Neuromodulator-dependent synaptic tagging and capture retroactively controls neural coding in spiking neural networks

Lehr, Andrew B.; Luboeinski, Jannik; Tetzlaff, Christian

doi:10.1038/s41598-022-22430-7

Cited by 12 publications

(6 citation statements)

References 105 publications

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“…It can also be placed in the more recent context of bidirectional dynamic processes (Benna & Fusi, 2016), where the weight pushes-pulls µ and in turn high values µ stabilize the weight. We have restricted our analysis to the interplay between fliopodia and spines, but the model could be extended to include other well-known processes of synaptic consolidation that would affect spines (as could be the experimentally (Frey & Morris, 1997; Redondo & Morris, 2011) and computationally (Clopath et al, 2008; Ziegler et al, 2015; Lehr et al, 2022) tested Synaptic Tagging and Capture (STC) mechanism).…”

Section: Discussionmentioning

confidence: 99%

Learning with filopodia and spines

Albesa-González,

Clopath

2023

Preprint

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Filopodia are thin synaptic protrusions that have been long known to play an important role in early development. It has recently been found that they are more abundant in the adult cortex than previously thought, and more plastic than spines (button-shaped mature synapses). Inspired by these findings, we introduce a new model of synaptic plasticity that jointly describes learning of filopodia and spines. The model assumes that filopodia exhibit additive learning, which is highly competitive and volatile. At the same time, it proposes that if filopodia undergo sufficient potentiation they consolidate into spines, and start following multiplicative learning dynamics. This makes spines more stable and sensitive to the fine structure of input correlations. We show that our learning rule has a selectivity comparable to additive spike-timing-dependent plasticity (STDP) and represents input correlations as well as multiplicative STDP. We also show how it can protect previously formed memories and act as a synaptic consolidation mechanism. Overall, our results provide a mechanistic explanation of how filopodia and spines could cooperate to overcome the difficulties that these separate forms of learning (additive and multiplicative) each have.Author SummaryChanges in the strength of synaptic connections between neurons are the basis of learning in biological and artificial networks. In animals, these changes can only depend on locally available signals, and are usually modeled withlearning rules. Based on recent discoveries onfilopodia, a special type of synaptic structure, we propose a new learning rule called Filopodium-Spine spike-timing-dependent-plasticity. Our rule proposes that filopodia follow additive STDP and spines (mature synapses) multiplicative STDP. We show that our model overcomes classic difficulties that these learning rules have separately, like the absence of stability or specificity, and can also be seen as a first stage of synaptic consolidation.

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Section: Discussionmentioning

confidence: 99%

Learning with filopodia and spines

Albesa-González,

Clopath

2023

Preprint

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“…Weak sequences may also compensate for small assembly sizes by potentiating recurrent interactions, weakening feed-forward inhibition, or recruiting more neurons (assembly outgrowth, see Tetzlaff et al, 2015; Lehr et al, 2022). Here, the underlying learning scenario is highly simplified.…”

Section: Discussionmentioning

confidence: 99%

Competition and Cooperation of Assembly Sequences in Recurrent Neural Networks

Stöber,

Lehr,

Fyhn

et al. 2023

Preprint

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Neural activity sequences are ubiquitous in the brain and play pivotal roles in functions such as long-term memory formation and motor control. While conditions for storing and reactivating individual sequences have been thoroughly characterized, it remains unclear how multiple sequences may interact when activated simultaneously in recurrent neural networks. This question is especially relevant for weak sequences, comprised of fewer neurons, competing against strong sequences. Using a non-linear rate model with discrete, pre-configured assemblies, we demonstrate that weak sequences can compensate for their competitive disadvantage either by increasing excitatory connections between subsequent assemblies or by cooperating with other co-active sequences. Further, our model suggests that such cooperation can negatively affect sequence speed unless subsequently active assemblies are paired. Our analysis, validated by an analytically tractable linear approximation, characterizes the conditions for successful sequence progression in isolated, competing, and cooperating sequences, and identifies the distinct contributions of recurrent and feed-forward projections. This proof-of-principle study shows how even disadvantaged sequences can be prioritized for reactivation, a process which has recently been implicated in hippocampal memory processing.

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“…Stimulus driven annealing ought to be able to act rather on longer time-scales because the animal may only now and then encounter the relevant stimuli. Longer lasting reduction of LTP could be obtained by mechanisms that operate on its later phases (late-LTP) [39][40][41][42] suspected to be essential for establishing synaptic consolidation. However, any potential role of this mechanisms in meta-plasticity related to annealing effects remains unknown.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Unsupervised learning of perceptual feature combinations

Tamosiunaite,

Tetzlaff,

Wörgötter

2024

PLoS Comput Biol

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In many situations it is behaviorally relevant for an animal to respond to co-occurrences of perceptual, possibly polymodal features, while these features alone may have no importance. Thus, it is crucial for animals to learn such feature combinations in spite of the fact that they may occur with variable intensity and occurrence frequency. Here, we present a novel unsupervised learning mechanism that is largely independent of these contingencies and allows neurons in a network to achieve specificity for different feature combinations. This is achieved by a novel correlation-based (Hebbian) learning rule, which allows for linear weight growth and which is combined with a mechanism for gradually reducing the learning rate as soon as the neuron’s response becomes feature combination specific. In a set of control experiments, we show that other existing advanced learning rules cannot satisfactorily form ordered multi-feature representations. In addition, we show that networks, which use this type of learning always stabilize and converge to subsets of neurons with different feature-combination specificity. Neurons with this property may, thus, serve as an initial stage for the processing of ecologically relevant real world situations for an animal.

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Neuromodulator-dependent synaptic tagging and capture retroactively controls neural coding in spiking neural networks

Cited by 12 publications

References 105 publications

Learning with filopodia and spines

Learning with filopodia and spines

Competition and Cooperation of Assembly Sequences in Recurrent Neural Networks

Unsupervised learning of perceptual feature combinations

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