognition of objects from sensory stimuli is essential for survival. To that end, sensory networks in the brain must form object representations invariant to stimulus changes, such as size, orientation and context. Although Hebbian plasticity is known to shape sensory networks, it fails to create invariant object representations in computational models, raising the question of how the brain achieves such processing. In the present study, we show that combining Hebbian plasticity with a predictive form of plasticity leads to invariant representations in deep neural network models. We derive a local learning rule that generalizes to spiking neural networks and naturally accounts for several experimentally observed properties of synaptic plasticity, including metaplasticity and spike-timing-dependent plasticity. Finally, our model accurately captures neuronal selectivity changes observed in the primate inferotemporal cortex in response to altered visual experience. Thus, we provide a plausible normative theory emphasizing the importance of predictive plasticity mechanisms for successful representational learning.
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