1
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Bayones L, Zainos A, Alvarez M, Romo R, Franci A, Rossi-Pool R. Orthogonality of sensory and contextual categorical dynamics embedded in a continuum of responses from the second somatosensory cortex. Proc Natl Acad Sci U S A 2024; 121:e2316765121. [PMID: 38990946 DOI: 10.1073/pnas.2316765121] [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] [Accepted: 06/12/2024] [Indexed: 07/13/2024] Open
Abstract
How does the brain simultaneously process signals that bring complementary information, like raw sensory signals and their transformed counterparts, without any disruptive interference? Contemporary research underscores the brain's adeptness in using decorrelated responses to reduce such interference. Both neurophysiological findings and artificial neural networks support the notion of orthogonal representation for signal differentiation and parallel processing. Yet, where, and how raw sensory signals are transformed into more abstract representations remains unclear. Using a temporal pattern discrimination task in trained monkeys, we revealed that the second somatosensory cortex (S2) efficiently segregates faithful and transformed neural responses into orthogonal subspaces. Importantly, S2 population encoding for transformed signals, but not for faithful ones, disappeared during a nondemanding version of this task, which suggests that signal transformation and their decoding from downstream areas are only active on-demand. A mechanistic computation model points to gain modulation as a possible biological mechanism for the observed context-dependent computation. Furthermore, individual neural activities that underlie the orthogonal population representations exhibited a continuum of responses, with no well-determined clusters. These findings advocate that the brain, while employing a continuum of heterogeneous neural responses, splits population signals into orthogonal subspaces in a context-dependent fashion to enhance robustness, performance, and improve coding efficiency.
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Affiliation(s)
- Lucas Bayones
- Instituto de Fisiología Celular, Departamento de Neurociencia Cognitiva, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Antonio Zainos
- Instituto de Fisiología Celular, Departamento de Neurociencia Cognitiva, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Manuel Alvarez
- Instituto de Fisiología Celular, Departamento de Neurociencia Cognitiva, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | | | - Alessio Franci
- Departmento de Matemática, Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
- Montefiore Institute, University of Liège, Liège 4000, Belgique
- Wallon ExceLlence (WEL) Research Institute, Wavre 1300, Belgique
| | - Román Rossi-Pool
- Instituto de Fisiología Celular, Departamento de Neurociencia Cognitiva, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
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2
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Hajnal MA, Tran D, Szabó Z, Albert A, Safaryan K, Einstein M, Vallejo Martelo M, Polack PO, Golshani P, Orbán G. Shifts in attention drive context-dependent subspace encoding in anterior cingulate cortex in mice during decision making. Nat Commun 2024; 15:5559. [PMID: 38956080 PMCID: PMC11220070 DOI: 10.1038/s41467-024-49845-2] [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: 10/11/2023] [Accepted: 06/19/2024] [Indexed: 07/04/2024] Open
Abstract
Attention supports decision making by selecting the features that are relevant for decisions. Selective enhancement of the relevant features and inhibition of distractors has been proposed as potential neural mechanisms driving this selection process. Yet, how attention operates when relevance cannot be directly determined, and the attention signal needs to be internally constructed is less understood. Here we recorded from populations of neurons in the anterior cingulate cortex (ACC) of mice in an attention-shifting task where relevance of stimulus modalities changed across blocks of trials. In contrast with V1 recordings, decoding of the irrelevant modality gradually declined in ACC after an initial transient. Our analytical proof and a recurrent neural network model of the task revealed mutually inhibiting connections that produced context-gated suppression as observed in mice. Using this RNN model we predicted a correlation between contextual modulation of individual neurons and their stimulus drive, which we confirmed in ACC but not in V1.
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Affiliation(s)
- Márton Albert Hajnal
- Department of Computational Sciences, HUN-REN Wigner Research Centre for Physics, Budapest, Hungary.
| | - Duy Tran
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Albert Einstein College of Medicine, New York, NY, USA
| | - Zsombor Szabó
- Department of Computational Sciences, HUN-REN Wigner Research Centre for Physics, Budapest, Hungary
| | - Andrea Albert
- Department of Computational Sciences, HUN-REN Wigner Research Centre for Physics, Budapest, Hungary
| | - Karen Safaryan
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Michael Einstein
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Mauricio Vallejo Martelo
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Pierre-Olivier Polack
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA
| | - Peyman Golshani
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, USA.
- West Los Angeles VA Medical Center, Los Angeles, CA, USA.
| | - Gergő Orbán
- Department of Computational Sciences, HUN-REN Wigner Research Centre for Physics, Budapest, Hungary.
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3
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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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4
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Miller JA, Constantinidis C. Timescales of learning in prefrontal cortex. Nat Rev Neurosci 2024:10.1038/s41583-024-00836-8. [PMID: 38937654 DOI: 10.1038/s41583-024-00836-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/03/2024] [Indexed: 06/29/2024]
Abstract
The lateral prefrontal cortex (PFC) in humans and other primates is critical for immediate, goal-directed behaviour and working memory, which are classically considered distinct from the cognitive and neural circuits that support long-term learning and memory. Over the past few years, a reconsideration of this textbook perspective has emerged, in that different timescales of memory-guided behaviour are in constant interaction during the pursuit of immediate goals. Here, we will first detail how neural activity related to the shortest timescales of goal-directed behaviour (which requires maintenance of current states and goals in working memory) is sculpted by long-term knowledge and learning - that is, how the past informs present behaviour. Then, we will outline how learning across different timescales (from seconds to years) drives plasticity in the primate lateral PFC, from single neuron firing rates to mesoscale neuroimaging activity patterns. Finally, we will review how, over days and months of learning, dense local and long-range connectivity patterns in PFC facilitate longer-lasting changes in population activity by changing synaptic weights and recruiting additional neural resources to inform future behaviour. Our Review sheds light on how the machinery of plasticity in PFC circuits facilitates the integration of learned experiences across time to best guide adaptive behaviour.
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Affiliation(s)
- Jacob A Miller
- Wu Tsai Institute, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 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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5
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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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6
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Zhou S, Buonomano DV. Unified control of temporal and spatial scales of sensorimotor behavior through neuromodulation of short-term synaptic plasticity. SCIENCE ADVANCES 2024; 10:eadk7257. [PMID: 38701208 DOI: 10.1126/sciadv.adk7257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 04/03/2024] [Indexed: 05/05/2024]
Abstract
Neuromodulators have been shown to alter the temporal profile of short-term synaptic plasticity (STP); however, the computational function of this neuromodulation remains unexplored. Here, we propose that the neuromodulation of STP provides a general mechanism to scale neural dynamics and motor outputs in time and space. We trained recurrent neural networks that incorporated STP to produce complex motor trajectories-handwritten digits-with different temporal (speed) and spatial (size) scales. Neuromodulation of STP produced temporal and spatial scaling of the learned dynamics and enhanced temporal or spatial generalization compared to standard training of the synaptic weights in the absence of STP. The model also accounted for the results of two experimental studies involving flexible sensorimotor timing. Neuromodulation of STP provides a unified and biologically plausible mechanism to control the temporal and spatial scales of neural dynamics and sensorimotor behaviors.
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Affiliation(s)
- Shanglin Zhou
- Institute for Translational Brain Research, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhongshan Hospital, Fudan University, Shanghai, China
| | - Dean V Buonomano
- Department of Neurobiology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
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7
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Shin YS, Sheremata SL. When remembering less is more: Unfiltered items are associated with reduced memory fidelity in visual short-term memory. Atten Percept Psychophys 2024; 86:1248-1258. [PMID: 38684591 DOI: 10.3758/s13414-024-02891-w] [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] [Accepted: 03/29/2024] [Indexed: 05/02/2024]
Abstract
Visual short-term memory (VSTM), the ability to store information no longer visible, is essential for human behavior. VSTM limits vary across the population and are correlated with overall cognitive ability. It has been proposed that low-memory individuals are unable to select only relevant items for storage and that these limitations are greatest when memory demands are high. However, it is unknown whether these effects simply reflect task difficulty and whether they impact the quality of memory representations. Here we varied the number of items presented, or set size, to investigate the effect of memory demands on the performance of visual short-term memory across low- and high-memory groups. Group differences emerged as set size exceeded memory limits, even when task difficulty was controlled. In a change-detection task, the low-memory group performed more poorly when set size exceeded their memory limits. We then predicted that low-memory individuals encoding items beyond measured memory limits would result in the degraded fidelity of memory representations. A continuous report task confirmed that low, but not high, memory individuals demonstrated decreased memory fidelity as set size exceeded measured memory limits. The current study demonstrates that items held in VSTM are stored distinctly across groups and task demands. These results link the ability to maintain high quality representations with overall cognitive ability.
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Affiliation(s)
- Young Seon Shin
- Laboratory for Rehabilitation Neuroscience, University of Florida, 1864 Stadium Road, Gainesville, FL, 32611, USA.
| | - Summer L Sheremata
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, 33431, USA
- Department of Psychology, Florida Atlantic University, Boca Raton, FL, 33431, USA
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8
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Jones B, Snyder L, Ching S. Heterogeneous Forgetting Rates and Greedy Allocation in Slot-Based Memory Networks Promotes Signal Retention. Neural Comput 2024; 36:1022-1040. [PMID: 38658026 PMCID: PMC11045047 DOI: 10.1162/neco_a_01655] [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: 09/07/2023] [Accepted: 01/10/2024] [Indexed: 04/26/2024]
Abstract
A key question in the neuroscience of memory encoding pertains to the mechanisms by which afferent stimuli are allocated within memory networks. This issue is especially pronounced in the domain of working memory, where capacity is finite. Presumably the brain must embed some "policy" by which to allocate these mnemonic resources in an online manner in order to maximally represent and store afferent information for as long as possible and without interference from subsequent stimuli. Here, we engage this question through a top-down theoretical modeling framework. We formally optimize a gating mechanism that projects afferent stimuli onto a finite number of memory slots within a recurrent network architecture. In the absence of external input, the activity in each slot attenuates over time (i.e., a process of gradual forgetting). It turns out that the optimal gating policy consists of a direct projection from sensory activity to memory slots, alongside an activity-dependent lateral inhibition. Interestingly, allocating resources myopically (greedily with respect to the current stimulus) leads to efficient utilization of slots over time. In other words, later-arriving stimuli are distributed across slots in such a way that the network state is minimally shifted and so prior signals are minimally "overwritten." Further, networks with heterogeneity in the timescales of their forgetting rates retain stimuli better than those that are more homogeneous. Our results suggest how online, recurrent networks working on temporally localized objectives without high-level supervision can nonetheless implement efficient allocation of memory resources over time.
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Affiliation(s)
- BethAnna Jones
- Department of Electrical and Systems Science, Washington University in St. Louis, St. Louis, MO 63130, U.S.A.
| | - Lawrence Snyder
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO 63130, U.S.A.
| | - ShiNung Ching
- Department of Electrical and Systems Science, Washington University in St. Louis, St. Louis, MO 63130, U.S.A.
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9
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Wu H, Liang X, Wang R, Ma Y, Gao Y, Ning X. A Multivariate analysis on evoked components of Chinese semantic congruity: an OP-MEG study with EEG. Cereb Cortex 2024; 34:bhae108. [PMID: 38610084 DOI: 10.1093/cercor/bhae108] [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: 01/08/2024] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 04/14/2024] Open
Abstract
The application of wearable magnetoencephalography using optically-pumped magnetometers has drawn extensive attention in the field of neuroscience. Electroencephalogram system can cover the whole head and reflect the overall activity of a large number of neurons. The efficacy of optically-pumped magnetometer in detecting event-related components can be validated through electroencephalogram results. Multivariate pattern analysis is capable of tracking the evolution of neurocognitive processes over time. In this paper, we adopted a classical Chinese semantic congruity paradigm and separately collected electroencephalogram and optically-pumped magnetometer signals. Then, we verified the consistency of optically-pumped magnetometer and electroencephalogram in detecting N400 using mutual information index. Multivariate pattern analysis revealed the difference in decoding performance of these two modalities, which can be further validated by dynamic/stable coding analysis on the temporal generalization matrix. The results from searchlight analysis provided a neural basis for this dissimilarity at the magnetoencephalography source level and the electroencephalogram sensor level. This study opens a new avenue for investigating the brain's coding patterns using wearable magnetoencephalography and reveals the differences in sensitivity between the two modalities in reflecting neuron representation patterns.
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Affiliation(s)
- Huanqi Wu
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
- Hangzhou Institute of National Extremely-weak Magnetic Field Infrastructure, Hangzhou 310051, China
- Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou Innovation Institute, Beihang University, Beijing 100191, China
| | - Xiaoyu Liang
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
- Hangzhou Institute of National Extremely-weak Magnetic Field Infrastructure, Hangzhou 310051, China
- Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou Innovation Institute, Beihang University, Beijing 100191, China
| | - Ruonan Wang
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
- Hangzhou Institute of National Extremely-weak Magnetic Field Infrastructure, Hangzhou 310051, China
- Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou Innovation Institute, Beihang University, Beijing 100191, China
| | - Yuyu Ma
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
- Hangzhou Institute of National Extremely-weak Magnetic Field Infrastructure, Hangzhou 310051, China
- Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou Innovation Institute, Beihang University, Beijing 100191, China
| | - Yang Gao
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
- Hangzhou Institute of National Extremely-weak Magnetic Field Infrastructure, Hangzhou 310051, China
- Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou Innovation Institute, Beihang University, Beijing 100191, China
| | - Xiaolin Ning
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
- Hangzhou Institute of National Extremely-weak Magnetic Field Infrastructure, Hangzhou 310051, China
- Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou Innovation Institute, Beihang University, Beijing 100191, China
- Shandong Key Laboratory for Magnetic Field-free Medicine & Functional Imaging, Institute of Magnetic Field-free Medicine & Functional Imaging, Shandong University, Shandong 264209, China
- Hefei National Laboratory, Anhui 230026, China
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10
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Lopez MR, Wasberg SMH, Gagliardi CM, Normandin ME, Muzzio IA. Mystery of the memory engram: History, current knowledge, and unanswered questions. Neurosci Biobehav Rev 2024; 159:105574. [PMID: 38331127 DOI: 10.1016/j.neubiorev.2024.105574] [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] [Received: 09/18/2023] [Revised: 12/22/2023] [Accepted: 02/03/2024] [Indexed: 02/10/2024]
Abstract
The quest to understand the memory engram has intrigued humans for centuries. Recent technological advances, including genetic labelling, imaging, optogenetic and chemogenetic techniques, have propelled the field of memory research forward. These tools have enabled researchers to create and erase memory components. While these innovative techniques have yielded invaluable insights, they often focus on specific elements of the memory trace. Genetic labelling may rely on a particular immediate early gene as a marker of activity, optogenetics may activate or inhibit one specific type of neuron, and imaging may capture activity snapshots in a given brain region at specific times. Yet, memories are multifaceted, involving diverse arrays of neuronal subpopulations, circuits, and regions that work in concert to create, store, and retrieve information. Consideration of contributions of both excitatory and inhibitory neurons, micro and macro circuits across brain regions, the dynamic nature of active ensembles, and representational drift is crucial for a comprehensive understanding of the complex nature of memory.
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Affiliation(s)
- M R Lopez
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
| | - S M H Wasberg
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA 52242, USA
| | - C M Gagliardi
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA 52242, USA
| | - M E Normandin
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA 52242, USA
| | - I A Muzzio
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA 52242, USA.
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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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Ma F, Zhang L, Zhou J. Event-specific and persistent representations for contextual states in orbitofrontal neurons. Curr Biol 2024; 34:1023-1033.e5. [PMID: 38366594 DOI: 10.1016/j.cub.2024.01.060] [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] [Received: 09/25/2023] [Revised: 12/13/2023] [Accepted: 01/24/2024] [Indexed: 02/18/2024]
Abstract
Flexible and context-dependent behaviors require animals, including humans, to identify their current contextual state for proper rules to apply, especially when information that defines these states is partially observable. Depending on behavioral needs, contextual states usually persist for prolonged periods and across other events, including sensory stimuli, actions, and rewards, highlighting prominent challenges of holding a reliable state representation. The orbitofrontal cortex (OFC) is crucial in behaviors requiring the identification of the current context (e.g., reversal learning); however, how single units in the OFC accomplish this function has not been assessed. Do they maintain such information persistently, in separate populations from those responding phasically to events within a task, or is contextual information dynamic and embedded in these phasic responses? Here, we investigated this question by recording single units from OFC in rats performing a task that required them to identify the current contextual state related to estimated proximity to future reward with distracting olfactory cues. We found that while some OFC neurons encode contextual states, most change their selectivity upon the transition of task events. Nevertheless, despite dynamic activities in single neurons, the neural populations maintain persistent representations regarding current contextual states within particular neural subspaces.
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Affiliation(s)
- Fengjun Ma
- College of Biological Sciences, China Agricultural University, Beijing 100193, China; Chinese Institute for Brain Research, Beijing 102206, China
| | - Lingwei Zhang
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Jingfeng Zhou
- Chinese Institute for Brain Research, Beijing 102206, China.
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13
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Santo-Angles A, Temudo A, Babushkin V, Sreenivasan KK. Effective connectivity of working memory performance: a DCM study of MEG data. Front Hum Neurosci 2024; 18:1339728. [PMID: 38501039 PMCID: PMC10944968 DOI: 10.3389/fnhum.2024.1339728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 02/12/2024] [Indexed: 03/20/2024] Open
Abstract
Visual working memory (WM) engages several nodes of a large-scale network that includes frontal, parietal, and visual regions; however, little is understood about how these regions interact to support WM behavior. In particular, it is unclear whether network dynamics during WM maintenance primarily represent feedforward or feedback connections. This question has important implications for current debates about the relative roles of frontoparietal and visual regions in WM maintenance. In the current study, we investigated the network activity supporting WM using MEG data acquired while healthy subjects performed a multi-item delayed estimation WM task. We used computational modeling of behavior to discriminate correct responses (high accuracy trials) from two different types of incorrect responses (low accuracy and swap trials), and dynamic causal modeling of MEG data to measure effective connectivity. We observed behaviorally dependent changes in effective connectivity in a brain network comprising frontoparietal and early visual areas. In comparison with high accuracy trials, frontoparietal and frontooccipital networks showed disrupted signals depending on type of behavioral error. Low accuracy trials showed disrupted feedback signals during early portions of WM maintenance and disrupted feedforward signals during later portions of maintenance delay, while swap errors showed disrupted feedback signals during the whole delay period. These results support a distributed model of WM that emphasizes the role of visual regions in WM storage and where changes in large scale network configurations can have important consequences for memory-guided behavior.
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Affiliation(s)
- Aniol Santo-Angles
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
- Center for Brain and Health, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Ainsley Temudo
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Vahan Babushkin
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
| | - Kartik K. Sreenivasan
- Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
- Center for Brain and Health, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
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14
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Bi Z, Li H, Tian L. Top-down generation of low-resolution representations improves visual perception and imagination. Neural Netw 2024; 171:440-456. [PMID: 38150870 DOI: 10.1016/j.neunet.2023.12.030] [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] [Received: 03/25/2023] [Revised: 11/30/2023] [Accepted: 12/18/2023] [Indexed: 12/29/2023]
Abstract
Perception or imagination requires top-down signals from high-level cortex to primary visual cortex (V1) to reconstruct or simulate the representations bottom-up stimulated by the seen images. Interestingly, top-down signals in V1 have lower spatial resolution than bottom-up representations. It is unclear why the brain uses low-resolution signals to reconstruct or simulate high-resolution representations. By modeling the top-down pathway of the visual system using the decoder of a variational auto-encoder (VAE), we reveal that low-resolution top-down signals can better reconstruct or simulate the information contained in the sparse activities of V1 simple cells, which facilitates perception and imagination. This advantage of low-resolution generation is related to facilitating high-level cortex to form geometry-respecting representations observed in experiments. Furthermore, we present two findings regarding this phenomenon in the context of AI-generated sketches, a style of drawings made of lines. First, we found that the quality of the generated sketches critically depends on the thickness of the lines in the sketches: thin-line sketches are harder to generate than thick-line sketches. Second, we propose a technique to generate high-quality thin-line sketches: instead of directly using original thin-line sketches, we use blurred sketches to train VAE or GAN (generative adversarial network), and then infer the thin-line sketches from the VAE- or GAN-generated blurred sketches. Collectively, our work suggests that low-resolution top-down generation is a strategy the brain uses to improve visual perception and imagination, which inspires new sketch-generation AI techniques.
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Affiliation(s)
- Zedong Bi
- Lingang Laboratory, Shanghai 200031, China.
| | - Haoran Li
- Department of Physics, Hong Kong Baptist University, Hong Kong, China
| | - Liang Tian
- Department of Physics, Hong Kong Baptist University, Hong Kong, China; Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong, China; Institute of Systems Medicine and Health Sciences, Hong Kong Baptist University, Hong Kong, China; State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China.
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15
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Yao J, Hou R, Fan H, Liu J, Chen Z, Hou J, Cheng Q, Li CT. Prefrontal projections modulate recurrent circuitry in the insular cortex to support short-term memory. Cell Rep 2024; 43:113756. [PMID: 38358886 DOI: 10.1016/j.celrep.2024.113756] [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] [Received: 05/01/2023] [Revised: 11/30/2023] [Accepted: 01/23/2024] [Indexed: 02/17/2024] Open
Abstract
Short-term memory (STM) maintains information during a short delay period. How long-range and local connections interact to support STM encoding remains elusive. Here, we tackle the problem focusing on long-range projections from the medial prefrontal cortex (mPFC) to the anterior agranular insular cortex (aAIC) in head-fixed mice performing an olfactory delayed-response task. Optogenetic and electrophysiological experiments reveal the behavioral importance of the two regions in encoding STM information. Spike-correlogram analysis reveals strong local and cross-region functional coupling (FC) between memory neurons encoding the same information. Optogenetic suppression of mPFC-aAIC projections during the delay period reduces behavioral performance, the proportion of memory neurons, and memory-specific FC within the aAIC, whereas optogenetic excitation enhances all of them. mPFC-aAIC projections also bidirectionally modulate the efficacy of STM-information transfer, measured by the contribution of FC spiking pairs to the memory-coding ability of following neurons. Thus, prefrontal projections modulate insular neurons' functional connectivity and memory-coding ability to support STM.
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Affiliation(s)
- Jian Yao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; Lingang Laboratory, Shanghai 200031, China
| | - Ruiqing Hou
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Hongmei Fan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jiawei Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhaoqin Chen
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 200031, China
| | - Jincan Hou
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; Lingang Laboratory, Shanghai 200031, China
| | - Qi Cheng
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; Lingang Laboratory, Shanghai 200031, China
| | - Chengyu T Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; Lingang Laboratory, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 200031, China.
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16
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Affan RO, Bright IM, Pemberton LN, Cruzado NA, Scott BB, Howard MW. Ramping Dynamics in the Frontal Cortex Unfold Over Multiple Timescales During Motor Planning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.05.578819. [PMID: 38370792 PMCID: PMC10871223 DOI: 10.1101/2024.02.05.578819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Plans are formulated and refined over the period leading to their execution, ensuring that the appropriate behavior is enacted at just the right time. While existing evidence suggests that memory circuits convey the passage of time through diverse neuronal responses, it remains unclear whether the neural circuits involved in planning behavior exhibit analogous temporal dynamics. Using publicly available data, we analyzed how activity in the frontal motor cortex evolves during motor planning. Individual neurons exhibited diverse ramping activity throughout a delay interval that preceded a planned movement. The collective activity of these neurons was useful for making temporal predictions that became increasingly precise as the movement time approached. This temporal diversity gave rise to a spectrum of encoding patterns, ranging from stable to dynamic representations of the upcoming movement. Our results indicate that neural activity unfolds over multiple timescales during motor planning, suggesting a shared mechanism in the brain for processing temporal information related to both past memories and future plans.
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Affiliation(s)
- R O Affan
- Graduate Program in Neuroscience, Boston University, Boston, MA
| | - I M Bright
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215
| | - L N Pemberton
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215
| | - N A Cruzado
- Graduate Program in Neuroscience, Boston University, Boston, MA
| | - B B Scott
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215
| | - M W Howard
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215
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17
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Sharma KK, Diltz MA, Lincoln T, Albuquerque ER, Romanski LM. Neuronal Population Encoding of Identity in Primate Prefrontal Cortex. J Neurosci 2024; 44:e0703232023. [PMID: 37963766 PMCID: PMC10860606 DOI: 10.1523/jneurosci.0703-23.2023] [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: 04/19/2023] [Revised: 08/22/2023] [Accepted: 10/10/2023] [Indexed: 11/16/2023] Open
Abstract
The ventrolateral prefrontal cortex (VLPFC) shows robust activation during the perception of faces and voices. However, little is known about what categorical features of social stimuli drive neural activity in this region. Since perception of identity and expression are critical social functions, we examined whether neural responses to naturalistic stimuli were driven by these two categorical features in the prefrontal cortex. We recorded single neurons in the VLPFC, while two male rhesus macaques (Macaca mulatta) viewed short audiovisual videos of unfamiliar conspecifics making expressions of aggressive, affiliative, and neutral valence. Of the 285 neurons responsive to the audiovisual stimuli, 111 neurons had a main effect (two-way ANOVA) of identity, expression, or their interaction in their stimulus-related firing rates; however, decoding of expression and identity using single-unit firing rates rendered poor accuracy. Interestingly, when decoding from pseudo-populations of recorded neurons, the accuracy for both expression and identity increased with population size, suggesting that the population transmitted information relevant to both variables. Principal components analysis of mean population activity across time revealed that population responses to the same identity followed similar trajectories in the response space, facilitating segregation from other identities. Our results suggest that identity is a critical feature of social stimuli that dictates the structure of population activity in the VLPFC, during the perception of vocalizations and their corresponding facial expressions. These findings enhance our understanding of the role of the VLPFC in social behavior.
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Affiliation(s)
- K K Sharma
- Department of Neuroscience, School of Medicine and Dentistry, University of Rochester, Rochester, New York 14620
| | - M A Diltz
- Department of Neuroscience, School of Medicine and Dentistry, University of Rochester, Rochester, New York 14620
| | - T Lincoln
- Department of Neuroscience, School of Medicine and Dentistry, University of Rochester, Rochester, New York 14620
| | - E R Albuquerque
- Department of Neuroscience, School of Medicine and Dentistry, University of Rochester, Rochester, New York 14620
| | - L M Romanski
- Department of Neuroscience, School of Medicine and Dentistry, University of Rochester, Rochester, New York 14620
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18
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Crosser JT, Brinkman BAW. Applications of information geometry to spiking neural network activity. Phys Rev E 2024; 109:024302. [PMID: 38491696 DOI: 10.1103/physreve.109.024302] [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: 05/18/2023] [Accepted: 01/10/2024] [Indexed: 03/18/2024]
Abstract
The space of possible behaviors that complex biological systems may exhibit is unimaginably vast, and these systems often appear to be stochastic, whether due to variable noisy environmental inputs or intrinsically generated chaos. The brain is a prominent example of a biological system with complex behaviors. The number of possible patterns of spikes emitted by a local brain circuit is combinatorially large, although the brain may not make use of all of them. Understanding which of these possible patterns are actually used by the brain, and how those sets of patterns change as properties of neural circuitry change is a major goal in neuroscience. Recently, tools from information geometry have been used to study embeddings of probabilistic models onto a hierarchy of model manifolds that encode how model outputs change as a function of their parameters, giving a quantitative notion of "distances" between outputs. We apply this method to a network model of excitatory and inhibitory neural populations to understand how the competition between membrane and synaptic response timescales shapes the network's information geometry. The hyperbolic embedding allows us to identify the statistical parameters to which the model behavior is most sensitive, and demonstrate how the ranking of these coordinates changes with the balance of excitation and inhibition in the network.
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Affiliation(s)
- Jacob T Crosser
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, USA and Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York 11794, USA
| | - Braden A W Brinkman
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, USA and Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York 11794, USA
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19
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Lin XX, Nieder A, Jacob SN. The neuronal implementation of representational geometry in primate prefrontal cortex. SCIENCE ADVANCES 2023; 9:eadh8685. [PMID: 38091404 PMCID: PMC10848744 DOI: 10.1126/sciadv.adh8685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 11/14/2023] [Indexed: 12/18/2023]
Abstract
Modern neuroscience has seen the rise of a population-doctrine that represents cognitive variables using geometrical structures in activity space. Representational geometry does not, however, account for how individual neurons implement these representations. Leveraging the principle of sparse coding, we present a framework to dissect representational geometry into biologically interpretable components that retain links to single neurons. Applied to extracellular recordings from the primate prefrontal cortex in a working memory task with interference, the identified components revealed disentangled and sequential memory representations including the recovery of memory content after distraction, signals hidden to conventional analyses. Each component was contributed by small subpopulations of neurons with distinct spiking properties and response dynamics. Modeling showed that such sparse implementations are supported by recurrently connected circuits as in prefrontal cortex. The perspective of neuronal implementation links representational geometries to their cellular constituents, providing mechanistic insights into how neural systems encode and process information.
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Affiliation(s)
- Xiao-Xiong Lin
- Translational Neurotechnology Laboratory, Department of Neurosurgery, Klinikum rechts der Isar, Technical University of Munich, Germany
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-University Munich, Germany
| | | | - Simon N. Jacob
- Translational Neurotechnology Laboratory, Department of Neurosurgery, Klinikum rechts der Isar, Technical University of Munich, Germany
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20
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Ceccarelli F, Ferrucci L, Londei F, Ramawat S, Brunamonti E, Genovesio A. Static and dynamic coding in distinct cell types during associative learning in the prefrontal cortex. Nat Commun 2023; 14:8325. [PMID: 38097560 PMCID: PMC10721651 DOI: 10.1038/s41467-023-43712-2] [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: 03/08/2023] [Accepted: 11/17/2023] [Indexed: 12/17/2023] Open
Abstract
The prefrontal cortex maintains information in memory through static or dynamic population codes depending on task demands, but whether the population coding schemes used are learning-dependent and differ between cell types is currently unknown. We investigate the population coding properties and temporal stability of neurons recorded from male macaques in two mapping tasks during and after stimulus-response associative learning, and then we use a Strategy task with the same stimuli and responses as control. We identify a heterogeneous population coding for stimuli, responses, and novel associations: static for putative pyramidal cells and dynamic for putative interneurons that show the strongest selectivity for all the variables. The population coding of learned associations shows overall the highest stability driven by cell types, with interneurons changing from dynamic to static coding after successful learning. The results support that prefrontal microcircuitry expresses mixed population coding governed by cell types and changes its stability during associative learning.
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Affiliation(s)
- Francesco Ceccarelli
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Lorenzo Ferrucci
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Fabrizio Londei
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
- PhD program in Behavioral Neuroscience, Sapienza University, Rome, Italy
| | - Surabhi Ramawat
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Emiliano Brunamonti
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Aldo Genovesio
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy.
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21
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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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22
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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: 0] [Impact Index Per Article: 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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23
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Shinn M. Phantom oscillations in principal component analysis. Proc Natl Acad Sci U S A 2023; 120:e2311420120. [PMID: 37988465 PMCID: PMC10691246 DOI: 10.1073/pnas.2311420120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/18/2023] [Indexed: 11/23/2023] Open
Abstract
Principal component analysis (PCA) is a dimensionality reduction method that is known for being simple and easy to interpret. Principal components are often interpreted as low-dimensional patterns in high-dimensional space. However, this simple interpretation fails for timeseries, spatial maps, and other continuous data. In these cases, nonoscillatory data may have oscillatory principal components. Here, we show that two common properties of data cause oscillatory principal components: smoothness and shifts in time or space. These two properties implicate almost all neuroscience data. We show how the oscillations produced by PCA, which we call "phantom oscillations," impact data analysis. We also show that traditional cross-validation does not detect phantom oscillations, so we suggest procedures that do. Our findings are supported by a collection of mathematical proofs. Collectively, our work demonstrates that patterns which emerge from high-dimensional data analysis may not faithfully represent the underlying data.
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Affiliation(s)
- Maxwell Shinn
- University College London (UCL) Queen Square Institute of Neurology, University College London, LondonWC1E 6BT, United Kingdom
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24
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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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25
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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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26
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Hajnal MA, Tran D, Szabó Z, Albert A, Safaryan K, Einstein M, Martelo MV, Polack PO, Golshani P, Orbán G. Shifts in attention drive context-dependent subspace encoding in anterior cingulate cortex during decision making. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561737. [PMID: 37873364 PMCID: PMC10592791 DOI: 10.1101/2023.10.10.561737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Attention is a cognitive faculty that selects part of a larger set of percepts, driven by cues such as stimulus saliency, internal goals or priors. The enhancement of the attended representation and inhibition of distractors have been proposed as potential neural mechanisms driving this selection process. Yet, how attention operates when the cue has to be internally constructed from conflicting stimuli, decision rules, and reward contingencies, is less understood. Here we recorded from populations of neurons in the anterior cingulate cortex (ACC), an area implicated in ongoing error monitoring and correction during decision conflicts, in a challenging attention-shifting task. In this task, mice had to attend to the rewarded modality when presented identical auditory and visual stimuli in two contexts without direct external cues. In the ACC, the irrelevant stimulus continuously became less decodable than the relevant stimulus as the trial progressed to the decision point. This contrasted strongly with our previous findings in V1 where both relevant and irrelevant stimuli were equally decodable throughout the trial. Using analytical tools and a recurrent neural network (RNN) model, we found that the linearly independent representation of stimulus modalities in ACC was well suited to context-gated suppression of a stimulus modality. We demonstrated that the feedback structure of lateral connections in the RNN consisted of excitatory interactions between cell ensembles representing the same modality and mutual inhibition between cell ensembles representing distinct stimulus modalities. Using this RNN model showing signatures of context-gated suppression, we predicted that the level of contextual modulation of individual neurons should be correlated with their relative responsiveness to the two stimulus modalities used in the task. We verified this prediction in recordings from ACC neurons but not from recordings from V1 neurons. Therefore, ACC effectively operates on low-dimensional neuronal subspaces to combine stimulus related information with internal cues to drive actions under conflict.
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Affiliation(s)
- Márton Albert Hajnal
- Department of Computational Sciences, Wigner Research Center for Physics, Budapest, 1121, Hungary
| | - Duy Tran
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, United States
- Albert Einstein College of Medicine, New York, NY 10461, United States
| | - Zsombor Szabó
- Department of Computational Sciences, Wigner Research Center for Physics, Budapest, 1121, Hungary
| | - Andrea Albert
- Department of Computational Sciences, Wigner Research Center for Physics, Budapest, 1121, Hungary
| | - Karen Safaryan
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, United States
| | - Michael Einstein
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, United States
| | - Mauricio Vallejo Martelo
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, United States
| | - Pierre-Olivier Polack
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, United States
| | - Peyman Golshani
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, United States
- Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los Angeles, Los Angeles, CA 90095, United States
- West Los Angeles VA Medical Center, CA 90073 Los Angeles, United States
| | - Gergő Orbán
- Department of Computational Sciences, Wigner Research Center for Physics, Budapest, 1121, Hungary
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27
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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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28
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Funahashi S, Gao B, Takeda K, Watanabe Y, Wu J, Yan T. Individual prefrontal neurons contribute to sensory-to-motor information transformation by rotating reference frames during spatial working memory performance. Cereb Cortex 2023; 33:10258-10271. [PMID: 37557911 DOI: 10.1093/cercor/bhad280] [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] [Received: 04/14/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 08/11/2023] Open
Abstract
Performing working memory tasks correctly requires not only the temporary maintenance of information but also the visual-to-motor transformation of information. Although sustained delay-period activity is known to be a mechanism for temporarily maintaining information, the mechanism for information transformation is not well known. An analysis using a population of delay-period activities recorded from prefrontal neurons visualized a gradual change of maintained information from sensory to motor as the delay period progressed. However, the contributions of individual prefrontal neurons to this process are not known. In the present study, we used a version of the delayed-response task, in which monkeys needed to make a saccade 90o clockwise from a visual cue after a 3-s delay, and examined the temporal change in the preferred directions of delay-period activity during the delay period for individual neurons. One group of prefrontal neurons encoded the cue direction by a retinotopic reference frame and either maintained it throughout the delay period or rotated it 90o counterclockwise to adjust visual information to saccade information, whereas other groups of neurons encoded the cue direction by a saccade-based reference frame and rotated it 90o clockwise. The results indicate that visual-to-motor information transformation is achieved by manipulating the reference frame to adjust visual coordinates to motor coordinates.
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Affiliation(s)
- Shintaro Funahashi
- Advanced Research Institute for Multidisciplinary Science, Beijing Institute of Technology, Haidian District, Beijing 100018, People's Republic of China
- School of Life Science, Beijing Institute of Technology, Haidian District, Beijing 100018, People's Republic of China
- Department of Cognitive and Behavioral Sciences, Graduate School of Human and Environmental Studies, Kyoto University, Yoshida, Sakyo-ku, Kyoto 606-8501, Japan
- Kokoro Research Center, Kyoto University, Yoshida, Sakyo-ku, Kyoto 606-8501, Japan
| | - Binbin Gao
- School of Life Science, Beijing Institute of Technology, Haidian District, Beijing 100018, People's Republic of China
| | - Kazuyoshi Takeda
- Department of Cognitive and Behavioral Sciences, Graduate School of Human and Environmental Studies, Kyoto University, Yoshida, Sakyo-ku, Kyoto 606-8501, Japan
| | - Yumiko Watanabe
- Department of Cognitive and Behavioral Sciences, Graduate School of Human and Environmental Studies, Kyoto University, Yoshida, Sakyo-ku, Kyoto 606-8501, Japan
| | - Jinglong Wu
- School of Medical Technology, Beijing Institute of Technology, Haidian District, Beijing 100018, People's Republic of China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Haidian District, Beijing 100018, People's Republic of China
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29
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Testard C, Tremblay S, Parodi F, DiTullio RW, Acevedo-Ithier A, Gardiner K, Kording KP, Platt M. Neural signatures of natural behavior in socializing macaques. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.05.547833. [PMID: 37461580 PMCID: PMC10349985 DOI: 10.1101/2023.07.05.547833] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Our understanding of the neurobiology of primate behavior largely derives from artificial tasks in highly-controlled laboratory settings, overlooking most natural behaviors primate brains evolved to produce1. In particular, how primates navigate the multidimensional social relationships that structure daily life and shape survival and reproductive success remains largely unexplored at the single neuron level. Here, we combine ethological analysis with new wireless recording technologies to uncover neural signatures of natural behavior in unrestrained, socially interacting pairs of rhesus macaques within a larger colony. Population decoding of single neuron activity in prefrontal and temporal cortex unveiled robust encoding of 24 species-typical behaviors, which was strongly modulated by the presence and identity of surrounding monkeys. Male-female partners demonstrated near-perfect reciprocity in grooming, a key behavioral mechanism supporting friendships and alliances, and neural activity maintained a running account of these social investments. When confronted with an aggressive intruder, behavioral and neural population responses reflected empathy and were buffered by the presence of a partner. Surprisingly, neural signatures in prefrontal and temporal cortex were largely indistinguishable and irreducible to visual and motor contingencies. By employing an ethological approach to the study of primate neurobiology, we reveal a highly-distributed neurophysiological record of social dynamics, a potential computational foundation supporting communal life in primate societies, including our own.
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30
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Wu S, Huang C, Snyder A, Smith M, Doiron B, Yu B. Automated customization of large-scale spiking network models to neuronal population activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.21.558920. [PMID: 37790533 PMCID: PMC10542160 DOI: 10.1101/2023.09.21.558920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet the dependence of their activity on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models and thereby enable deeper insight into how networks of neurons give rise to brain function.
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Affiliation(s)
- Shenghao Wu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Chengcheng Huang
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Adam Snyder
- Department of Neuroscience, University of Rochester, Rochester, NY, USA
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, USA
- Center for Visual Science, University of Rochester, Rochester, NY, USA
| | - Matthew Smith
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Brent Doiron
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
- Department of Statistics, University of Chicago, Chicago, IL, USA
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
| | - Byron Yu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
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31
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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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32
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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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33
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Gao W, Shen J, Lin Y, Wang K, Lin Z, Tang H, Chen X. Sequential sparse autoencoder for dynamic heading representation in ventral intraparietal area. Comput Biol Med 2023; 163:107114. [PMID: 37329620 DOI: 10.1016/j.compbiomed.2023.107114] [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] [Received: 02/08/2023] [Revised: 05/12/2023] [Accepted: 05/30/2023] [Indexed: 06/19/2023]
Abstract
To navigate in space, it is important to predict headings in real-time from neural responses in the brain to vestibular and visual signals, and the ventral intraparietal area (VIP) is one of the critical brain areas. However, it remains unexplored in the population level how the heading perception is represented in VIP. And there are no commonly used methods suitable for decoding the headings from the population responses in VIP, given the large spatiotemporal dynamics and heterogeneity in the neural responses. Here, responses were recorded from 210 VIP neurons in three rhesus monkeys when they were performing a heading perception task. And by specifically and separately modelling the both dynamics with sparse representation, we built a sequential sparse autoencoder (SSAE) to do the population decoding on the recorded dataset and tried to maximize the decoding performance. The SSAE relies on a three-layer sparse autoencoder to extract temporal and spatial heading features in the dataset via unsupervised learning, and a softmax classifier to decode the headings. Compared with other population decoding methods, the SSAE achieves a leading accuracy of 96.8% ± 2.1%, and shows the advantages of robustness, low storage and computing burden for real-time prediction. Therefore, our SSAE model performs well in learning neurobiologically plausible features comprising dynamic navigational information.
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Affiliation(s)
- Wei Gao
- Department of Neurology and Psychiatry of the Second Affiliated Hospital, College of Biomedical Engineering and Instrument Science, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, 268 Kaixuan Road, Jianggan District, Hangzhou, 310029, China
| | - Jiangrong Shen
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, Xihu District, Hangzhou, 310027, China
| | - Yipeng Lin
- Department of Neurology and Psychiatry of the Second Affiliated Hospital, College of Biomedical Engineering and Instrument Science, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, 268 Kaixuan Road, Jianggan District, Hangzhou, 310029, China
| | - Kejun Wang
- School of Software Technology, Zhejiang University, 38 Zheda Road, Xihu District, Hangzhou, 310027, China
| | - Zheng Lin
- Department of Psychiatry, Second Affiliated Hospital, School of Medicine, Zhejiang University, 88 Jiefang Road, Shangcheng District, Hangzhou, 310009, China
| | - Huajin Tang
- College of Computer Science and Technology, Zhejiang University, 38 Zheda Road, Xihu District, Hangzhou, 310027, China.
| | - Xiaodong Chen
- Department of Neurology and Psychiatry of the Second Affiliated Hospital, College of Biomedical Engineering and Instrument Science, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, 268 Kaixuan Road, Jianggan District, Hangzhou, 310029, China.
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34
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Nocon JC, Witter J, Gritton H, Han X, Houghton C, Sen K. A robust and compact population code for competing sounds in auditory cortex. J Neurophysiol 2023; 130:775-787. [PMID: 37646080 PMCID: PMC10642980 DOI: 10.1152/jn.00148.2023] [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: 04/10/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/01/2023] Open
Abstract
Cortical circuits encoding sensory information consist of populations of neurons, yet how information aggregates via pooling individual cells remains poorly understood. Such pooling may be particularly important in noisy settings where single-neuron encoding is degraded. One example is the cocktail party problem, with competing sounds from multiple spatial locations. How populations of neurons in auditory cortex code competing sounds have not been previously investigated. Here, we apply a novel information-theoretic approach to estimate information in populations of neurons in mouse auditory cortex about competing sounds from multiple spatial locations, including both summed population (SP) and labeled line (LL) codes. We find that a small subset of neurons is sufficient to nearly maximize mutual information over different spatial configurations, with the labeled line code outperforming the summed population code and approaching information levels attained in the absence of competing stimuli. Finally, information in the labeled line code increases with spatial separation between target and masker, in correspondence with behavioral results on spatial release from masking in humans and animals. Taken together, our results reveal that a compact population of neurons in auditory cortex provides a robust code for competing sounds from different spatial locations.NEW & NOTEWORTHY Little is known about how populations of neurons within cortical circuits encode sensory stimuli in the presence of competing stimuli at other spatial locations. Here, we investigate this problem in auditory cortex using a recently proposed information-theoretic approach. We find a small subset of neurons nearly maximizes information about target sounds in the presence of competing maskers, approaching information levels for isolated stimuli, and provides a noise-robust code for sounds in a complex auditory scene.
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Affiliation(s)
- Jian Carlo Nocon
- Neurophotonics Center, Boston University, Boston, Massachusetts, United States
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States
- Hearing Research Center, Boston University, Boston, Massachusetts, United States
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States
| | - Jake Witter
- Department of Computer Science, University of Bristol, Bristol, United Kingdom
| | - Howard Gritton
- Department of Comparative Biosciences, University of Illinois, Urbana, Illinois, United States
- Department of Bioengineering, University of Illinois, Urbana, Illinois, United States
| | - Xue Han
- Neurophotonics Center, Boston University, Boston, Massachusetts, United States
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States
- Hearing Research Center, Boston University, Boston, Massachusetts, United States
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States
| | - Conor Houghton
- Department of Computer Science, University of Bristol, Bristol, United Kingdom
| | - Kamal Sen
- Neurophotonics Center, Boston University, Boston, Massachusetts, United States
- Center for Systems Neuroscience, Boston University, Boston, Massachusetts, United States
- Hearing Research Center, Boston University, Boston, Massachusetts, United States
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States
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35
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Jonikaitis D, Zhu S. Action space restructures visual working memory in prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.13.553135. [PMID: 37645942 PMCID: PMC10462047 DOI: 10.1101/2023.08.13.553135] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Visual working memory enables flexible behavior by decoupling sensory stimuli from behavioral actions. While previous studies have predominantly focused on the storage component of working memory, the role of future actions in shaping working memory remains unknown. To answer this question, we used two working memory tasks that allowed the dissociation of sensory and action components of working memory. We measured behavioral performance and neuronal activity in the macaque prefrontal cortex area, frontal eye fields. We show that the action space reshapes working memory, as evidenced by distinct patterns of memory tuning and attentional orienting between the two tasks. Notably, neuronal activity during the working memory period predicted future behavior and exhibited mixed selectivity in relation to the sensory space but linear selectivity relative to the action space. This linear selectivity was achieved through the rapid transformation from sensory to action space and was subsequently maintained as a stable cross-temporal population activity pattern. Combined, we provide direct physiological evidence of the action-oriented nature of frontal eye field neurons during memory tasks and demonstrate that the anticipation of behavioral outcomes plays a significant role in transforming and maintaining the contents of visual working memory.
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36
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Daie K, Fontolan L, Druckmann S, Svoboda K. Feedforward amplification in recurrent networks underlies paradoxical neural coding. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.04.552026. [PMID: 37577599 PMCID: PMC10418196 DOI: 10.1101/2023.08.04.552026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
The activity of single neurons encodes behavioral variables, such as sensory stimuli (Hubel & Wiesel 1959) and behavioral choice (Britten et al. 1992; Guo et al. 2014), but their influence on behavior is often mysterious. We estimated the influence of a unit of neural activity on behavioral choice from recordings in anterior lateral motor cortex (ALM) in mice performing a memory-guided movement task (H. K. Inagaki et al. 2018). Choice selectivity grew as it flowed through a sequence of directions in activity space. Early directions carried little selectivity but were predicted to have a large behavioral influence, while late directions carried large selectivity and little behavioral influence. Consequently, estimated behavioral influence was only weakly correlated with choice selectivity; a large proportion of neurons selective for one choice were predicted to influence choice in the opposite direction. These results were consistent with models in which recurrent circuits produce feedforward amplification (Goldman 2009; Ganguli et al. 2008; Murphy & Miller 2009) so that small amplitude signals along early directions are amplified to produce low-dimensional choice selectivity along the late directions, and behavior. Targeted photostimulation experiments (Daie et al. 2021b) revealed that activity along the early directions triggered sequential activity along the later directions and caused predictable behavioral biases. These results demonstrate the existence of an amplifying feedforward dynamical motif in the motor cortex, explain paradoxical responses to perturbation experiments (Chettih & Harvey 2019; Daie et al. 2021b; Russell et al. 2019), and reveal behavioral relevance of small amplitude neural dynamics.
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37
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Rouzitalab A, Boulay CB, Park J, Sachs AJ. Intracortical brain-computer interfaces in primates: a review and outlook. Biomed Eng Lett 2023; 13:375-390. [PMID: 37519868 PMCID: PMC10382423 DOI: 10.1007/s13534-023-00286-8] [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: 12/30/2022] [Revised: 05/04/2023] [Accepted: 05/14/2023] [Indexed: 08/01/2023] Open
Abstract
Brain-computer interfaces (BCI) translate brain signals into artificial output to restore or replace natural central nervous system (CNS) functions. Multiple processes, including sensorimotor integration, decision-making, motor planning, execution, and updating, are involved in any movement. For example, a BCI may be better able to restore naturalistic motor behaviors if it uses signals from multiple brain areas and decodes natural behaviors' cognitive and motor aspects. This review provides an overview of the preliminary information necessary to plan a BCI project focusing on intracortical implants in primates. Since the brain structure and areas of non-human primates (NHP) are similar to humans, exploring the result of NHP studies will eventually benefit human BCI studies. The different types of BCI systems based on the target cortical area, types of signals, and decoding methods will be discussed. In addition, various successful state-of-the-art cases will be reviewed in more detail, focusing on the general algorithm followed in the real-time system. Finally, an outlook for improving the current BCI research studies will be debated.
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Affiliation(s)
- Alireza Rouzitalab
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5 Canada
- The Ottawa Hospital Research Institute, Ottawa, ON Canada
| | | | - Jeongwon Park
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5 Canada
- Department of Electrical and Biomedical Engineering, University of Nevada, Reno, NV 89557 USA
| | - Adam J. Sachs
- The Ottawa Hospital Research Institute, Ottawa, ON Canada
- The University of Ottawa Brain and Mind Research Institute, Ottawa, ON Canada
- Division of Neurosurgery, Department of Surgery, The Ottawa Hospital, Ottawa, ON Canada
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38
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Genkin M, Shenoy KV, Chandrasekaran C, Engel TA. The dynamics and geometry of choice in premotor cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.22.550183. [PMID: 37546748 PMCID: PMC10401920 DOI: 10.1101/2023.07.22.550183] [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
The brain represents sensory variables in the coordinated activity of neural populations, in which tuning curves of single neurons define the geometry of the population code. Whether the same coding principle holds for dynamic cognitive variables remains unknown because internal cognitive processes unfold with a unique time course on single trials observed only in the irregular spiking of heterogeneous neural populations. Here we show the existence of such a population code for the dynamics of choice formation in the primate premotor cortex. We developed an approach to simultaneously infer population dynamics and tuning functions of single neurons to the population state. Applied to spike data recorded during decision-making, our model revealed that populations of neurons encoded the same dynamic variable predicting choices, and heterogeneous firing rates resulted from the diverse tuning of single neurons to this decision variable. The inferred dynamics indicated an attractor mechanism for decision computation. Our results reveal a common geometric principle for neural encoding of sensory and dynamic cognitive variables.
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Affiliation(s)
| | - Krishna V Shenoy
- Howard Hughes Medical Institute, Stanford University, Stanford, CA
- Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Chandramouli Chandrasekaran
- Department of Anatomy & Neurobiology, Boston University, Boston, MA
- Department of Psychological and Brain Sciences, Boston University, Boston, MA
- Center for Systems Neuroscience, Boston University, Boston, MA
| | - Tatiana A Engel
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
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39
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Zhou S, Seay M, Taxidis J, Golshani P, Buonomano DV. Multiplexing working memory and time in the trajectories of neural networks. Nat Hum Behav 2023; 7:1170-1184. [PMID: 37081099 PMCID: PMC10913811 DOI: 10.1038/s41562-023-01592-y] [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: 11/08/2022] [Accepted: 03/22/2023] [Indexed: 04/22/2023]
Abstract
Working memory (WM) and timing are generally considered distinct cognitive functions, but similar neural signatures have been implicated in both. To explore the hypothesis that WM and timing may rely on shared neural mechanisms, we used psychophysical tasks that contained either task-irrelevant timing or WM components. In both cases, the task-irrelevant component influenced performance. We then developed recurrent neural network (RNN) simulations that revealed that cue-specific neural sequences, which multiplexed WM and time, emerged as the dominant regime that captured the behavioural findings. During training, RNN dynamics transitioned from low-dimensional ramps to high-dimensional neural sequences, and depending on task requirements, steady-state or ramping activity was also observed. Analysis of RNN structure revealed that neural sequences relied primarily on inhibitory connections, and could survive the deletion of all excitatory-to-excitatory connections. Our results indicate that in some instances WM is encoded in time-varying neural activity because of the importance of predicting when WM will be used.
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Affiliation(s)
- Shanglin Zhou
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Michael Seay
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - Jiannis Taxidis
- Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Physiology, University of Toronto, Toronto, Ontario, Canada
| | - Peyman Golshani
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Integrative Center for Learning and Memory, Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, USA
- UCLA Semel Institute for Neuroscience and Behavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
- West Los Angeles VA Medical Center, Los Angeles, CA, USA
| | - Dean V Buonomano
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
- Department of Psychology, University of California, Los Angeles, CA, USA.
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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40
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Kikumoto A, Bhandari A, Shibata K, Badre D. A Transient High-dimensional Geometry Affords Stable Conjunctive Subspaces for Efficient Action Selection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.09.544428. [PMID: 37333209 PMCID: PMC10274903 DOI: 10.1101/2023.06.09.544428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Flexible action selection requires cognitive control mechanisms capable of mapping the same inputs to diverse output actions depending on goals and contexts. How the brain encodes information to enable this capacity remains one of the longstanding and fundamental problems in cognitive neuroscience. From a neural state-space perspective, solving this problem requires a control representation that can disambiguate similar input neural states, making task-critical dimensions separable depending on the context. Moreover, for action selection to be robust and time-invariant, control representations must be stable in time, thereby enabling efficient readout by downstream processing units. Thus, an ideal control representation should leverage geometry and dynamics that maximize the separability and stability of neural trajectories for task computations. Here, using novel EEG decoding methods, we investigated how the geometry and dynamics of control representations constrain flexible action selection in the human brain. Specifically, we tested the hypothesis that encoding a temporally stable conjunctive subspace that integrates stimulus, response, and context (i.e., rule) information in a high-dimensional geometry achieves the separability and stability needed for context-dependent action selection. Human participants performed a task that requires context-dependent action selection based on pre-instructed rules. Participants were cued to respond immediately at varying intervals following stimulus presentation, which forced responses at different states in neural trajectories. We discovered that in the moments before successful responses, there was a transient expansion of representational dimensionality that separated conjunctive subspaces. Further, we found that the dynamics stabilized in the same time window, and that the timing of entry into this stable and high-dimensional state predicted the quality of response selection on individual trials. These results establish the neural geometry and dynamics the human brain needs for flexible control over behavior.
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Affiliation(s)
- Atsushi Kikumoto
- Department of Cognitive, Linguistic, and Psychological Sciences
- RIKEN Center for Brain Science, Wako, Saitama, Japan
| | | | | | - David Badre
- Department of Cognitive, Linguistic, and Psychological Sciences
- Carney Institute for Brain Science Brown University, Providence, Rhode island, U.S
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41
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Liu Y, Li A, Bair-Marshall C, Xu H, Jee HJ, Zhu E, Sun M, Zhang Q, Lefevre A, Chen ZS, Grinevich V, Froemke RC, Wang J. Oxytocin promotes prefrontal population activity via the PVN-PFC pathway to regulate pain. Neuron 2023; 111:1795-1811.e7. [PMID: 37023755 PMCID: PMC10272109 DOI: 10.1016/j.neuron.2023.03.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 09/02/2022] [Accepted: 03/08/2023] [Indexed: 04/08/2023]
Abstract
Neurons in the prefrontal cortex (PFC) can provide top-down regulation of sensory-affective experiences such as pain. Bottom-up modulation of sensory coding in the PFC, however, remains poorly understood. Here, we examined how oxytocin (OT) signaling from the hypothalamus regulates nociceptive coding in the PFC. In vivo time-lapse endoscopic calcium imaging in freely behaving rats showed that OT selectively enhanced population activity in the prelimbic PFC in response to nociceptive inputs. This population response resulted from the reduction of evoked GABAergic inhibition and manifested as elevated functional connectivity involving pain-responsive neurons. Direct inputs from OT-releasing neurons in the paraventricular nucleus (PVN) of the hypothalamus are crucial to maintaining this prefrontal nociceptive response. Activation of the prelimbic PFC by OT or direct optogenetic stimulation of oxytocinergic PVN projections reduced acute and chronic pain. These results suggest that oxytocinergic signaling in the PVN-PFC circuit constitutes a key mechanism to regulate cortical sensory processing.
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Affiliation(s)
- Yaling Liu
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Anna Li
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University Grossman School of Medicine, New York, NY, USA; Interdisciplinary Pain Research Program, New York University Langone Health, New York, NY, USA
| | - Chloe Bair-Marshall
- Skirball Institute for Biomolecular Medicine, New York University Grossman School of Medicine, New York, NY, USA; Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, USA; Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA; Department of Otolaryngology, New York University Grossman School of Medicine, New York, NY, USA
| | - Helen Xu
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University Grossman School of Medicine, New York, NY, USA; Interdisciplinary Pain Research Program, New York University Langone Health, New York, NY, USA
| | - Hyun Jung Jee
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University Grossman School of Medicine, New York, NY, USA; Interdisciplinary Pain Research Program, New York University Langone Health, New York, NY, USA
| | - Elaine Zhu
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University Grossman School of Medicine, New York, NY, USA; Interdisciplinary Pain Research Program, New York University Langone Health, New York, NY, USA
| | - Mengqi Sun
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Qiaosheng Zhang
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University Grossman School of Medicine, New York, NY, USA; Interdisciplinary Pain Research Program, New York University Langone Health, New York, NY, USA
| | - Arthur Lefevre
- Department of Neuropeptide Research in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Zhe Sage Chen
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, USA; Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA; Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Valery Grinevich
- Department of Neuropeptide Research in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Robert C Froemke
- Skirball Institute for Biomolecular Medicine, New York University Grossman School of Medicine, New York, NY, USA; Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, USA; Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA; Department of Otolaryngology, New York University Grossman School of Medicine, New York, NY, USA
| | - Jing Wang
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University Grossman School of Medicine, New York, NY, USA; Interdisciplinary Pain Research Program, New York University Langone Health, New York, NY, USA; Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY, USA; Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA.
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42
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Wang XJ, Jiang J, Pereira-Obilinovic U. Bifurcation in space: Emergence of function modularity in the neocortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.04.543639. [PMID: 37333347 PMCID: PMC10274618 DOI: 10.1101/2023.06.04.543639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
How does functional modularity emerge in a multiregional cortex made with repeats of a canonical local circuit architecture? We investigated this question by focusing on neural coding of working memory, a core cognitive function. Here we report a mechanism dubbed "bifurcation in space", and show that its salient signature is spatially localized "critical slowing down" leading to an inverted V-shaped profile of neuronal time constants along the cortical hierarchy during working memory. The phenomenon is confirmed in connectome-based large-scale models of mouse and monkey cortices, offering an experimentally testable prediction to assess whether working memory representation is modular. Many bifurcations in space could explain the emergence of different activity patterns potentially deployed for distinct cognitive functions, This work demonstrates that a distributed mental representation is compatible with functional specificity as a consequence of macroscopic gradients of neurobiological properties across the cortex, suggesting a general principle for understanding brain's modular organization.
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Affiliation(s)
- Xiao-Jing Wang
- Center for Neural Science, New York University, 4 Washington Place, New York 10003, USA
| | - Junjie Jiang
- Center for Neural Science, New York University, 4 Washington Place, New York 10003, USA
- Present address: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Research Center for Brain-inspired Intelligence, Xi’an Jiaotong University, No.28, West Xianning Road, Xi’an, 710049, Shaanxi, P. R. China
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43
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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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44
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Domanski APF, Kucewicz MT, Russo E, Tricklebank MD, Robinson ESJ, Durstewitz D, Jones MW. Distinct hippocampal-prefrontal neural assemblies coordinate memory encoding, maintenance, and recall. Curr Biol 2023; 33:1220-1236.e4. [PMID: 36898372 PMCID: PMC10728550 DOI: 10.1016/j.cub.2023.02.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 01/05/2023] [Accepted: 02/08/2023] [Indexed: 03/11/2023]
Abstract
Short-term memory enables incorporation of recent experience into subsequent decision-making. This processing recruits both the prefrontal cortex and hippocampus, where neurons encode task cues, rules, and outcomes. However, precisely which information is carried when, and by which neurons, remains unclear. Using population decoding of activity in rat medial prefrontal cortex (mPFC) and dorsal hippocampal CA1, we confirm that mPFC populations lead in maintaining sample information across delays of an operant non-match to sample task, despite individual neurons firing only transiently. During sample encoding, distinct mPFC subpopulations joined distributed CA1-mPFC cell assemblies hallmarked by 4-5 Hz rhythmic modulation; CA1-mPFC assemblies re-emerged during choice episodes but were not 4-5 Hz modulated. Delay-dependent errors arose when attenuated rhythmic assembly activity heralded collapse of sustained mPFC encoding. Our results map component processes of memory-guided decisions onto heterogeneous CA1-mPFC subpopulations and the dynamics of physiologically distinct, distributed cell assemblies.
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Affiliation(s)
- Aleksander P F Domanski
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK; The Alan Turing Institute, British Library, 96 Euston Rd, London, UK; The Francis Crick Institute, 1 Midland Road, London, UK
| | - Michal T Kucewicz
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK; BioTechMed Center, Brain & Mind Electrophysiology Laboratory, Multimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233 Gdansk, Poland.
| | - Eleonora Russo
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany; Department of Psychiatry and Psychotherapy, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany
| | - Mark D Tricklebank
- Centre for Neuroimaging Science, King's College London, Denmark Hill, London SE5 8AF, UK
| | - Emma S J Robinson
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159 Mannheim, Germany
| | - Matt W Jones
- School of Physiology, Pharmacology & Neuroscience, Faculty of Life Sciences, University of Bristol, University Walk, Bristol BS8 1TD, UK
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45
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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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46
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Ahveninen J, Uluç I, Raij T, Nummenmaa A, Mamashli F. Spectrotemporal content of human auditory working memory represented in functional connectivity patterns. Commun Biol 2023; 6:294. [PMID: 36941477 PMCID: PMC10027691 DOI: 10.1038/s42003-023-04675-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 03/07/2023] [Indexed: 03/23/2023] Open
Abstract
Recent research suggests that working memory (WM), the mental sketchpad underlying thinking and communication, is maintained by multiple regions throughout the brain. Whether parts of a stable WM representation could be distributed across these brain regions is, however, an open question. We addressed this question by examining the content-specificity of connectivity-pattern matrices between subparts of cortical regions-of-interest (ROI). These connectivity patterns were calculated from functional MRI obtained during a ripple-sound auditory WM task. Statistical significance was assessed by comparing the decoding results to a null distribution derived from a permutation test considering all comparable two- to four-ROI connectivity patterns. Maintained WM items could be decoded from connectivity patterns across ROIs in frontal, parietal, and superior temporal cortices. All functional connectivity patterns that were specific to maintained sound content extended from early auditory to frontoparietal cortices. Our results demonstrate that WM maintenance is supported by content-specific patterns of functional connectivity across different levels of cortical hierarchy.
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Affiliation(s)
- Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
- Department of Radiology, Harvard Medical School, Boston, MA, USA.
| | - Işıl Uluç
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Fahimeh Mamashli
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
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47
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Pugavko MM, Maslennikov OV, Nekorkin VI. Multitask computation through dynamics in recurrent spiking neural networks. Sci Rep 2023; 13:3997. [PMID: 36899052 PMCID: PMC10006454 DOI: 10.1038/s41598-023-31110-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 03/06/2023] [Indexed: 03/12/2023] Open
Abstract
In this work, inspired by cognitive neuroscience experiments, we propose recurrent spiking neural networks trained to perform multiple target tasks. These models are designed by considering neurocognitive activity as computational processes through dynamics. Trained by input-output examples, these spiking neural networks are reverse engineered to find the dynamic mechanisms that are fundamental to their performance. We show that considering multitasking and spiking within one system provides insightful ideas on the principles of neural computation.
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Affiliation(s)
- Mechislav M Pugavko
- Institute of Applied Physics of the Russian Academy of Sciences, Nizhny Novgorod, 603950, Russia
| | - Oleg V Maslennikov
- Institute of Applied Physics of the Russian Academy of Sciences, Nizhny Novgorod, 603950, Russia.
| | - Vladimir I Nekorkin
- Institute of Applied Physics of the Russian Academy of Sciences, Nizhny Novgorod, 603950, Russia
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48
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Howard MW, Esfahani ZG, Le B, Sederberg PB. Foundations of a temporal RL. ARXIV 2023:arXiv:2302.10163v1. [PMID: 36866224 PMCID: PMC9980275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
Abstract
Recent advances in neuroscience and psychology show that the brain has access to timelines of both the past and the future. Spiking across populations of neurons in many regions of the mammalian brain maintains a robust temporal memory, a neural timeline of the recent past. Behavioral results demonstrate that people can estimate an extended temporal model of the future, suggesting that the neural timeline of the past could extend through the present into the future. This paper presents a mathematical framework for learning and expressing relationships between events in continuous time. We assume that the brain has access to a temporal memory in the form of the real Laplace transform of the recent past. Hebbian associations with a diversity of synaptic time scales are formed between the past and the present that record the temporal relationships between events. Knowing the temporal relationships between the past and the present allows one to predict relationships between the present and the future, thus constructing an extended temporal prediction for the future. Both memory for the past and the predicted future are represented as the real Laplace transform, expressed as the firing rate over populations of neurons indexed by different rate constants s. The diversity of synaptic timescales allows for a temporal record over the much larger time scale of trial history. In this framework, temporal credit assignment can be assessed via a Laplace temporal difference. The Laplace temporal difference compares the future that actually follows a stimulus to the future predicted just before the stimulus was observed. This computational framework makes a number of specific neurophysiological predictions and, taken together, could provide the basis for a future iteration of RL that incorporates temporal memory as a fundamental building block.
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49
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Abrishamdar M, Farbood Y, Sarkaki A, Rashno M, Badavi M. Evaluation of betulinic acid effects on pain, memory, anxiety, catalepsy, and oxidative stress in animal model of Parkinson's disease. Metab Brain Dis 2023; 38:467-482. [PMID: 35708868 DOI: 10.1007/s11011-022-00962-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 03/11/2022] [Indexed: 01/25/2023]
Abstract
Parkinson's disease (PD) is known for motor impairments. Betulinic acid (BA) is a natural compound with antioxidant activity. The present study addresses the question of whether BA affects motor and non-motor dysfunctions and molecular changes in the rat model of PD. The right medial forebrain bundle was lesioned by injection of 6-hydroxydopamine in Male Wistar rats (10-12 weeks old, 270-320 g). Animals were divided into Sham, PD, 3 treated groups with BA (0.5, 5, and 10 mg/kg, IP), and a positive control group received L-dopa (20 mg/kg, P.O) for 7 days. rigidity, anxiety, analgesia, and memory were assessed by bar test, open-field, elevated plus-maze (EPM), tail-flick, and shuttle box. Additionally, the malondialdehyde (MDA), Superoxide dismutase (SOD), glutathione peroxidase (GPx) activity, Brain-derived neurotrophic factor (BDNF) and Interleukin 10 (IL10) levels in the whole brain were measured. BA significantly reversed the 6-hydroxydopamine-induced motor and memory complication in the bar test and shuttle box. It modified anxiety-like behavior neither in open-field nor in EPM. It only decreased the time spent in open arms. Moreover, no significant changes were found in the tail-flick between treatment and sham groups. On the other hand, the level of MDA & IL10 were decreased, while the activity of GPx levels of SOD & BDNF in the rats' brains was increased. Our results showed that BA as a free radical scavenger can account for a possible promise as a good therapeutic agent for motor and non-motor complications in PD however further studies may be needed.
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Affiliation(s)
- M Abrishamdar
- Persian Gulf Physiology Research Center, Medical Basic Sciences Research Institute, Department of Physiology, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Yaghoob Farbood
- Persian Gulf Physiology Research Center, Medical Basic Sciences Research Institute, Department of Physiology, Medicine Faculty, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
- Department of Physiology, Medicine Faculty and Physiology Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
| | - A Sarkaki
- Persian Gulf Physiology Research Center, Medical Basic Sciences Research Institute, Department of Physiology, Medicine Faculty, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - M Rashno
- Department of Immunulogy, Cellular and Molecular Research Center, Medicine Faculty, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - M Badavi
- Persian Gulf Physiology Research Center, Medical Basic Sciences Research Institute, Department of Physiology, Medicine Faculty, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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50
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Koh TH, Bishop WE, Kawashima T, Jeon BB, Srinivasan R, Mu Y, Wei Z, Kuhlman SJ, Ahrens MB, Chase SM, Yu BM. Dimensionality reduction of calcium-imaged neuronal population activity. NATURE COMPUTATIONAL SCIENCE 2023; 3:71-85. [PMID: 37476302 PMCID: PMC10358781 DOI: 10.1038/s43588-022-00390-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 12/05/2022] [Indexed: 07/22/2023]
Abstract
Calcium imaging has been widely adopted for its ability to record from large neuronal populations. To summarize the time course of neural activity, dimensionality reduction methods, which have been applied extensively to population spiking activity, may be particularly useful. However, it is unclear if the dimensionality reduction methods applied to spiking activity are appropriate for calcium imaging. We thus carried out a systematic study of design choices based on standard dimensionality reduction methods. We also developed a method to perform deconvolution and dimensionality reduction simultaneously (Calcium Imaging Linear Dynamical System, CILDS). CILDS most accurately recovered the single-trial, low-dimensional time courses from simulated calcium imaging data. CILDS also outperformed the other methods on calcium imaging recordings from larval zebrafish and mice. More broadly, this study represents a foundation for summarizing calcium imaging recordings of large neuronal populations using dimensionality reduction in diverse experimental settings.
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Affiliation(s)
- Tze Hui Koh
- Department of Biomedical Engineering, Carnegie Mellon University, PA
- Center for the Neural Basis of Cognition, PA
| | - William E. Bishop
- Center for the Neural Basis of Cognition, PA
- Department of Machine Learning, Carnegie Mellon University, PA
- Janelia Research Campus, Howard Hughes Medical Institute, VA
| | - Takashi Kawashima
- Janelia Research Campus, Howard Hughes Medical Institute, VA
- Department of Brain Sciences, Weizmann Institute of Science, Israel
| | - Brian B. Jeon
- Department of Biomedical Engineering, Carnegie Mellon University, PA
- Center for the Neural Basis of Cognition, PA
| | - Ranjani Srinivasan
- Department of Biomedical Engineering, Carnegie Mellon University, PA
- Department of Electrical and Computer Engineering, Johns Hopkins University, MD
| | - Yu Mu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, China
| | - Ziqiang Wei
- Janelia Research Campus, Howard Hughes Medical Institute, VA
| | - Sandra J. Kuhlman
- Carnegie Mellon Neuroscience Institute, Carnegie Mellon University, PA
- Department of Biological Sciences, Carnegie Mellon University, PA
| | - Misha B. Ahrens
- Janelia Research Campus, Howard Hughes Medical Institute, VA
| | - Steven M. Chase
- Department of Biomedical Engineering, Carnegie Mellon University, PA
- Carnegie Mellon Neuroscience Institute, Carnegie Mellon University, PA
| | - Byron M. Yu
- Department of Biomedical Engineering, Carnegie Mellon University, PA
- Carnegie Mellon Neuroscience Institute, Carnegie Mellon University, PA
- Department of Electrical and Computer Engineering, Carnegie Mellon University, PA
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