1
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Kim JZ, Larsen B, Parkes L. Shaping dynamical neural computations using spatiotemporal constraints. Biochem Biophys Res Commun 2024; 728:150302. [PMID: 38968771 DOI: 10.1016/j.bbrc.2024.150302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/21/2024] [Accepted: 04/11/2024] [Indexed: 07/07/2024]
Abstract
Dynamics play a critical role in computation. The principled evolution of states over time enables both biological and artificial networks to represent and integrate information to make decisions. In the past few decades, significant multidisciplinary progress has been made in bridging the gap between how we understand biological versus artificial computation, including how insights gained from one can translate to the other. Research has revealed that neurobiology is a key determinant of brain network architecture, which gives rise to spatiotemporally constrained patterns of activity that underlie computation. Here, we discuss how neural systems use dynamics for computation, and claim that the biological constraints that shape brain networks may be leveraged to improve the implementation of artificial neural networks. To formalize this discussion, we consider a natural artificial analog of the brain that has been used extensively to model neural computation: the recurrent neural network (RNN). In both the brain and the RNN, we emphasize the common computational substrate atop which dynamics occur-the connectivity between neurons-and we explore the unique computational advantages offered by biophysical constraints such as resource efficiency, spatial embedding, and neurodevelopment.
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Affiliation(s)
- Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY, 14853, USA.
| | - Bart Larsen
- Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, USA
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ, 08854, USA.
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2
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Alleman M, Panichello M, Buschman TJ, Johnston WJ. The neural basis of swap errors in working memory. Proc Natl Acad Sci U S A 2024; 121:e2401032121. [PMID: 39102534 DOI: 10.1073/pnas.2401032121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 06/03/2024] [Indexed: 08/07/2024] Open
Abstract
When making decisions in a cluttered world, humans and other animals often have to hold multiple items in memory at once-such as the different items on a shopping list. Psychophysical experiments in humans and other animals have shown remembered stimuli can sometimes become confused, with participants reporting chimeric stimuli composed of features from different stimuli. In particular, subjects will often make "swap errors" where they misattribute a feature from one object as belonging to another object. While swap errors have been described behaviorally and theoretical explanations have been proposed, their neural mechanisms are unknown. Here, we elucidate these neural mechanisms by analyzing neural population recordings from monkeys performing two multistimulus working memory tasks. In these tasks, monkeys were cued to report the color of an item that either was previously shown at a corresponding location or will be shown at the corresponding location. Animals made swap errors in both tasks. In the neural data, we find evidence that the neural correlates of swap errors emerged when correctly remembered information is selected from working memory. This led to a representation of the distractor color as if it were the target color, underlying the eventual swap error. We did not find consistent evidence that swap errors arose from misinterpretation of the cue or errors during encoding or storage in working memory. These results provide evidence that swap errors emerge during selection of correctly remembered information from working memory, and highlight this selection as a crucial-yet surprisingly brittle-neural process.
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Affiliation(s)
- Matteo Alleman
- Department of Neuroscience, Center for Theoretical Neuroscience and Zuckerman Mind, Brain, and Behavior Institute, Columbia University, New York, NY 10027
| | - Matthew Panichello
- Department of Neurobiology, Stanford University, Stanford, CA 94305
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ 08544
| | - Timothy J Buschman
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ 08544
| | - W Jeffrey Johnston
- Department of Neuroscience, Center for Theoretical Neuroscience and Zuckerman Mind, Brain, and Behavior Institute, Columbia University, New York, NY 10027
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3
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Ibañez S, Sengupta N, Luebke JI, Wimmer K, Weaver CM. Myelin dystrophy impairs signal transmission and working memory in a multiscale model of the aging prefrontal cortex. eLife 2024; 12:RP90964. [PMID: 39028036 PMCID: PMC11259433 DOI: 10.7554/elife.90964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024] Open
Abstract
Normal aging leads to myelin alterations in the rhesus monkey dorsolateral prefrontal cortex (dlPFC), which are positively correlated with degree of cognitive impairment. It is hypothesized that remyelination with shorter and thinner myelin sheaths partially compensates for myelin degradation, but computational modeling has not yet explored these two phenomena together systematically. Here, we used a two-pronged modeling approach to determine how age-related myelin changes affect a core cognitive function: spatial working memory. First, we built a multicompartment pyramidal neuron model fit to monkey dlPFC empirical data, with an axon including myelinated segments having paranodes, juxtaparanodes, internodes, and tight junctions. This model was used to quantify conduction velocity (CV) changes and action potential (AP) failures after demyelination and subsequent remyelination. Next, we incorporated the single neuron results into a spiking neural network model of working memory. While complete remyelination nearly recovered axonal transmission and network function to unperturbed levels, our models predict that biologically plausible levels of myelin dystrophy, if uncompensated by other factors, can account for substantial working memory impairment with aging. The present computational study unites empirical data from ultrastructure up to behavior during normal aging, and has broader implications for many demyelinating conditions, such as multiple sclerosis or schizophrenia.
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Affiliation(s)
- Sara Ibañez
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of MedicineBostonUnited States
- Centre de Recerca Matemàtica, Edifici C, Campus BellaterraBellaterraSpain
- Departament de Matemàtiques, Universitat Autònoma de Barcelona, Edifici CBellaterraSpain
| | - Nilapratim Sengupta
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of MedicineBostonUnited States
- Department of Mathematics, Franklin and Marshall CollegeLancasterUnited States
| | - Jennifer I Luebke
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of MedicineBostonUnited States
| | - Klaus Wimmer
- Centre de Recerca Matemàtica, Edifici C, Campus BellaterraBellaterraSpain
- Departament de Matemàtiques, Universitat Autònoma de Barcelona, Edifici CBellaterraSpain
| | - Christina M Weaver
- Department of Mathematics, Franklin and Marshall CollegeLancasterUnited States
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4
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Stroud JP, Duncan J, Lengyel M. The computational foundations of dynamic coding in working memory. Trends Cogn Sci 2024; 28:614-627. [PMID: 38580528 DOI: 10.1016/j.tics.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/29/2024] [Accepted: 02/29/2024] [Indexed: 04/07/2024]
Abstract
Working memory (WM) is a fundamental aspect of cognition. WM maintenance is classically thought to rely on stable patterns of neural activities. However, recent evidence shows that neural population activities during WM maintenance undergo dynamic variations before settling into a stable pattern. Although this has been difficult to explain theoretically, neural network models optimized for WM typically also exhibit such dynamics. Here, we examine stable versus dynamic coding in neural data, classical models, and task-optimized networks. We review principled mathematical reasons for why classical models do not, while task-optimized models naturally do exhibit dynamic coding. We suggest an update to our understanding of WM maintenance, in which dynamic coding is a fundamental computational feature rather than an epiphenomenon.
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Affiliation(s)
- Jake P Stroud
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK.
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, UK; Center for Cognitive Computation, Department of Cognitive Science, Central European University, Budapest, Hungary
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5
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Monov G, Stein H, Klock L, Gallinat J, Kühn S, Lincoln T, Krkovic K, Murphy PR, Donner TH. Linking Cognitive Integrity to Working Memory Dynamics in the Aging Human Brain. J Neurosci 2024; 44:e1883232024. [PMID: 38760163 PMCID: PMC11211717 DOI: 10.1523/jneurosci.1883-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 04/13/2024] [Accepted: 04/18/2024] [Indexed: 05/19/2024] Open
Abstract
Aging is accompanied by a decline of working memory, an important cognitive capacity that involves stimulus-selective neural activity that persists after stimulus presentation. Here, we unraveled working memory dynamics in older human adults (male and female) including those diagnosed with mild cognitive impairment (MCI) using a combination of behavioral modeling, neuropsychological assessment, and MEG recordings of brain activity. Younger adults (male and female) were studied with behavioral modeling only. Participants performed a visuospatial delayed match-to-sample task under systematic manipulation of the delay and distance between sample and test stimuli. Their behavior (match/nonmatch decisions) was fit with a computational model permitting the dissociation of noise in the internal operations underlying the working memory performance from a strategic decision threshold. Task accuracy decreased with delay duration and sample/test proximity. When sample/test distances were small, older adults committed more false alarms than younger adults. The computational model explained the participants' behavior well. The model parameters reflecting internal noise (not decision threshold) correlated with the precision of stimulus-selective cortical activity measured with MEG during the delay interval. The model uncovered an increase specifically in working memory noise in older compared with younger participants. Furthermore, in the MCI group, but not in the older healthy controls, internal noise correlated with the participants' clinically assessed cognitive integrity. Our results are consistent with the idea that the stability of working memory contents deteriorates in aging, in a manner that is specifically linked to the overall cognitive integrity of individuals diagnosed with MCI.
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Affiliation(s)
- Gina Monov
- Section of Computational Cognitive Neuroscience, Department of Neurophysiology & Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
| | - Henrik Stein
- Department of Psychiatry, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
| | - Leonie Klock
- Department of Psychiatry, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
| | - Juergen Gallinat
- Department of Psychiatry, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
| | - Simone Kühn
- Department of Psychiatry, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
| | - Tania Lincoln
- Department of Clinical Psychology and Psychotherapy, Institute of Psychology, University of Hamburg, Hamburg 20146, Germany
| | - Katarina Krkovic
- Department of Clinical Psychology and Psychotherapy, Institute of Psychology, University of Hamburg, Hamburg 20146, Germany
| | - Peter R Murphy
- Section of Computational Cognitive Neuroscience, Department of Neurophysiology & Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
- Department of Psychology, Maynooth University, Co. Kildare, Ireland
| | - Tobias H Donner
- Section of Computational Cognitive Neuroscience, Department of Neurophysiology & Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
- Bernstein Center for Computational Neuroscience, Charité Universitätsmedizin, Berlin 10115, Germany
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6
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Huang Y, Brosch M. Behavior-related visual activations in the auditory cortex of nonhuman primates. Prog Neurobiol 2024; 240:102637. [PMID: 38879074 DOI: 10.1016/j.pneurobio.2024.102637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 05/29/2024] [Accepted: 06/06/2024] [Indexed: 06/22/2024]
Abstract
While it is well established that sensory cortical regions traditionally thought to be unimodal can be activated by stimuli from modalities other than the dominant one, functions of such foreign-modal activations are still not clear. Here we show that visual activations in early auditory cortex can be related to whether or not the monkeys engaged in audio-visual tasks, to the time when the monkeys reacted to the visual component of such tasks, and to the correctness of the monkeys' response to the auditory component of such tasks. These relationships between visual activations and behavior suggest that auditory cortex can be recruited for visually-guided behavior and that visual activations can prime auditory cortex such that it is prepared for processing future sounds. Our study thus provides evidence that foreign-modal activations in sensory cortex can contribute to a subject's ability to perform tasks on stimuli from foreign and dominant modalities.
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Affiliation(s)
- Ying Huang
- Research Group Comparative Neuroscience, Leibniz Institute for Neurobiology, Brenneckestraße 6, Magdeburg 39118, Germany.
| | - Michael Brosch
- Research Group Comparative Neuroscience, Leibniz Institute for Neurobiology, Brenneckestraße 6, Magdeburg 39118, Germany; Center for Behavioral Brain Sciences, Otto-von-Guericke-University, Universitätsplatz 2, Magdeburg 39106, Germany
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7
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Bays PM, Schneegans S, Ma WJ, Brady TF. Representation and computation in visual working memory. Nat Hum Behav 2024; 8:1016-1034. [PMID: 38849647 DOI: 10.1038/s41562-024-01871-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 03/22/2024] [Indexed: 06/09/2024]
Abstract
The ability to sustain internal representations of the sensory environment beyond immediate perception is a fundamental requirement of cognitive processing. In recent years, debates regarding the capacity and fidelity of the working memory (WM) system have advanced our understanding of the nature of these representations. In particular, there is growing recognition that WM representations are not merely imperfect copies of a perceived object or event. New experimental tools have revealed that observers possess richer information about the uncertainty in their memories and take advantage of environmental regularities to use limited memory resources optimally. Meanwhile, computational models of visuospatial WM formulated at different levels of implementation have converged on common principles relating capacity to variability and uncertainty. Here we review recent research on human WM from a computational perspective, including the neural mechanisms that support it.
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Affiliation(s)
- Paul M Bays
- Department of Psychology, University of Cambridge, Cambridge, UK
| | | | - Wei Ji Ma
- Center for Neural Science and Department of Psychology, New York University, New York, NY, USA
| | - Timothy F Brady
- Department of Psychology, University of California, San Diego, La Jolla, CA, USA.
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8
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Salinas E, Sheikh BI. Memorable first impressions. eLife 2024; 13:e98274. [PMID: 38700912 PMCID: PMC11068352 DOI: 10.7554/elife.98274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/05/2024] Open
Abstract
Our ability to recall details from a remembered image depends on a single mechanism that is engaged from the very moment the image disappears from view.
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Affiliation(s)
- Emilio Salinas
- Department of Translational Neuroscience, Wake Forest University School of MedicineWinston-SalemUnited States
| | - Bashirul I Sheikh
- Neuroscience Graduate Program, Wake Forest University School of MedicineWinston-SalemUnited States
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9
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Abstract
Probing memory of a complex visual image within a few hundred milliseconds after its disappearance reveals significantly greater fidelity of recall than if the probe is delayed by as little as a second. Classically interpreted, the former taps into a detailed but rapidly decaying visual sensory or 'iconic' memory (IM), while the latter relies on capacity-limited but comparatively stable visual working memory (VWM). While iconic decay and VWM capacity have been extensively studied independently, currently no single framework quantitatively accounts for the dynamics of memory fidelity over these time scales. Here, we extend a stationary neural population model of VWM with a temporal dimension, incorporating rapid sensory-driven accumulation of activity encoding each visual feature in memory, and a slower accumulation of internal error that causes memorized features to randomly drift over time. Instead of facilitating read-out from an independent sensory store, an early cue benefits recall by lifting the effective limit on VWM signal strength imposed when multiple items compete for representation, allowing memory for the cued item to be supplemented with information from the decaying sensory trace. Empirical measurements of human recall dynamics validate these predictions while excluding alternative model architectures. A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.
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Affiliation(s)
- Ivan Tomić
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
- Department of Psychology, Faculty of Humanities and Social Sciences, University of ZagrebZagrebCroatia
| | - Paul M Bays
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
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10
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Penny W. Stochastic attractor models of visual working memory. PLoS One 2024; 19:e0301039. [PMID: 38568927 PMCID: PMC10990203 DOI: 10.1371/journal.pone.0301039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 03/10/2024] [Indexed: 04/05/2024] Open
Abstract
This paper investigates models of working memory in which memory traces evolve according to stochastic attractor dynamics. These models have previously been shown to account for response-biases that are manifest across multiple trials of a visual working memory task. Here we adapt this approach by making the stable fixed points correspond to the multiple items to be remembered within a single-trial, in accordance with standard dynamical perspectives of memory, and find evidence that this multi-item model can provide a better account of behavioural data from continuous-report tasks. Additionally, the multi-item model proposes a simple mechanism by which swap-errors arise: memory traces diffuse away from their initial state and are captured by the attractors of other items. Swap-error curves reveal the evolution of this process as a continuous function of time throughout the maintenance interval and can be inferred from experimental data. Consistent with previous findings, we find that empirical memory performance is not well characterised by a purely-diffusive process but rather by a stochastic process that also embodies error-correcting dynamics.
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Affiliation(s)
- W. Penny
- School of Psychology, University East Anglia, Norwich, United Kingdom
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11
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Mozumder R, Chung S, Li S, Constantinidis C. Contributions of narrow- and broad-spiking prefrontal and parietal neurons on working memory tasks. Front Syst Neurosci 2024; 18:1365622. [PMID: 38577690 PMCID: PMC10991738 DOI: 10.3389/fnsys.2024.1365622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 03/11/2024] [Indexed: 04/06/2024] Open
Abstract
Neurons that generate persistent activity in the primate dorsolateral prefrontal and posterior parietal cortex have been shown to be predictive of behavior in working memory tasks, though subtle differences between them have been observed in how information is represented. The role of different neuron types in each of these areas has not been investigated at depth. We thus compared the activity of neurons classified as narrow-spiking, putative interneurons, and broad-spiking, putative pyramidal neurons, recorded from the dorsolateral prefrontal and posterior parietal cortex of male monkeys, to analyze their role in the maintenance of working memory. Our results demonstrate that narrow-spiking neurons are active during a range of tasks and generate persistent activity during the delay period over which stimuli need to be maintained in memory. Furthermore, the activity of narrow-spiking neurons was predictive of the subject's recall no less than that of broad-spiking neurons, which are exclusively projection neurons in the cortex. Our results show that putative interneurons play an active role during the maintenance of working memory and shed light onto the fundamental neural circuits that determine subjects' memories and judgments.
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Affiliation(s)
- Rana Mozumder
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Sophia Chung
- Neuroscience Program, Vanderbilt University, Nashville, TN, United States
| | - Sihai Li
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - Christos Constantinidis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
- Neuroscience Program, Vanderbilt University, Nashville, TN, United States
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
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12
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Secer G, Knierim JJ, Cowan NJ. Continuous Bump Attractor Networks Require Explicit Error Coding for Gain Recalibration. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.12.579874. [PMID: 38562699 PMCID: PMC10983875 DOI: 10.1101/2024.02.12.579874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Representations of continuous variables are crucial to create internal models of the external world. A prevailing model of how the brain maintains these representations is given by continuous bump attractor networks (CBANs) in a broad range of brain functions across different areas, such as spatial navigation in hippocampal/entorhinal circuits and working memory in prefrontal cortex. Through recurrent connections, a CBAN maintains a persistent activity bump, whose peak location can vary along a neural space, corresponding to different values of a continuous variable. To track the value of a continuous variable changing over time, a CBAN updates the location of its activity bump based on inputs that encode the changes in the continuous variable (e.g., movement velocity in the case of spatial navigation)-a process akin to mathematical integration. This integration process is not perfect and accumulates error over time. For error correction, CBANs can use additional inputs providing ground-truth information about the continuous variable's correct value (e.g., visual landmarks for spatial navigation). These inputs enable the network dynamics to automatically correct any representation error. Recent experimental work on hippocampal place cells has shown that, beyond correcting errors, ground-truth inputs also fine-tune the gain of the integration process, a crucial factor that links the change in the continuous variable to the updating of the activity bump's location. However, existing CBAN models lack this plasticity, offering no insights into the neural mechanisms and representations involved in the recalibration of the integration gain. In this paper, we explore this gap by using a ring attractor network, a specific type of CBAN, to model the experimental conditions that demonstrated gain recalibration in hippocampal place cells. Our analysis reveals the necessary conditions for neural mechanisms behind gain recalibration within a CBAN. Unlike error correction, which occurs through network dynamics based on ground-truth inputs, gain recalibration requires an additional neural signal that explicitly encodes the error in the network's representation via a rate code. Finally, we propose a modified ring attractor network as an example CBAN model that verifies our theoretical findings. Combining an error-rate code with Hebbian synaptic plasticity, this model achieves recalibration of integration gain in a CBAN, ensuring accurate representation for continuous variables.
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Affiliation(s)
- Gorkem Secer
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD 21218, USA
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA
| | - James J Knierim
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Noah J Cowan
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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13
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Singh B, Wang Z, Madiah LM, Gatti SE, Fulton JN, Johnson GW, Li R, Dawant BM, Englot DJ, Bick SK, Roberson SW, Constantinidis C. Brain-wide human oscillatory local field potential activity during visual working memory. iScience 2024; 27:109130. [PMID: 38380249 PMCID: PMC10877957 DOI: 10.1016/j.isci.2024.109130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/10/2024] [Accepted: 02/01/2024] [Indexed: 02/22/2024] Open
Abstract
Oscillatory activity in the local field potential (LFP) is thought to be a marker of cognitive processes. To understand how it differentiates tasks and brain areas in humans, we recorded LFPs in 15 adults with intracranial depth electrodes, as they performed visual-spatial and shape working memory tasks. Stimulus appearance produced widespread, broad-band activation, including in occipital, parietal, temporal, insular, and prefrontal cortex, and the amygdala and hippocampus. Occipital cortex was characterized by most elevated power in the high-gamma (100-150 Hz) range during the visual stimulus presentation. The most consistent feature of the delay period was a systematic pattern of modulation in the beta frequency (16-40 Hz), which included a decrease in power of variable timing across areas, and rebound during the delay period. These results reveal the widespread nature of oscillatory activity across a broad brain network and region-specific signatures of oscillatory processes associated with visual working memory.
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Affiliation(s)
- Balbir Singh
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Zhengyang Wang
- Neuroscience Program, Vanderbilt University, Nashville, TN, USA
| | - Leen M. Madiah
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - S. Elizabeth Gatti
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Jenna N. Fulton
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Graham W. Johnson
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rui Li
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Benoit M. Dawant
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Dario J. Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sarah K. Bick
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shawniqua Williams Roberson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Christos Constantinidis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Neuroscience Program, Vanderbilt University, Nashville, TN, USA
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
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14
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Lindsay GW. Grounding neuroscience in behavioral changes using artificial neural networks. Curr Opin Neurobiol 2024; 84:102816. [PMID: 38052111 DOI: 10.1016/j.conb.2023.102816] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 09/15/2023] [Accepted: 11/05/2023] [Indexed: 12/07/2023]
Abstract
Connecting neural activity to function is a common aim in neuroscience. How to define and conceptualize function, however, can vary. Here I focus on grounding this goal in the specific question of how a given change in behavior is produced by a change in neural circuits or activity. Artificial neural network models offer a particularly fruitful format for tackling such questions because they use neural mechanisms to perform complex transformations and produce appropriate behavior. Therefore, they can be a means of causally testing the extent to which a neural change can be responsible for an experimentally observed behavioral change. Furthermore, because the field of interpretability in artificial intelligence has similar aims, neuroscientists can look to interpretability methods for new ways of identifying neural features that drive performance and behaviors.
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Affiliation(s)
- Grace W Lindsay
- Department of Psychology and Center for Data Science, New York University, USA.
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15
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Mahrach A, Bestue D, Qi XL, Constantinidis C, Compte A. Cholinergic neuromodulation of prefrontal attractor dynamics controls performance in spatial working memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.17.576071. [PMID: 38293215 PMCID: PMC10827212 DOI: 10.1101/2024.01.17.576071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
The behavioral and neural effects of the endogenous release of acetylcholine following stimulation of the Nucleus Basalis of Meynert (NB) have been recently examined (Qi et al. 2021). Counterintuitively, NB stimulation enhanced behavioral performance while broadening neural tuning in the prefrontal cortex (PFC). The mechanism by which a weaker mnemonic neural code could lead to better performance remains unclear. Here, we show that increased neural excitability in a simple continuous bump attractor model can induce broader neural tuning and decrease bump diffusion, provided neural rates are saturated. Increased memory precision in the model overrides memory accuracy, improving overall task performance. Moreover, we show that bump attractor dynamics can account for the nonuniform impact of neuromodulation on distractibility, depending on distractor distance from the target. Finally, we delve into the conditions under which bump attractor tuning and diffusion balance in biologically plausible heterogeneous network models. In these discrete bump attractor networks, we show that reducing spatial correlations or enhancing excitatory transmission can improve memory precision. Altogether, we provide a mechanistic understanding of how cholinergic neuromodulation controls spatial working memory through perturbed attractor dynamics in PFC.
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Affiliation(s)
- Alexandre Mahrach
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain
| | - David Bestue
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain
| | - Xue-Lian Qi
- Wake Forest School of Medicine, Winston Salem, NC 27157, USA
| | | | - Albert Compte
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain
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16
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Gort J. Emergence of Universal Computations Through Neural Manifold Dynamics. Neural Comput 2024; 36:227-270. [PMID: 38101328 DOI: 10.1162/neco_a_01631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 09/05/2023] [Indexed: 12/17/2023]
Abstract
There is growing evidence that many forms of neural computation may be implemented by low-dimensional dynamics unfolding at the population scale. However, neither the connectivity structure nor the general capabilities of these embedded dynamical processes are currently understood. In this work, the two most common formalisms of firing-rate models are evaluated using tools from analysis, topology, and nonlinear dynamics in order to provide plausible explanations for these problems. It is shown that low-rank structured connectivities predict the formation of invariant and globally attracting manifolds in all these models. Regarding the dynamics arising in these manifolds, it is proved they are topologically equivalent across the considered formalisms. This letter also shows that under the low-rank hypothesis, the flows emerging in neural manifolds, including input-driven systems, are universal, which broadens previous findings. It explores how low-dimensional orbits can bear the production of continuous sets of muscular trajectories, the implementation of central pattern generators, and the storage of memory states. These dynamics can robustly simulate any Turing machine over arbitrary bounded memory strings, virtually endowing rate models with the power of universal computation. In addition, the letter shows how the low-rank hypothesis predicts the parsimonious correlation structure observed in cortical activity. Finally, it discusses how this theory could provide a useful tool from which to study neuropsychological phenomena using mathematical methods.
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Affiliation(s)
- Joan Gort
- Facultat de Psicologia, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona, Spain
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17
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Oby ER, Degenhart AD, Grigsby EM, Motiwala A, McClain NT, Marino PJ, Yu BM, Batista AP. Dynamical constraints on neural population activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.03.573543. [PMID: 38260549 PMCID: PMC10802336 DOI: 10.1101/2024.01.03.573543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The manner in which neural activity unfolds over time is thought to be central to sensory, motor, and cognitive functions in the brain. Network models have long posited that the brain's computations involve time courses of activity that are shaped by the underlying network. A prediction from this view is that the activity time courses should be difficult to violate. We leveraged a brain-computer interface (BCI) to challenge monkeys to violate the naturally-occurring time courses of neural population activity that we observed in motor cortex. This included challenging animals to traverse the natural time course of neural activity in a time-reversed manner. Animals were unable to violate the natural time courses of neural activity when directly challenged to do so. These results provide empirical support for the view that activity time courses observed in the brain indeed reflect the underlying network-level computational mechanisms that they are believed to implement.
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18
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Marshall TR, Ruesseler M, Hunt LT, O’Reilly JX. The representation of priors and decisions in the human parietal cortex. PLoS Biol 2024; 22:e3002383. [PMID: 38285671 PMCID: PMC10824454 DOI: 10.1371/journal.pbio.3002383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 12/11/2023] [Indexed: 01/31/2024] Open
Abstract
Animals actively sample their environment through orienting actions such as saccadic eye movements. Saccadic targets are selected based both on sensory evidence immediately preceding the saccade, and a "salience map" or prior built-up over multiple saccades. In the primate cortex, the selection of each individual saccade depends on competition between target-selective cells that ramp up their firing rate to saccade release. However, it is less clear how a cross-saccade prior might be implemented, either in neural firing or through an activity-silent mechanism such as modification of synaptic weights on sensory inputs. Here, we present evidence from magnetoencephalography for 2 distinct processes underlying the selection of the current saccade, and the representation of the prior, in human parietal cortex. While the classic ramping decision process for each saccade was reflected in neural firing rates (measured in the event-related field), a prior built-up over multiple saccades was implemented via modulation of the gain on sensory inputs from the preferred target, as evidenced by rapid frequency tagging. A cascade of computations over time (initial representation of the prior, followed by evidence accumulation and then an integration of prior and evidence) provides a mechanism by which a salience map may be built up across saccades in parietal cortex. It also provides insight into the apparent contradiction that inactivation of parietal cortex has been shown not to affect performance on single-trials, despite the presence of clear evidence accumulation signals in this region.
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Affiliation(s)
- Tom R. Marshall
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, Oxford University, Oxford, United Kingdom
| | - Maria Ruesseler
- Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, Oxford University, Oxford, United Kingdom
| | - Laurence T. Hunt
- Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, Oxford University, Oxford, United Kingdom
| | - Jill X. O’Reilly
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, Oxford University, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department for Clinical Neurosciences, Oxford University, Oxford, United Kingdom
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19
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Thrower L, Dang W, Jaffe RG, Sun JD, Constantinidis C. Decoding working memory information from neurons with and without persistent activity in the primate prefrontal cortex. J Neurophysiol 2023; 130:1392-1402. [PMID: 37910532 PMCID: PMC11068397 DOI: 10.1152/jn.00290.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/24/2023] [Accepted: 10/24/2023] [Indexed: 11/03/2023] Open
Abstract
Persistent activity of neurons in the prefrontal cortex has been thought to represent the information maintained in working memory, though alternative models have challenged this idea. Theories that depend on the dynamic representation of information posit that stimulus information may be maintained by the activity pattern of neurons whose firing rate is not significantly elevated above their baseline during the delay period of working memory tasks. We thus tested the ability of neurons that do and do not generate persistent activity in the prefrontal cortex of monkeys to represent spatial and object information in working memory. Neurons that generated persistent activity represented more information about the stimuli in both spatial and object working memory tasks. The amount of information that could be decoded from neural activity depended on the choice of decoder and parameters used but neurons with persistent activity outperformed non-persistent neurons consistently. Averaged across all neurons and stimuli, the firing rate did not appear clearly elevated above baseline during the maintenance of neural activity particularly for object working memory; however, this grand average masked neurons that generated persistent activity selective for their preferred stimuli, which carried the majority of stimulus information. These results reveal that prefrontal neurons that generate persistent activity maintain information more reliably during working memory.NEW & NOTEWORTHY Competing theories suggest that neurons that generate persistent activity or do not are primarily responsible for the maintenance of information, particularly regarding object working memory. Although the two models have been debated on theoretical terms, direct comparison of empirical results has been lacking. Analysis of neural activity in a large database of prefrontal recordings revealed that neurons that generate persistent activity were primarily responsible for the maintenance of both spatial and object working memory.
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Affiliation(s)
- Lilianna Thrower
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States
| | - Wenhao Dang
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States
| | - Rye G Jaffe
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States
| | - Jasmine D Sun
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States
| | - Christos Constantinidis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States
- Neuroscience Program, Vanderbilt University, Nashville, Tennessee, United States
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States
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20
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Kühn T, Monasson R. Information content in continuous attractor neural networks is preserved in the presence of moderate disordered background connectivity. Phys Rev E 2023; 108:064301. [PMID: 38243526 DOI: 10.1103/physreve.108.064301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 10/04/2023] [Indexed: 01/21/2024]
Abstract
Continuous attractor neural networks (CANN) form an appealing conceptual model for the storage of information in the brain. However a drawback of CANN is that they require finely tuned interactions. We here study the effect of quenched noise in the interactions on the coding of positional information within CANN. Using the replica method we compute the Fisher information for a network with position-dependent input and recurrent connections composed of a short-range (in space) and a disordered component. We find that the loss in positional information is small for not too large disorder strength, indicating that CANN have a regime in which the advantageous effects of local connectivity on information storage outweigh the detrimental ones. Furthermore, a substantial part of this information can be extracted with a simple linear readout.
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Affiliation(s)
- Tobias Kühn
- Laboratoire de Physique de l'Ecole Normale Supérieure, CNRS UMR8023 and PSL Research, Sorbonne Université, Université Paris Cité, F-75005 Paris, France
- Institut de la Vision, Sorbonne Université, CNRS, INSERM, F-75012 Paris, France
| | - Rémi Monasson
- Laboratoire de Physique de l'Ecole Normale Supérieure, CNRS UMR8023 and PSL Research, Sorbonne Université, Université Paris Cité, F-75005 Paris, France
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21
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Stroud JP, Watanabe K, Suzuki T, Stokes MG, Lengyel M. Optimal information loading into working memory explains dynamic coding in the prefrontal cortex. Proc Natl Acad Sci U S A 2023; 120:e2307991120. [PMID: 37983510 PMCID: PMC10691340 DOI: 10.1073/pnas.2307991120] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/29/2023] [Indexed: 11/22/2023] Open
Abstract
Working memory involves the short-term maintenance of information and is critical in many tasks. The neural circuit dynamics underlying working memory remain poorly understood, with different aspects of prefrontal cortical (PFC) responses explained by different putative mechanisms. By mathematical analysis, numerical simulations, and using recordings from monkey PFC, we investigate a critical but hitherto ignored aspect of working memory dynamics: information loading. We find that, contrary to common assumptions, optimal loading of information into working memory involves inputs that are largely orthogonal, rather than similar, to the late delay activities observed during memory maintenance, naturally leading to the widely observed phenomenon of dynamic coding in PFC. Using a theoretically principled metric, we show that PFC exhibits the hallmarks of optimal information loading. We also find that optimal information loading emerges as a general dynamical strategy in task-optimized recurrent neural networks. Our theory unifies previous, seemingly conflicting theories of memory maintenance based on attractor or purely sequential dynamics and reveals a normative principle underlying dynamic coding.
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Affiliation(s)
- Jake P. Stroud
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
| | - Kei Watanabe
- Graduate School of Frontier Biosciences, Osaka University, Osaka565-0871, Japan
| | - Takafumi Suzuki
- Center for Information and Neural Networks, National Institute of Communication and Information Technology, Osaka565-0871, Japan
| | - Mark G. Stokes
- Department of Experimental Psychology, University of Oxford, OxfordOX2 6GG, United Kingdom
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, OxfordOX3 9DU, United Kingdom
| | - Máté Lengyel
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
- Center for Cognitive Computation, Department of Cognitive Science, Central European University, BudapestH-1051, Hungary
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22
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Luo TZ, Kim TD, Gupta D, Bondy AG, Kopec CD, Elliot VA, DePasquale B, Brody CD. Transitions in dynamical regime and neural mode underlie perceptual decision-making. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.15.562427. [PMID: 37904994 PMCID: PMC10614809 DOI: 10.1101/2023.10.15.562427] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Perceptual decision-making is the process by which an animal uses sensory stimuli to choose an action or mental proposition. This process is thought to be mediated by neurons organized as attractor networks 1,2 . However, whether attractor dynamics underlie decision behavior and the complex neuronal responses remains unclear. Here we use an unsupervised, deep learning-based method to discover decision-related dynamics from the simultaneous activity of neurons in frontal cortex and striatum of rats while they accumulate pulsatile auditory evidence. We show that contrary to prevailing hypotheses, attractors play a role only after a transition from a regime in the dynamics that is strongly driven by inputs to one dominated by the intrinsic dynamics. The initial regime mediates evidence accumulation, and the subsequent intrinsic-dominant regime subserves decision commitment. This regime transition is coupled to a rapid reorganization in the representation of the decision process in the neural population (a change in the "neural mode" along which the process develops). A simplified model approximating the coupled transition in the dynamics and neural mode allows inferring, from each trial's neural activity, the internal decision commitment time in that trial, and captures diverse and complex single-neuron temporal profiles, such as ramping and stepping 3-5 . It also captures trial-averaged curved trajectories 6-8 , and reveals distinctions between brain regions. Our results show that the formation of a perceptual choice involves a rapid, coordinated transition in both the dynamical regime and the neural mode of the decision process, and suggest pairing deep learning and parsimonious models as a promising approach for understanding complex data.
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23
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Chong HR, Ranjbar-Slamloo Y, Ho MZH, Ouyang X, Kamigaki T. Functional alterations of the prefrontal circuit underlying cognitive aging in mice. Nat Commun 2023; 14:7254. [PMID: 37945561 PMCID: PMC10636129 DOI: 10.1038/s41467-023-43142-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 11/01/2023] [Indexed: 11/12/2023] Open
Abstract
Executive function is susceptible to aging. How aging impacts the circuit-level computations underlying executive function remains unclear. Using calcium imaging and optogenetic manipulation during memory-guided behavior, we show that working-memory coding and the relevant recurrent connectivity in the mouse medial prefrontal cortex (mPFC) are altered as early as middle age. Population activity in the young adult mPFC exhibits dissociable yet overlapping patterns between tactile and auditory modalities, enabling crossmodal memory coding concurrent with modality-dependent coding. In middle age, however, crossmodal coding remarkably diminishes while modality-dependent coding persists, and both types of coding decay in advanced age. Resting-state functional connectivity, especially among memory-coding neurons, decreases already in middle age, suggesting deteriorated recurrent circuits for memory maintenance. Optogenetic inactivation reveals that the middle-aged mPFC exhibits heightened vulnerability to perturbations. These findings elucidate functional alterations of the prefrontal circuit that unfold in middle age and deteriorate further as a hallmark of cognitive aging.
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Affiliation(s)
- Huee Ru Chong
- Neuroscience & Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Yadollah Ranjbar-Slamloo
- Neuroscience & Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Malcolm Zheng Hao Ho
- Neuroscience & Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
- IGP-Neuroscience, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore, 308232, Singapore
| | - Xuan Ouyang
- Neuroscience & Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Tsukasa Kamigaki
- Neuroscience & Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore.
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24
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Constantinidis C, Ahmed AA, Wallis JD, Batista AP. Common Mechanisms of Learning in Motor and Cognitive Systems. J Neurosci 2023; 43:7523-7529. [PMID: 37940591 PMCID: PMC10634576 DOI: 10.1523/jneurosci.1505-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 08/29/2023] [Accepted: 08/31/2023] [Indexed: 11/10/2023] Open
Abstract
Rapid progress in our understanding of the brain's learning mechanisms has been accomplished over the past decade, particularly with conceptual advances, including representing behavior as a dynamical system, large-scale neural population recordings, and new methods of analysis of neuronal populations. However, motor and cognitive systems have been traditionally studied with different methods and paradigms. Recently, some common principles, evident in both behavior and neural activity, that underlie these different types of learning have become to emerge. Here we review results from motor and cognitive learning, relying on different techniques and studying different systems to understand the mechanisms of learning. Movement is intertwined with cognitive operations, and its dynamics reflect cognitive variables. Training, in either motor or cognitive tasks, involves recruitment of previously unresponsive neurons and reorganization of neural activity in a low dimensional manifold. Mapping of new variables in neural activity can be very rapid, instantiating flexible learning of new tasks. Communication between areas is just as critical a part of learning as are patterns of activity within an area emerging with learning. Common principles across systems provide a map for future research.
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Affiliation(s)
| | - Alaa A Ahmed
- Department of Mechanical Engineering, University of Colorado Boulder, Boulder Colorado 80309
| | - Joni D Wallis
- Department of Psychology, University of California Berkeley, Berkeley, California 94720
| | - Aaron P Batista
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
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25
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Zhang WH, Wu S, Josić K, Doiron B. Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons. Nat Commun 2023; 14:7074. [PMID: 37925497 PMCID: PMC10625605 DOI: 10.1038/s41467-023-41743-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 09/15/2023] [Indexed: 11/06/2023] Open
Abstract
Two facts about cortex are widely accepted: neuronal responses show large spiking variability with near Poisson statistics and cortical circuits feature abundant recurrent connections between neurons. How these spiking and circuit properties combine to support sensory representation and information processing is not well understood. We build a theoretical framework showing that these two ubiquitous features of cortex combine to produce optimal sampling-based Bayesian inference. Recurrent connections store an internal model of the external world, and Poissonian variability of spike responses drives flexible sampling from the posterior stimulus distributions obtained by combining feedforward and recurrent neuronal inputs. We illustrate how this framework for sampling-based inference can be used by cortex to represent latent multivariate stimuli organized either hierarchically or in parallel. A neural signature of such network sampling are internally generated differential correlations whose amplitude is determined by the prior stored in the circuit, which provides an experimentally testable prediction for our framework.
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Affiliation(s)
- Wen-Hao Zhang
- Department of Neurobiology and Statistics, University of Chicago, Chicago, IL, USA
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Si Wu
- School of Psychological and Cognitive Sciences, Peking University, Beijing, 100871, China
- IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China
- Center of Quantitative Biology, Peking University, Beijing, 100871, China
| | - Krešimir Josić
- Department of Mathematics, University of Houston, Houston, TX, USA.
- Department of Biology and Biochemistry, University of Houston, Houston, TX, USA.
| | - Brent Doiron
- Department of Neurobiology and Statistics, University of Chicago, Chicago, IL, USA.
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA.
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA.
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA.
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26
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Eissa TL, Kilpatrick ZP. Learning efficient representations of environmental priors in working memory. PLoS Comput Biol 2023; 19:e1011622. [PMID: 37943956 PMCID: PMC10662764 DOI: 10.1371/journal.pcbi.1011622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/21/2023] [Accepted: 10/20/2023] [Indexed: 11/12/2023] Open
Abstract
Experience shapes our expectations and helps us learn the structure of the environment. Inference models render such learning as a gradual refinement of the observer's estimate of the environmental prior. For instance, when retaining an estimate of an object's features in working memory, learned priors may bias the estimate in the direction of common feature values. Humans display such biases when retaining color estimates on short time intervals. We propose that these systematic biases emerge from modulation of synaptic connectivity in a neural circuit based on the experienced stimulus history, shaping the persistent and collective neural activity that encodes the stimulus estimate. Resulting neural activity attractors are aligned to common stimulus values. Using recently published human response data from a delayed-estimation task in which stimuli (colors) were drawn from a heterogeneous distribution that did not necessarily correspond with reported population biases, we confirm that most subjects' response distributions are better described by experience-dependent learning models than by models with fixed biases. This work suggests systematic limitations in working memory reflect efficient representations of inferred environmental structure, providing new insights into how humans integrate environmental knowledge into their cognitive strategies.
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Affiliation(s)
- Tahra L. Eissa
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - Zachary P. Kilpatrick
- Department of Applied Mathematics, University of Colorado Boulder, Boulder, Colorado, United States of America
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, Colorado, United States of America
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27
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Brennan C, Proekt A. Attractor dynamics with activity-dependent plasticity capture human working memory across time scales. COMMUNICATIONS PSYCHOLOGY 2023; 1:28. [PMID: 38764555 PMCID: PMC11101211 DOI: 10.1038/s44271-023-00027-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 09/15/2023] [Indexed: 05/21/2024]
Abstract
Most cognitive functions require the brain to maintain immediately preceding stimuli in working memory. Here, using a human working memory task with multiple delays, we test the hypothesis that working memories are stored in a discrete set of stable neuronal activity configurations called attractors. We show that while discrete attractor dynamics can approximate working memory on a single time scale, they fail to generalize across multiple timescales. This failure occurs because at longer delay intervals the responses contain more information about the stimuli than can be stored in a discrete attractor model. We present a modeling approach that combines discrete attractor dynamics with activity-dependent plasticity. This model successfully generalizes across all timescales and correctly predicts intertrial interactions. Thus, our findings suggest that discrete attractor dynamics are insufficient to model working memory and that activity-dependent plasticity improves durability of information storage in attractor systems.
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Affiliation(s)
- Connor Brennan
- University of Pennsylvania, 3160 Chestnut St., Philadelphia, PA, USA
| | - Alex Proekt
- University of Pennsylvania, 3160 Chestnut St., Philadelphia, PA, USA
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28
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Alleman M, Panichello M, Buschman TJ, Johnston WJ. The neural basis of swap errors in working memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.09.561584. [PMID: 37873433 PMCID: PMC10592761 DOI: 10.1101/2023.10.09.561584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
When making decisions in a cluttered world, humans and other animals often have to hold multiple items in memory at once - such as the different items on a shopping list. Psychophysical experiments in humans and other animals have shown remembered stimuli can sometimes become confused, with participants reporting chimeric stimuli composed of features from different stimuli. In particular, subjects will often make "swap errors" where they misattribute a feature from one object as belonging to another object. While swap errors have been described behaviorally, their neural mechanisms are unknown. Here, we elucidate these neural mechanisms through trial-by-trial analysis of neural population recordings from posterior and frontal brain regions while monkeys perform two multi-stimulus working memory tasks. In these tasks, monkeys were cued to report the color of an item that either was previously shown at a corresponding location (requiring selection from working memory) or will be shown at the corresponding location (requiring attention to a position). Animals made swap errors in both tasks. In the neural data, we find evidence that the neural correlates of swap errors emerged when correctly remembered information is selected incorrectly from working memory. This led to a representation of the distractor color as if it were the target color, underlying the eventual swap error. We did not find consistent evidence that swap errors arose from misinterpretation of the cue or errors during encoding or storage in working memory. These results suggest an alternative to established views on the neural origins of swap errors, and highlight selection from and manipulation in working memory as crucial - yet surprisingly brittle - neural processes.
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Affiliation(s)
- Matteo Alleman
- Center for Theoretical Neuroscience
- Mortimer B. Zuckerman Mind, Brain, and Behavior Institute Columbia University, New York, NY, USA
| | - Matthew Panichello
- Princeton Neuroscience Institute and Department of Psychology Princeton University, Princeton, NJ, USA
- Department of Neurobiology Stanford University, Stanford, CA, USA
| | - Timothy J. Buschman
- Princeton Neuroscience Institute and Department of Psychology Princeton University, Princeton, NJ, USA
| | - W. Jeffrey Johnston
- Center for Theoretical Neuroscience
- Mortimer B. Zuckerman Mind, Brain, and Behavior Institute Columbia University, New York, NY, USA
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29
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Li S, Rosen MC, Chang S, David S, Freedman DJ. Alterations of neural activity in the prefrontal cortex associated with deficits in working memory performance. Front Behav Neurosci 2023; 17:1213435. [PMID: 37915531 PMCID: PMC10616307 DOI: 10.3389/fnbeh.2023.1213435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/31/2023] [Indexed: 11/03/2023] Open
Abstract
Working memory (WM), a core cognitive function, enables the temporary holding and manipulation of information in mind to support ongoing behavior. Neurophysiological recordings conducted in nonhuman primates have revealed neural correlates of this process in a network of higher-order cortical regions, particularly the prefrontal cortex (PFC). Here, we review the circuit mechanisms and functional importance of WM-related activity in these areas. Recent neurophysiological data indicates that the absence of these neural correlates at different stages of WM is accompanied by distinct behavioral deficits, which are characteristic of various disease states/normal aging and which we review here. Finally, we discuss emerging evidence of electrical stimulation ameliorating these WM deficits in both humans and non-human primates. These results are important for a basic understanding of the neural mechanisms supporting WM, as well as for translational efforts to developing therapies capable of enhancing healthy WM ability or restoring WM from dysfunction.
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Affiliation(s)
- Sihai Li
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - Matthew C. Rosen
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - Suha Chang
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - Samuel David
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - David J. Freedman
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
- Neuroscience Institute, The University of Chicago, Chicago, IL, United States
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30
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Ma H, Qi Y, Gong P, Zhang J, Lu WL, Feng J. Self-Organization of Nonlinearly Coupled Neural Fluctuations Into Synergistic Population Codes. Neural Comput 2023; 35:1820-1849. [PMID: 37725705 DOI: 10.1162/neco_a_01612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/26/2023] [Indexed: 09/21/2023]
Abstract
Neural activity in the brain exhibits correlated fluctuations that may strongly influence the properties of neural population coding. However, how such correlated neural fluctuations may arise from the intrinsic neural circuit dynamics and subsequently affect the computational properties of neural population activity remains poorly understood. The main difficulty lies in resolving the nonlinear coupling between correlated fluctuations with the overall dynamics of the system. In this study, we investigate the emergence of synergistic neural population codes from the intrinsic dynamics of correlated neural fluctuations in a neural circuit model capturing realistic nonlinear noise coupling of spiking neurons. We show that a rich repertoire of spatial correlation patterns naturally emerges in a bump attractor network and further reveals the dynamical regime under which the interplay between differential and noise correlations leads to synergistic codes. Moreover, we find that negative correlations may induce stable bound states between two bumps, a phenomenon previously unobserved in firing rate models. These noise-induced effects of bump attractors lead to a number of computational advantages including enhanced working memory capacity and efficient spatiotemporal multiplexing and can account for a range of cognitive and behavioral phenomena related to working memory. This study offers a dynamical approach to investigating realistic correlated neural fluctuations and insights to their roles in cortical computations.
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Affiliation(s)
- Hengyuan Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Yang Qi
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, NSW 2006, Australia
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
| | - Wen-Lian Lu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, U.K.
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31
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Laing CR, Omel’chenko OE. Periodic solutions in next generation neural field models. BIOLOGICAL CYBERNETICS 2023; 117:259-274. [PMID: 37535104 PMCID: PMC10600056 DOI: 10.1007/s00422-023-00969-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 07/12/2023] [Indexed: 08/04/2023]
Abstract
We consider a next generation neural field model which describes the dynamics of a network of theta neurons on a ring. For some parameters the network supports stable time-periodic solutions. Using the fact that the dynamics at each spatial location are described by a complex-valued Riccati equation we derive a self-consistency equation that such periodic solutions must satisfy. We determine the stability of these solutions, and present numerical results to illustrate the usefulness of this technique. The generality of this approach is demonstrated through its application to several other systems involving delays, two-population architecture and networks of Winfree oscillators.
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Affiliation(s)
- Carlo R. Laing
- School of Mathematical and Computational Sciences, Massey University, Private Bag 102-904 NSMC, Auckland, New Zealand
| | - Oleh E. Omel’chenko
- Institute of Physics and Astronomy, University of Potsdam, Karl-Liebknecht-Str. 24/25, 14476 Potsdam, Germany
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32
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Zhu J, Hammond BM, Zhou XM, Constantinidis C. Laminar pattern of adolescent development changes in working memory neuronal activity. J Neurophysiol 2023; 130:980-989. [PMID: 37703490 PMCID: PMC10649837 DOI: 10.1152/jn.00294.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 09/15/2023] Open
Abstract
Adolescent development is characterized by an improvement in cognitive abilities, such as working memory. Neurophysiological recordings in a nonhuman primate model of adolescence have revealed changes in neural activity that mirror improvement in behavior, including higher firing rate during the delay intervals of working memory tasks. The laminar distribution of these changes is unknown. By some accounts, persistent activity is more pronounced in superficial layers, so we sought to determine whether changes are most pronounced there. We therefore analyzed neurophysiological recordings from the young and adult stage of male monkeys, at different cortical depths. Superficial layers exhibited an increased baseline firing rate in the adult stage. Unexpectedly, we also detected substantial increases in delay period activity in the middle layers after adolescence, which was confirmed even after excluding penetrations near sulci. Finally, improved discriminability around the saccade period was most evident in the deeper layers. These results reveal the laminar pattern of neural activity maturation that is associated with cognitive improvement.NEW & NOTEWORTHY Structural brain changes are evident during adolescent development particularly in the cortical thickness of the prefrontal cortex, at a time when working memory ability increases markedly. The depth distribution of neurophysiological changes during adolescence is not known. Here, we show that neurophysiological changes are not confined to superficial layers, which have most often been implicated in the maintenance of working memory. Contrary to expectations, substantial changes were evident in intermediate layers of the prefrontal cortex.
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Affiliation(s)
- Junda Zhu
- Program in Neuroscience, Vanderbilt University, Nashville, Tennessee, United States
| | - Benjamin M Hammond
- Program in Neuroscience, Vanderbilt University, Nashville, Tennessee, United States
| | - Xin Maizie Zhou
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States
| | - Christos Constantinidis
- Program in Neuroscience, Vanderbilt University, Nashville, Tennessee, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States
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33
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Nolan SO, Melugin PR, Erickson KR, Adams WR, Farahbakhsh ZZ, Mcgonigle CE, Kwon MH, Costa VD, Lapish CC, Hackett TA, Cuzon Carlson VC, Constantinidis C, Grant KA, Siciliano CA. Recurrent activity within microcircuits of macaque dorsolateral prefrontal cortex tracks cognitive flexibility. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.23.559125. [PMID: 38529503 PMCID: PMC10962741 DOI: 10.1101/2023.09.23.559125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Human and non-human primate data clearly implicate the dorsolateral prefrontal cortex (dlPFC) as critical for advanced cognitive functions 1,2 . It is thought that intracortical synaptic architectures within dlPFC are the integral neurobiological substrate that gives rise to these processes, including working memory, inferential reasoning, and decision-making 3-7 . In the prevailing model, each cortical column makes up one fundamental processing unit composed of dense intrinsic connectivity, conceptualized as the 'canonical' cortical microcircuit 3,8 . Each cortical microcircuit receives sensory and cognitive information from a variety of sources which are represented by sustained activity within the microcircuit, referred to as persistent or recurrent activity 4,9 . Via recurrent connections within the microcircuit, activity can propagate for a variable length of time, thereby allowing temporary storage and computations to occur locally before ultimately passing a transformed representation to a downstream output 4,5,10 . Competing theories regarding how microcircuit activity is coordinated have proven difficult to reconcile in vivo where intercortical and intracortical computations cannot be fully dissociated 5,9,11,12 . Here, we interrogated the intrinsic features of isolated microcircuit networks using high-density calcium imaging of macaque dlPFC ex vivo . We found that spontaneous activity is intrinsically maintained by microcircuit architecture, persisting at a high rate in the absence of extrinsic connections. Further, using perisulcal stimulation to evoke persistent activity in deep layers, we found that activity propagates through stochastically assembled intracortical networks, creating predictable population-level events from largely non-overlapping ensembles. Microcircuit excitability covaried with individual cognitive performance, thus anchoring heuristic models of abstract cortical functions within quantifiable constraints imposed by the underlying synaptic architecture.
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Tan PK, Tang C, Herikstad R, Pillay A, Libedinsky C. Distinct Lateral Prefrontal Regions Are Organized in an Anterior-Posterior Functional Gradient. J Neurosci 2023; 43:6564-6572. [PMID: 37607819 PMCID: PMC10513068 DOI: 10.1523/jneurosci.0007-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 08/13/2023] [Accepted: 08/15/2023] [Indexed: 08/24/2023] Open
Abstract
The dorsolateral prefrontal cortex (dlPFC) is composed of multiple anatomically defined regions involved in higher-order cognitive processes, including working memory and selective attention. It is organized in an anterior-posterior global gradient where posterior regions track changes in the environment, whereas anterior regions support abstract neural representations. However, it remains unknown if such a global gradient results from a smooth gradient that spans regions or an emergent property arising from functionally distinct regions, that is, an areal gradient. Here, we recorded single neurons in the dlPFC of nonhuman primates trained to perform a memory-guided saccade task with an interfering distractor and analyzed their physiological properties along the anterior-posterior axis. We found that these physiological properties were best described by an areal gradient. Further, population analyses revealed that there is a distributed representation of spatial information across the dlPFC. Our results validate the functional boundaries between anatomically defined dlPFC regions and highlight the distributed nature of computations underlying working memory across the dlPFC.SIGNIFICANCE STATEMENT Activity of frontal lobe regions is known to possess an anterior-posterior functional gradient. However, it is not known whether this gradient is the result of individual brain regions organized in a gradient (like a staircase), or a smooth gradient that spans regions (like a slide). Analysis of physiological properties of individual neurons in the primate frontal regions suggest that individual regions are organized as a gradient, rather than a smooth gradient. At the population level, working memory was more prominent in posterior regions, although it was also present in anterior regions. This is consistent with the functional segregation of brain regions that is also observed in other systems (i.e., the visual system).
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Affiliation(s)
- Pin Kwang Tan
- Institute of Neuroscience, University of Texas at Austin, Austin, Texas 78712
| | - Cheng Tang
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Roger Herikstad
- The N1 Institute for Health, National University of Singapore, Singapore 117456
| | - Arunika Pillay
- Department of Psychology, Royal Holloway University of London, Egham TW20 OEX, United Kingdom
| | - Camilo Libedinsky
- The N1 Institute for Health, National University of Singapore, Singapore 117456
- Department of Psychology, National University of Singapore, Singapore 117570
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, National University of Singapore, Singapore 138632
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35
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Li HH, Curtis CE. Neural population dynamics of human working memory. Curr Biol 2023; 33:3775-3784.e4. [PMID: 37595590 PMCID: PMC10528783 DOI: 10.1016/j.cub.2023.07.067] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/20/2023] [Accepted: 07/31/2023] [Indexed: 08/20/2023]
Abstract
The activity of neurons in macaque prefrontal cortex (PFC) persists during working memory (WM) delays, providing a mechanism for memory.1,2,3,4,5,6,7,8,9,10,11 Although theory,11,12 including formal network models,13,14 assumes that WM codes are stable over time, PFC neurons exhibit dynamics inconsistent with these assumptions.15,16,17,18,19 Recently, multivariate reanalyses revealed the coexistence of both stable and dynamic WM codes in macaque PFC.20,21,22,23 Human EEG studies also suggest that WM might contain dynamics.24,25 Nonetheless, how WM dynamics vary across the cortical hierarchy and which factors drive dynamics remain unknown. To elucidate WM dynamics in humans, we decoded WM content from fMRI responses across multiple cortical visual field maps.26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48 We found coexisting stable and dynamic neural representations of WM during a memory-guided saccade task. Geometric analyses of neural subspaces revealed that early visual cortex exhibited stronger dynamics than high-level visual and frontoparietal cortex. Leveraging models of population receptive fields, we visualized and made the neural dynamics interpretable. We found that during WM delays, V1 population initially encoded a narrowly tuned bump of activation centered on the peripheral memory target. Remarkably, this bump then spread inward toward foveal locations, forming a vector along the trajectory of the forthcoming memory-guided saccade. In other words, the neural code transformed into an abstraction of the stimulus more proximal to memory-guided behavior. Therefore, theories of WM must consider both sensory features and their task-relevant abstractions because changes in the format of memoranda naturally drive neural dynamics.
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Affiliation(s)
- Hsin-Hung Li
- Department of Psychology, New York University, New York, NY 10003, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Clayton E Curtis
- Department of Psychology, New York University, New York, NY 10003, USA; Center for Neural Science, New York University, New York, NY 10003, USA.
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36
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Singh B, Wang Z, Madiah LM, Gatti SE, Fulton JN, Johnson GW, Li R, Dawant BM, Englot DJ, Bick SK, Roberson SW, Constantinidis C. Brain-wide human oscillatory LFP activity during visual working memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.06.556554. [PMID: 37732263 PMCID: PMC10508766 DOI: 10.1101/2023.09.06.556554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Oscillatory activity is thought to be a marker of cognitive processes, although its role and distribution across the brain during working memory has been a matter of debate. To understand how oscillatory activity differentiates tasks and brain areas in humans, we recorded local field potentials (LFPs) in 12 adults as they performed visual-spatial and shape-matching memory tasks. Tasks were designed to engage working memory processes at a range of delay intervals between stimulus delivery and response initiation. LFPs were recorded using intracranial depth electrodes implanted to localize seizures for management of intractable epilepsy. Task-related LFP power analyses revealed an extensive network of cortical regions that were activated during the presentation of visual stimuli and during their maintenance in working memory, including occipital, parietal, temporal, insular, and prefrontal cortical areas, and subcortical structures including the amygdala and hippocampus. Across most brain areas, the appearance of a stimulus produced broadband power increase, while gamma power was evident during the delay interval of the working memory task. Notable differences between areas included that occipital cortex was characterized by elevated power in the high gamma (100-150 Hz) range during the 500 ms of visual stimulus presentation, which was less pronounced or absent in other areas. A decrease in power centered in beta frequency (16-40 Hz) was also observed after the stimulus presentation, whose magnitude differed across areas. These results reveal the interplay of oscillatory activity across a broad network, and region-specific signatures of oscillatory processes associated with visual working memory.
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Affiliation(s)
- Balbir Singh
- Department of Biomedical Engineering, Vanderbilt University
| | | | - Leen M Madiah
- Department of Biomedical Engineering, Vanderbilt University
| | | | - Jenna N Fulton
- Department of Neurology, Vanderbilt University Medical Center
| | - Graham W Johnson
- Department of Neurological Surgery, Vanderbilt University Medical Center
| | - Rui Li
- Department of Electrical and Computer Engineering, Vanderbilt University
| | - Benoit M Dawant
- Department of Electrical and Computer Engineering, Vanderbilt University
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University
- Department of Neurological Surgery, Vanderbilt University Medical Center
| | - Sarah K Bick
- Department of Biomedical Engineering, Vanderbilt University
- Department of Neurological Surgery, Vanderbilt University Medical Center
| | - Shawniqua Williams Roberson
- Department of Biomedical Engineering, Vanderbilt University
- Department of Neurology, Vanderbilt University Medical Center
| | - Christos Constantinidis
- Department of Biomedical Engineering, Vanderbilt University
- Neuroscience Program, Vanderbilt University
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center
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37
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Ibañez S, Sengupta N, Luebke JI, Wimmer K, Weaver CM. Myelin dystrophy in the aging prefrontal cortex leads to impaired signal transmission and working memory decline: a multiscale computational study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.30.555476. [PMID: 37693412 PMCID: PMC10491254 DOI: 10.1101/2023.08.30.555476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Normal aging leads to myelin alternations in the rhesus monkey dorsolateral prefrontal cortex (dlPFC), which are often correlated with cognitive impairment. It is hypothesized that remyelination with shorter and thinner myelin sheaths partially compensates for myelin degradation, but computational modeling has not yet explored these two phenomena together systematically. Here, we used a two-pronged modeling approach to determine how age-related myelin changes affect a core cognitive function: spatial working memory. First we built a multicompartment pyramidal neuron model fit to monkey dlPFC data, with axon including myelinated segments having paranodes, juxtaparanodes, internodes, and tight junctions, to quantify conduction velocity (CV) changes and action potential (AP) failures after demyelination and subsequent remyelination in a population of neurons. Lasso regression identified distinctive parameter sets likely to modulate an axon's susceptibility to CV changes following demyelination versus remyelination. Next we incorporated the single neuron results into a spiking neural network model of working memory. While complete remyelination nearly recovered axonal transmission and network function to unperturbed levels, our models predict that biologically plausible levels of myelin dystrophy, if uncompensated by other factors, can account for substantial working memory impairment with aging. The present computational study unites empirical data from electron microscopy up to behavior on aging, and has broader implications for many demyelinating conditions, such as multiple sclerosis or schizophrenia.
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Affiliation(s)
- Sara Ibañez
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA 02118
- Centre de Recerca Matemàtica, Edifici C, Campus Bellaterra, 08193 Bellaterra, Spain
| | - Nilapratim Sengupta
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA 02118
- Department of Mathematics, Franklin and Marshall College, Lancaster, PA, USA 17604
| | - Jennifer I Luebke
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA USA 02118
| | - Klaus Wimmer
- Centre de Recerca Matemàtica, Edifici C, Campus Bellaterra, 08193 Bellaterra, Spain
| | - Christina M Weaver
- Department of Mathematics, Franklin and Marshall College, Lancaster, PA, USA 17604
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38
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Mozumder R, Constantinidis C. Single-neuron and population measures of neuronal activity in working memory tasks. J Neurophysiol 2023; 130:694-705. [PMID: 37609703 PMCID: PMC10649843 DOI: 10.1152/jn.00245.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/17/2023] [Accepted: 08/17/2023] [Indexed: 08/24/2023] Open
Abstract
Information represented in working memory is reflected in the firing rate of neurons in the prefrontal cortex and brain areas connected to it. In recent years, there has been an increased realization that population measures capture more accurately neural correlates of cognitive functions. We examined how single neuron firing in the prefrontal and posterior parietal cortex of two male monkeys compared with population measures in spatial working memory tasks. Persistent activity was observed in the dorsolateral prefrontal and posterior parietal cortex and firing rate predicted working memory behavior, particularly in the prefrontal cortex. These findings had equivalents in population measures, including trajectories in state space that became less separated in error trials. We additionally observed rotations of stimulus representations in the neuronal state space for different task conditions, which were not obvious in firing rate measures. These results suggest that population measures provide a richer view of how neuronal activity is associated with behavior, largely confirming that persistent activity is the core phenomenon that maintains visual-spatial information in working memory.NEW & NOTEWORTHY Recordings from large numbers of neurons led to a reevaluation of neural correlates of cognitive functions, which traditionally were defined based on responses of single neurons or averages of firing rates. Analysis of neuronal recordings from the dorsolateral prefrontal and posterior parietal cortex revealed that properties of neuronal firing captured in classical studies of persistent activity can account for population representations, though some population characteristics did not have clear correlates in single neuron activity.
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Affiliation(s)
- Rana Mozumder
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States
| | - Christos Constantinidis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States
- Program in Neuroscience, Vanderbilt University, Nashville, Tennessee, United States
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States
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39
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Ye L, Feng J, Li C. Controlling brain dynamics: Landscape and transition path for working memory. PLoS Comput Biol 2023; 19:e1011446. [PMID: 37669311 PMCID: PMC10503743 DOI: 10.1371/journal.pcbi.1011446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/15/2023] [Accepted: 08/21/2023] [Indexed: 09/07/2023] Open
Abstract
Understanding the underlying dynamical mechanisms of the brain and controlling it is a crucial issue in brain science. The energy landscape and transition path approach provides a possible route to address these challenges. Here, taking working memory as an example, we quantified its landscape based on a large-scale macaque model. The working memory function is governed by the change of landscape and brain-wide state switching in response to the task demands. The kinetic transition path reveals that information flow follows the direction of hierarchical structure. Importantly, we propose a landscape control approach to manipulate brain state transition by modulating external stimulation or inter-areal connectivity, demonstrating the crucial roles of associative areas, especially prefrontal and parietal cortical areas in working memory performance. Our findings provide new insights into the dynamical mechanism of cognitive function, and the landscape control approach helps to develop therapeutic strategies for brain disorders.
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Affiliation(s)
- Leijun Ye
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Chunhe Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
- School of Mathematical Sciences and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
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40
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Sun Y, Dang W, Jaffe RG, Constantinidis C. Local organization of spatial and shape information during working memory in the primate prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.26.554962. [PMID: 37693624 PMCID: PMC10491106 DOI: 10.1101/2023.08.26.554962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
While the current understanding of sensory and motor cortical areas has been defined topographical maps across the surface of these areas, higher cortical areas, such as the prefrontal cortex, seem to lack an equivalent organization, with only limited evidence of functional clustering of neurons with similar stimulus properties. We sought to examine whether neurons that represent similar spatial and object information are clustered in the monkey prefrontal cortex and whether such an organization only emerges as a result of training. We analyzed neurophysiological recordings from male macaque monkeys before and after they were trained to perform cognitive tasks. Neurons with similar spatial or shape selectivity were more likely than chance to be encountered at short distances from each other. This pattern of organization was present even in naïve animals, prior to any cognitive training. Our results reveal that prefrontal microstructure automatically supports orderly representations of spatial and object information.
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41
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Zhu J, Hammond BM, Zhou XM, Constantinidis C. Laminar pattern of adolescent development changes in working memory neuronal activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.28.550982. [PMID: 37546979 PMCID: PMC10402138 DOI: 10.1101/2023.07.28.550982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Adolescent development is characterized by an improvement in cognitive abilities, such as working memory. Neurophysiological recordings in a non-human primate model of adolescence have revealed changes in neural activity that mirror improvement in behavior, including higher firing rate during the delay intervals of working memory tasks. The laminar distribution of these changes is unknown. By some accounts, persistent activity is more pronounced in superficial layers, so we sought to determine whether changes are most pronounced there. We therefore analyzed neurophysiological recordings from neurons recorded in the young and adult stage, at different cortical depths. Superficial layers exhibited increased baseline firing rate in the adult stage. Unexpectedly, changes in persistent activity were most pronounced in the middle layers. Finally, improved discriminability of stimulus location was most evident in the deeper layers. These results reveal the laminar pattern of neural activity maturation that is associated with cognitive improvement. NEW AND NOTEWORTHY Structural brain changes are evident during adolescent development particularly in the cortical thickness of the prefrontal cortex, at a time when working memory ability increases markedly. The depth distribution of neurophysiological changes during adolescence is not known. Here we show that neurophysiological changes are not confined to superficial layers, which have most often been implicated in the maintenance of working memory. Contrary to expectations, greatest changes were evident in intermediate layers of the prefrontal cortex.
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Affiliation(s)
- Junda Zhu
- Program in Neuroscience, Vanderbilt University, Nashville, TN 37235
| | | | - Xin Maizie Zhou
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235
- Department of Computer Science, Vanderbilt University, Nashville, TN 37235
| | - Christos Constantinidis
- Program in Neuroscience, Vanderbilt University, Nashville, TN 37235
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN 37212
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Thrower L, Dang W, Jaffe RG, Sun JD, Constantinidis C. Decoding working memory information from persistent and activity-silent neurons in the primate prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.25.550371. [PMID: 37546782 PMCID: PMC10402050 DOI: 10.1101/2023.07.25.550371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Persistent activity of neurons in the prefrontal cortex has been thought to represent the information maintained in working memory, though alternative models have recently challenged this idea. Activity-silent theories posit that stimulus information may be maintained by the activity pattern of neurons that do not produce firing rate significantly elevated about their baseline during the delay period of working memory tasks. We thus tested the ability of neurons that do and do not generate persistent activity in the prefrontal cortex of monkeys to represent spatial and object information in working memory. Neurons that generated persistent activity represented more information about the stimuli in both spatial and object working memory tasks. The amount of information that could be decoded from neural activity depended on the choice of decoder and parameters used but neurons with persistent activity outperformed non-persistent neurons consistently. Although averaged across all neurons and stimuli, firing rate did not appear clearly elevated above baseline during the maintenance of neural activity particularly for object working memory, this grant average masked neurons that generated persistent activity selective for their preferred stimuli, which carried the majority of information about the stimulus identity. These results reveal that prefrontal neurons with generate persistent activity constitute the primary mechanism of working memory maintenance in the cortex. NEW AND NOTEWORTHY Competing theories suggest that neurons that generate persistent activity or do not are primarily responsible for the maintenance of information, particularly regarding object working memory. While the two models have been debated on theoretical terms, direct comparison of empirical results have been lacking. Analysis of neural activity in a large database of prefrontal recordings revealed that neurons that generate persistent activity were primarily responsible for the maintenance of both spatial and object working memory.
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Bava JM, Wang Z, Bick SK, Englot DJ, Constantinidis C. Improving Visual Working Memory with Cholinergic Deep Brain Stimulation. Brain Sci 2023; 13:917. [PMID: 37371395 PMCID: PMC10296349 DOI: 10.3390/brainsci13060917] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 05/30/2023] [Accepted: 05/31/2023] [Indexed: 06/29/2023] Open
Abstract
Acetylcholine is a critical modulatory neurotransmitter for cognitive function. Cholinergic drugs improve cognitive performance and enhance neuronal activity in the sensory and association cortices. An alternative means of improving cognitive function is through the use of deep brain stimulation. Prior animal studies have demonstrated that stimulation of the nucleus basalis of Meynert through DBS improves cognitive performance on a visual working memory task to the same degree as cholinesterase inhibitors. Additionally, unlike current pharmacological treatments for neurocognitive disorders, DBS does not lose efficacy over time and adverse effects are rare. These findings suggest that DBS may be a promising alternative for treating cognitive impairments in neurodegenerative disorders such as Alzheimer's disease. Thus, further research and human trials should be considered to assess the potential of DBS as a therapeutic treatment for these disorders.
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Affiliation(s)
- Janki M. Bava
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; (J.M.B.); (D.J.E.)
| | - Zhengyang Wang
- Neuroscience Program, Vanderbilt University, Nashville, TN 37235, USA;
| | - Sarah K. Bick
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA;
| | - Dario J. Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; (J.M.B.); (D.J.E.)
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA;
| | - Christos Constantinidis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; (J.M.B.); (D.J.E.)
- Neuroscience Program, Vanderbilt University, Nashville, TN 37235, USA;
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA;
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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44
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Yan M, Zhang WH, Wang H, Wong KYM. Bimodular continuous attractor neural networks with static and moving stimuli. Phys Rev E 2023; 107:064302. [PMID: 37464697 DOI: 10.1103/physreve.107.064302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 05/08/2023] [Indexed: 07/20/2023]
Abstract
We investigated the dynamical behaviors of bimodular continuous attractor neural networks, each processing a modality of sensory input and interacting with each other. We found that when bumps coexist in both modules, the position of each bump is shifted towards the other input when the intermodular couplings are excitatory and is shifted away when inhibitory. When one intermodular coupling is excitatory while another is moderately inhibitory, temporally modulated population spikes can be generated. On further increase of the inhibitory coupling, momentary spikes will emerge. In the regime of bump coexistence, bump heights are primarily strengthened by excitatory intermodular couplings, but there is a lesser weakening effect due to a bump being displaced from the direct input. When bimodular networks serve as decoders of multisensory integration, we extend the Bayesian framework to show that excitatory and inhibitory couplings encode attractive and repulsive priors, respectively. At low disparity, the bump positions decode the posterior means in the Bayesian framework, whereas at high disparity, multiple steady states exist. In the regime of multiple steady states, the less stable state can be accessed if the input causing the more stable state arrives after a sufficiently long delay. When one input is moving, the bump in the corresponding module is pinned when the moving stimulus is weak, unpinned at intermediate stimulus strength, and tracks the input at strong stimulus strength, and the stimulus strengths for these transitions increase with the velocity of the moving stimulus. These results are important to understanding multisensory integration of static and dynamic stimuli.
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Affiliation(s)
- Min Yan
- Department of Physics, Hong Kong University of Science and Technology, Hong Kong SAR, People's Republic of China
| | - Wen-Hao Zhang
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas 75390, USA
- O'Donnell Brain Institute, UT Southwestern Medical Center, Dallas, Texas 75390, USA
| | - He Wang
- Department of Physics, Hong Kong University of Science and Technology, Hong Kong SAR, People's Republic of China
- Hong Kong University of Science and Technology, Shenzhen Research Institute, Shenzhen 518057, China
| | - K Y Michael Wong
- Department of Physics, Hong Kong University of Science and Technology, Hong Kong SAR, People's Republic of China
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Langdon C, Genkin M, Engel TA. A unifying perspective on neural manifolds and circuits for cognition. Nat Rev Neurosci 2023; 24:363-377. [PMID: 37055616 PMCID: PMC11058347 DOI: 10.1038/s41583-023-00693-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/06/2023] [Indexed: 04/15/2023]
Abstract
Two different perspectives have informed efforts to explain the link between the brain and behaviour. One approach seeks to identify neural circuit elements that carry out specific functions, emphasizing connectivity between neurons as a substrate for neural computations. Another approach centres on neural manifolds - low-dimensional representations of behavioural signals in neural population activity - and suggests that neural computations are realized by emergent dynamics. Although manifolds reveal an interpretable structure in heterogeneous neuronal activity, finding the corresponding structure in connectivity remains a challenge. We highlight examples in which establishing the correspondence between low-dimensional activity and connectivity has been possible, unifying the neural manifold and circuit perspectives. This relationship is conspicuous in systems in which the geometry of neural responses mirrors their spatial layout in the brain, such as the fly navigational system. Furthermore, we describe evidence that, in systems in which neural responses are heterogeneous, the circuit comprises interactions between activity patterns on the manifold via low-rank connectivity. We suggest that unifying the manifold and circuit approaches is important if we are to be able to causally test theories about the neural computations that underlie behaviour.
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Affiliation(s)
- Christopher Langdon
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Mikhail Genkin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Tatiana A Engel
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
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Chunharas C, Hettwer MD, Wolff MJ, Rademaker RL. A gradual transition from veridical to categorical representations along the visual hierarchy during working memory, but not perception. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.18.541327. [PMID: 37292916 PMCID: PMC10245673 DOI: 10.1101/2023.05.18.541327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The ability to stably maintain visual information over brief delays is central to cognitive functioning. One possible way to achieve robust working memory maintenance is by having multiple concurrent mnemonic representations across multiple cortical loci. For example, early visual cortex might contribute to storage by representing information in a "sensory-like" format, while intraparietal sulcus uses a format transformed away from sensory driven responses. As an explicit test of mnemonic code transformations along the visual hierarchy, we quantitatively modeled the progression of veridical-to-categorical orientation representations in human participants. Participants directly viewed, or held in mind, an oriented grating pattern, and the similarity between fMRI activation patterns for different orientations was calculated throughout retinotopic cortex. During direct perception, similarity was clustered around cardinal orientations, while during working memory the obliques were represented more similarly. We modeled these similarity patterns based on the known distribution of orientation information in the natural world: The "veridical" model uses an efficient coding framework to capture hypothesized representations during visual perception. The "categorical" model assumes that different "psychological distances" between orientations result in orientation categorization relative to cardinal axes. During direct perception, the veridical model explained the data well in early visual areas, while the categorical model did worse. During working memory, the veridical model only explained some of the data, while the categorical model gradually gained explanatory power for increasingly anterior retinotopic regions. These findings suggest that directly viewed images are represented veridically, but once visual information is no longer tethered to the sensory world, there is a gradual progression to more categorical mnemonic formats along the visual hierarchy.
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Affiliation(s)
- Chaipat Chunharas
- Department of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok, Thailand
| | - Meike D Hettwer
- Max Planck School of Cognition, Max Planck Institute of Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Germany
| | - Michael J Wolff
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with the Max Planck Society, Frankfurt, Germany
| | - Rosanne L Rademaker
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with the Max Planck Society, Frankfurt, Germany
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Abdalaziz M, Redding ZV, Fiebelkorn IC. Rhythmic temporal coordination of neural activity prevents representational conflict during working memory. Curr Biol 2023; 33:1855-1863.e3. [PMID: 37100058 DOI: 10.1016/j.cub.2023.03.088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/27/2023] [Accepted: 03/31/2023] [Indexed: 04/28/2023]
Abstract
Selective attention1 is characterized by alternating states associated with either attentional sampling or attentional shifting, helping to prevent functional conflicts by isolating function-specific neural activity in time.2,3,4,5 We hypothesized that such rhythmic temporal coordination might also help to prevent representational conflicts during working memory.6 Multiple items can be simultaneously held in working memory, and these items can be represented by overlapping neural populations.7,8,9 Traditional theories propose that the short-term storage of to-be-remembered items occurs through persistent neural activity,10,11,12 but when neurons are simultaneously representing multiple items, persistent activity creates a potential for representational conflicts. In comparison, more recent, "activity-silent" theories of working memory propose that synaptic changes also contribute to short-term storage of to-be-remembered items.13,14,15,16 Transient bursts in neural activity,17 rather than persistent activity, could serve to occasionally refresh these synaptic changes. Here, we used EEG and response times to test whether rhythmic temporal coordination helps to isolate neural activity associated with different to-be-remembered items, thereby helping to prevent representational conflicts. Consistent with this hypothesis, we report that the relative strength of different item representations alternates over time as a function of the frequency-specific phase. Although RTs were linked to theta (∼6 Hz) and beta (∼25 Hz) phases during a memory delay, the relative strength of item representations only alternated as a function of the beta phase. The present findings (1) are consistent with rhythmic temporal coordination being a general mechanism for preventing functional or representational conflicts during cognitive processes and (2) inform models describing the role of oscillatory dynamics in organizing working memory.13,18,19,20,21.
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Affiliation(s)
- Miral Abdalaziz
- Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY 14627, USA
| | - Zach V Redding
- Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY 14627, USA
| | - Ian C Fiebelkorn
- Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY 14627, USA.
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Glavis-Bloom C, Vanderlip CR, Weiser Novak S, Kuwajima M, Kirk L, Harris KM, Manor U, Reynolds JH. Violation of the ultrastructural size principle in the dorsolateral prefrontal cortex underlies working memory impairment in the aged common marmoset (Callithrix jacchus). Front Aging Neurosci 2023; 15:1146245. [PMID: 37122384 PMCID: PMC10132463 DOI: 10.3389/fnagi.2023.1146245] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/21/2023] [Indexed: 05/02/2023] Open
Abstract
Morphology and function of the dorsolateral prefrontal cortex (dlPFC), and corresponding working memory performance, are affected early in the aging process, but nearly half of aged individuals are spared of working memory deficits. Translationally relevant model systems are critical for determining the neurobiological drivers of this variability. The common marmoset (Callithrix jacchus) is advantageous as a model for these investigations because, as a non-human primate, marmosets have a clearly defined dlPFC that enables measurement of prefrontal-dependent cognitive functions, and their short (∼10 year) lifespan facilitates longitudinal studies of aging. Previously, we characterized working memory capacity in a cohort of marmosets that collectively covered the lifespan, and found age-related working memory impairment. We also found a remarkable degree of heterogeneity in performance, similar to that found in humans. Here, we tested the hypothesis that changes to synaptic ultrastructure that affect synaptic efficacy stratify marmosets that age with cognitive impairment from those that age without cognitive impairment. We utilized electron microscopy to visualize synapses in the marmoset dlPFC and measured the sizes of boutons, presynaptic mitochondria, and synapses. We found that coordinated scaling of the sizes of synapses and mitochondria with their associated boutons is essential for intact working memory performance in aged marmosets. Further, lack of synaptic scaling, due to a remarkable failure of synaptic mitochondria to scale with presynaptic boutons, selectively underlies age-related working memory impairment. We posit that this decoupling results in mismatched energy supply and demand, leading to impaired synaptic transmission. We also found that aged marmosets have fewer synapses in dlPFC than young, though the severity of synapse loss did not predict whether aging occurred with or without cognitive impairment. This work identifies a novel mechanism of synapse dysfunction that stratifies marmosets that age with cognitive impairment from those that age without cognitive impairment. The process by which synaptic scaling is regulated is yet unknown and warrants future investigation.
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Affiliation(s)
- Courtney Glavis-Bloom
- Salk Institute for Biological Studies, Systems Neurobiology Laboratory, La Jolla, CA, United States
| | - Casey R. Vanderlip
- Salk Institute for Biological Studies, Systems Neurobiology Laboratory, La Jolla, CA, United States
| | - Sammy Weiser Novak
- Salk Institute for Biological Studies, Waitt Advanced Biophotonics Center, La Jolla, CA, United States
| | - Masaaki Kuwajima
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - Lyndsey Kirk
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - Kristen M. Harris
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - Uri Manor
- Salk Institute for Biological Studies, Waitt Advanced Biophotonics Center, La Jolla, CA, United States
| | - John H. Reynolds
- Salk Institute for Biological Studies, Systems Neurobiology Laboratory, La Jolla, CA, United States
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49
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Libedinsky C. Comparing representations and computations in single neurons versus neural networks. Trends Cogn Sci 2023; 27:517-527. [PMID: 37005114 DOI: 10.1016/j.tics.2023.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 04/03/2023]
Abstract
Single-neuron-level explanations have been the gold standard in neuroscience for decades. Recently, however, neural-network-level explanations have become increasingly popular. This increase in popularity is driven by the fact that the analysis of neural networks can solve problems that cannot be addressed by analyzing neurons independently. In this opinion article, I argue that while both frameworks employ the same general logic to link physical and mental phenomena, in many cases the neural network framework provides better explanatory objects to understand representations and computations related to mental phenomena. I discuss what constitutes a mechanistic explanation in neural systems, provide examples, and conclude by highlighting a number of the challenges and considerations associated with the use of analyses of neural networks to study brain function.
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50
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Xie W, Chapeton JI, Bhasin S, Zawora C, Wittig JH, Inati SK, Zhang W, Zaghloul KA. The medial temporal lobe supports the quality of visual short-term memory representation. Nat Hum Behav 2023; 7:627-641. [PMID: 36864132 DOI: 10.1038/s41562-023-01529-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 01/12/2023] [Indexed: 03/04/2023]
Abstract
The quality of short-term memory (STM) underlies our ability to recall the exact details of a recent event, yet how the human brain enables this core cognitive function remains poorly understood. Here we use multiple experimental approaches to test the hypothesis that the quality of STM, such as its precision or fidelity, relies on the medial temporal lobe (MTL), a region commonly associated with the ability to distinguish similar information remembered in long-term memory. First, with intracranial recordings, we find that delay-period MTL activity retains item-specific STM content that is predictive of subsequent recall precision. Second, STM recall precision is associated with an increase in the strength of intrinsic MTL-to-neocortical functional connections during a brief retention interval. Finally, perturbing the MTL through electrical stimulation or surgical removal can selectively reduce STM precision. Collectively, these findings provide converging evidence that the MTL is critically involved in the quality of STM representation.
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Affiliation(s)
- Weizhen Xie
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
| | - Julio I Chapeton
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Srijan Bhasin
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Christopher Zawora
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - John H Wittig
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Sara K Inati
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Weiwei Zhang
- Department of Psychology, University of California, Riverside, CA, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
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