Working memory capacity of crows and monkeys arises from similar neuronal computations

  1. Lukas Alexander Hahn
  2. Dmitry Balakhonov
  3. Erica Fongaro
  4. Andreas Nieder
  5. Jonas Rose

  • Curated by eLife

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    Evaluation Summary:

    In this study, Hahn et al. taught crows to perform a working memory task designed to mimic traditional monkey tasks. Using a combination of behavior and electrophysiology, the authors convincingly show that the neural mechanisms that limit working memory capacity in mammals and primates also limit working memory capacity in crows. What makes this finding particularly interesting is that the architecture of the avian brain is dramatically different than the architecture of the primate brain. Thus, two dramatically different architectures give rise to the same behavioral functions and neural computations. Such cross-species comparisons are fundamental to understanding the computational constraints that are placed on cognition and the brain.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to share their name with the authors.)

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Abstract

Complex cognition relies on flexible working memory, which is severely limited in its capacity. The neuronal computations underlying these capacity limits have been extensively studied in humans and in monkeys, resulting in competing theoretical models. We probed the working memory capacity of crows ( Corvus corone ) in a change detection task, developed for monkeys ( Macaca mulatta ), while we performed extracellular recordings of the prefrontal-like area nidopallium caudolaterale. We found that neuronal encoding and maintenance of information were affected by item load, in a way that is virtually identical to results obtained from monkey prefrontal cortex. Contemporary neurophysiological models of working memory employ divisive normalization as an important mechanism that may result in the capacity limitation. As these models are usually conceptualized and tested in an exclusively mammalian context, it remains unclear if they fully capture a general concept of working memory or if they are restricted to the mammalian neocortex. Here, we report that carrion crows and macaque monkeys share divisive normalization as a neuronal computation that is in line with mammalian models. This indicates that computational models of working memory developed in the mammalian cortex can also apply to non-cortical associative brain regions of birds.

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  1. Version published to 10.7554/elife.72783 on eLife
  2. eLife

    Evaluation Summary:

    In this study, Hahn et al. taught crows to perform a working memory task designed to mimic traditional monkey tasks. Using a combination of behavior and electrophysiology, the authors convincingly show that the neural mechanisms that limit working memory capacity in mammals and primates also limit working memory capacity in crows. What makes this finding particularly interesting is that the architecture of the avian brain is dramatically different than the architecture of the primate brain. Thus, two dramatically different architectures give rise to the same behavioral functions and neural computations. Such cross-species comparisons are fundamental to understanding the computational constraints that are placed on cognition and the brain.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to share their name with the authors.)

  3. eLife

    Reviewer #1 (Public Review):

    In this study, Hahn et al. taught crows to perform a working memory task designed to mimic traditional monkey tasks, where the birds had to use a touch screen and remember different numbers of stimuli across a delay. They recorded single neurons in the crow nidopallium caudolaterale (NCL) during task performance, and found changes in tuning with working memory load similar to those observed in the monkey dorsolateral prefrontal cortex. At the neural population level, increasing loads decreased stimulus information, and these changes could be explained as arising from divisive normalization. The authors conclude that there may be common neural mechanisms, that include stimulus tuning and divisive normalization, that have evolved in both primate and avian species to support multi-item working memory.

    Overall, I think that the premise of the study is interesting, the design and analyses are appropriate, and the conclusions drawn seem well-founded. The most interesting contributions of the paper are the comparative conclusions, particularly because they are counter to a common idea about primate dorsolateral prefrontal cortex and working memory. It's commonly believed that some neurophysiological properties of monkey dorsolateral prefrontal are unique, since a clear homolog doesn't exist in rodents, and it's often suggested that these properties arise from the cytoarchitecture of the region. This study shows that this is not the case, at least with respect to load-dependent effects on working memory, and the authors do an excellent job of elaborating on this point.

  4. eLife

    Reviewer #2 (Public Review):

    Recent work has provided insight into the neural mechanisms that limit the capacity of working memory. Recordings from non-human primates and functional imaging in humans, has found that increasing the number of items held in working memory reduces information about any individual item and shown that this reduction in information is due to divisive normalization of neural responses. The current manuscript builds on this work, showing that the same mechanisms are seen in crows. In brief, crows were trained on a change-detection task that required them to remember 2-5 objects over a short delay. Extracellular recordings from nidopallium caudolaterale (a prefrontal-like area) revealed neurons that were selective for the identity of the objects during visual presentation and during the memory delay. As with monkeys, the total amount of information about the identity of an object decreased as the animal was required to remember more objects. Finally, the authors provide evidence that this reduction in information is due to a divisive normalization like regularization.

    Overall, this is an interesting manuscript that extends the field in an exciting direction. This is a unique dataset and, by showing that similar mechanisms constrain the working memory capacity of a non-mammalian species, the results support the idea that there is a normative reason for these constraints that is constant across species. My largest concerns are around whether the authors convincingly demonstrate that divisive normalization can explain the changes in neural selectivity with increasing memory loads. In particular, the authors report a subset of neurons increase the amount of information they carry about a stimulus/memory as the number of items in working memory increases. It is not clear how this can be explained with a simple divisive normalization model.

  5. eLife

    Reviewer #3 (Public Review):

    The main purpose of the study was to examine the neural computations underlying working memory in the crow, and to compare these computations to those underlying working memory in primates. Through a series of careful analyses, the authors conclude that the neural computations underlying crow working memory, divisive normalization, is similar to that found in the primate brain.

    What makes these findings particularly interesting, of course, is that the architecture of the avian brain differs significantly from that of the mammalian brain. Namely, the primate brain has a layered cortical architecture, whereas the avian brain has a nuclear cluster architecture. Yet despite these divergent mechanisms at the architectural level, birds show convergent functions at the behavioural level and convergent computational functions at the neural level.

    The detailed and careful computational analyses are a strength of the paper. What is more difficult to know, owing to the fact that it is hard to track eye movements in birds, is how the findings relate to the receptive field of the NCL neurons. That said, what the authors have clearly shown is that neurons in a species very different from primates nevertheless display very primate-like neural computations.

    In terms of a larger picture, the finding that the neural computation underlying crow working memory is similar to that of primates despite a different neural architecture raises another interesting point. The clustering architecture, along with the fact that the neurons in the avian brain are generally smaller than neurons in the primate brain, results in birds having more neurons in their brains than a primate of the same size. Whether the greater number of neurons means the avian brain is computationally more efficient than the primate brain remains to be seen. One thing appears certain from the findings of this study: it is certainly no less computationally efficient.

  6. Version published to 10.1101/2021.08.17.456603v1 on bioRxiv