Oscillations without cortex: Working memory modulates brainwaves in the endbrain of crows

Hahn, Lukas Alexander; Balakhonov, Dmitry; Lundqvist, Mikael; Nieder, Andreas; Rose, Jonas

doi:10.1016/j.pneurobio.2022.102372

Cited by 8 publications

(5 citation statements)

References 77 publications

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“…Our findings support the notion that rhythmic neural activity can play a supporting role in cognitive phenomena. Brain waves during working memory are widely observed in neural systems of rats, primates, and birds [ 58 ]. Phase coding of information relative to theta oscillations has been proposed to be a general coding scheme in the brain [ 5 ].…”

Section: Discussionmentioning

confidence: 99%

Trained recurrent neural networks develop phase-locked limit cycles in a working memory task

Pals,

Macke,

Barak

2024

PLoS Comput Biol

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Neural oscillations are ubiquitously observed in many brain areas. One proposed functional role of these oscillations is that they serve as an internal clock, or ‘frame of reference’. Information can be encoded by the timing of neural activity relative to the phase of such oscillations. In line with this hypothesis, there have been multiple empirical observations of such phase codes in the brain. Here we ask: What kind of neural dynamics support phase coding of information with neural oscillations? We tackled this question by analyzing recurrent neural networks (RNNs) that were trained on a working memory task. The networks were given access to an external reference oscillation and tasked to produce an oscillation, such that the phase difference between the reference and output oscillation maintains the identity of transient stimuli. We found that networks converged to stable oscillatory dynamics. Reverse engineering these networks revealed that each phase-coded memory corresponds to a separate limit cycle attractor. We characterized how the stability of the attractor dynamics depends on both reference oscillation amplitude and frequency, properties that can be experimentally observed. To understand the connectivity structures that underlie these dynamics, we showed that trained networks can be described as two phase-coupled oscillators. Using this insight, we condensed our trained networks to a reduced model consisting of two functional modules: One that generates an oscillation and one that implements a coupling function between the internal oscillation and external reference. In summary, by reverse engineering the dynamics and connectivity of trained RNNs, we propose a mechanism by which neural networks can harness reference oscillations for working memory. Specifically, we propose that a phase-coding network generates autonomous oscillations which it couples to an external reference oscillation in a multi-stable fashion.

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Section: Discussionmentioning

confidence: 99%

Trained recurrent neural networks develop phase-locked limit cycles in a working memory task

Pals,

Macke,

Barak

2024

PLoS Comput Biol

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“…Several studies focusing on WM found no major differences between birds and mammals despite their long parallel evolution 42 , 79 , 80 . For instance, WM capacity and underlying neuronal computations were strikingly similar between crows and primates 2 , 38 , 40 . Due to their remarkable cognitive abilities and comparable WM dynamics, corvids are especially well suited as comparative animal model to probe recent models based on primate data.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies demonstrated that WM capacity and neuronal computations were largely similar in primates and crows 2,38 , despite approximately 320 million years of separate evolution 39 . Corvids were shown to master WM tasks in a similar fashion to primates 2,38,[40][41][42] and can successfully be trained on complex behavioral tasks [43][44][45][46] . Furthermore, as in primates, visual perception is an important sensory modality in many bird species 47,48 .…”

mentioning

confidence: 99%

Corvids optimize working memory by categorizing continuous stimuli

Apostel,

Panichello,

Buschman

et al. 2023

Commun Biol

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Working memory (WM) is a crucial element of the higher cognition of primates and corvid songbirds. Despite its importance, WM has a severely limited capacity and is vulnerable to noise. In primates, attractor dynamics mitigate the effect of noise by discretizing continuous information. Yet, it remains unclear whether similar dynamics are seen in avian brains. Here, we show jackdaws (Corvus monedula) have similar behavioral biases as humans; memories are less precise and more biased as memory demands increase. Model-based analysis reveal discrete attractors are evenly spread across the stimulus space. Altogether, our comparative approach suggests attractor dynamics in primates and corvids mitigate the effect of noise by systematically drifting towards specific attractors. By demonstrating this effect in an evolutionary distant species, our results strengthen attractor dynamics as general, adaptive biological principle to efficiently use WM.

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“…Our findings support the notion that rhythmic neural activity can play a supporting role in cognitive phenomena. Brain waves during working memory are widely observed in neural systems of rats, primates, and birds [57]. Phase coding of information relative to theta oscillations has been proposed to be a general coding scheme in the brain [5].…”

Section: Discussionmentioning

confidence: 99%

Trained recurrent neural networks develop phase-locked limit cycles in a working memory task

Pals

Macke

Barak

2023

Preprint

View full text Add to dashboard Cite

Neural oscillations are ubiquitously observed in many brain areas. One proposed functional role of these oscillations is that they serve as an internal clock, or 'frame of reference'. Information can be encoded by the timing of neural activity relative to the phase of such oscillations. In line with this hypothesis, there have been multiple empirical observations of such phase codes in the brain. Here we ask: What kind of neural dynamics support phase coding of information with neural oscillations? We tackled this question by analyzing recurrent neural networks (RNNs) that were trained on a working memory task. The networks were given access to an external reference oscillation and tasked to produce an oscillation, such that the phase difference between the reference and output oscillation maintains the identity of transient stimuli. We found that networks converged to stable oscillatory dynamics. Reverse engineering these networks revealed that each phase-coded memory corresponds to a separate limit cycle attractor. We characterized how the stability of the attractor dynamics depends on both reference oscillation amplitude and frequency, properties that can be experimentally observed. To understand the connectivity structures that underlie these dynamics, we showed that trained networks can be described as two phase-coupled oscillators. Using this insight, we condensed our trained networks to a reduced model consisting of two functional modules: One that generates an oscillation and one that implements a coupling function between the internal oscillation and external reference. In summary, by reverse engineering the dynamics and connectivity of trained RNNs, we propose a mechanism by which neural networks can harness reference oscillations for working memory. Specifically, we propose that a phase-coding network generates autonomous oscillations which it couples to an external reference oscillation in a multi-stable fashion.

show abstract

Oscillations without cortex: Working memory modulates brainwaves in the endbrain of crows

Cited by 8 publications

References 77 publications

Trained recurrent neural networks develop phase-locked limit cycles in a working memory task

Trained recurrent neural networks develop phase-locked limit cycles in a working memory task

Corvids optimize working memory by categorizing continuous stimuli

Trained recurrent neural networks develop phase-locked limit cycles in a working memory task

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