An active neural mechanism for relational learning and fast knowledge reassembly

Miconi, Thomas; Kay, Kenneth

doi:10.1101/2023.07.27.550739

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…While this learning model does not recapitulate behavioral patterns such as the symbolic distance effect, it can explain how subjects may learn transitive inference from a minimal number of trials. Finally, whereas both the reactivation-based and statistical learning models described above rely on emergent inductive biases of the underlying learning mechanisms, learning systems can also develop relational inductive biases through structure learning or meta-learning [122,123], which are associated with both hippocampus and prefrontal cortex [115,[124][125][126].…”

Section: Discussionmentioning

confidence: 99%

A mathematical theory of relational generalization in transitive inference

Lippl,

Kay,

Jensen

et al. 2023

Preprint

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Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items ("compositional generalization"). This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced cognition. However, how learning systems (including the brain) can implement the necessary inductive biases has been unclear. Here we investigated transitive inference (TI), a classic relational task paradigm in which subjects must learn a relation (A>B and B>C) and generalize it to new combinations of items (A>C). Through mathematical analysis, we found that a broad range of biologically relevant learning models (e.g. gradient flow or ridge regression) perform TI successfully and recapitulate signature behavioral patterns long observed in living subjects. First, we found that models with item-wise additive representations automatically encode transitive relations. Second, for more general representations, a single scalar "conjunctivity factor" determines model behavior on TI and, further, the principle of norm minimization (a standard statistical inductive bias) enables models with fixed, partly conjunctive representations to generalize transitively. Finally, neural networks in the "rich regime," which enables representation learning and often leads to better generalization, deviate in task behavior from living subjects and can make generalization errors. Our findings show systematically how minimal statistical learning principles can explain the rich behaviors empirically observed in TI in living subjects, uncover the mechanistic basis of transitive generalization in standard learning models, and lay out a formally tractable approach to understanding the neural basis of relational generalization.

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Section: Discussionmentioning

confidence: 99%

A mathematical theory of relational generalization in transitive inference

Lippl,

Kay,

Jensen

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

A mathematical theory of relational generalization in transitive inference

Lippl,

Kay,

Jensen

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

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Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced cognition. However, how learning systems (including the brain) can implement the necessary inductive biases has been unclear. We investigated transitive inference (TI), a classic relational task paradigm in which subjects must learn a relation ( A > B and B > C ) and generalize it to new combinations of items ( A > C ). Through mathematical analysis, we found that a broad range of biologically relevant learning models (e.g. gradient flow or ridge regression) perform TI successfully and recapitulate signature behavioral patterns long observed in living subjects. First, we found that models with item-wise additive representations automatically encode transitive relations. Second, for more general representations, a single scalar “conjunctivity factor” determines model behavior on TI and, further, the principle of norm minimization (a standard statistical inductive bias) enables models with fixed, partly conjunctive representations to generalize transitively. Finally, neural networks in the “rich regime,” which enables representation learning and improves generalization on many tasks, unexpectedly show poor generalization and anomalous behavior on TI. We find that such networks implement a form of norm minimization (over hidden weights) that yields a local encoding mechanism lacking transitivity. Our findings show how minimal statistical learning principles give rise to a classical relational inductive bias (transitivity), explain empirically observed behaviors, and establish a formal approach to understanding the neural basis of relational abstraction.

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Non-synaptic plasticity enables memory-dependent local learning

Romain,

Maximilian,

Florian

et al. 2023

Preprint

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Synaptic plasticity is essential for memory formation and learning in the brain. In addition, recent results indicate that non-synaptic plasticity processes such as the regulation of neural membrane properties contribute to memory formation, its functional role in memory and learning has however remained elusive. Here, we propose that non-synaptic and synaptic plasticity are both essential components to enable memory-dependent processing in neuronal networks. While the former acts on a fast time scale for rapid information storage, the latter shapes network processing on a slower time scale to harness this memory as a functional component. We analyse this concept in a network model where pyramidal neurons regulate their apical trunk excitability in a Hebbian manner. We find that local synaptic plasticity rules can be derived for this model and show that the interplay between this synaptic plasticity and the non-synaptic trunk plasticity enables the model to successfully accommodate memory-dependent processing capabilities in a number of tasks, ranging from simple memory tests to question answering. The model can also explain contextual fear conditioning experiments, where freezing responses could be recovered by optogenetic reactivation of memory engrams under amnesia.Author summaryHow memory is organized in the brain in order to enable cognitive processing is a central open question in systems neuroscience. Traditionally, synaptic plasticity is considered the key mechanism for the establishment of memory in the brain. Recently however, this view has been questioned, and it was proposed that non-synaptic plasticity mechanisms play a more prominent role as previously considered. In this article, we propose that both, synaptic and non-synaptic plasticity are central components for the formation and utilization of memory in biological neuronal networks. Our results show that non-synaptic plasticity can act on a fast time-scale to store important information, while synaptic plasticity can adapt network function on a slow time scale in order to facilitate memory-dependent cognitive processing.

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An active neural mechanism for relational learning and fast knowledge reassembly

Cited by 3 publications

References 34 publications

A mathematical theory of relational generalization in transitive inference

A mathematical theory of relational generalization in transitive inference

A mathematical theory of relational generalization in transitive inference

Non-synaptic plasticity enables memory-dependent local learning

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