1
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Rançon U, Masquelier T, Cottereau BR. A general model unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses. PLoS Comput Biol 2024; 20:e1012288. [PMID: 39093852 DOI: 10.1371/journal.pcbi.1012288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 06/29/2024] [Indexed: 08/04/2024] Open
Abstract
Sounds are temporal stimuli decomposed into numerous elementary components by the auditory nervous system. For instance, a temporal to spectro-temporal transformation modelling the frequency decomposition performed by the cochlea is a widely adopted first processing step in today's computational models of auditory neural responses. Similarly, increments and decrements in sound intensity (i.e., of the raw waveform itself or of its spectral bands) constitute critical features of the neural code, with high behavioural significance. However, despite the growing attention of the scientific community on auditory OFF responses, their relationship with transient ON, sustained responses and adaptation remains unclear. In this context, we propose a new general model, based on a pair of linear filters, named AdapTrans, that captures both sustained and transient ON and OFF responses into a unifying and easy to expand framework. We demonstrate that filtering audio cochleagrams with AdapTrans permits to accurately render known properties of neural responses measured in different mammal species such as the dependence of OFF responses on the stimulus fall time and on the preceding sound duration. Furthermore, by integrating our framework into gold standard and state-of-the-art machine learning models that predict neural responses from audio stimuli, following a supervised training on a large compilation of electrophysiology datasets (ready-to-deploy PyTorch models and pre-processed datasets shared publicly), we show that AdapTrans systematically improves the prediction accuracy of estimated responses within different cortical areas of the rat and ferret auditory brain. Together, these results motivate the use of our framework for computational and systems neuroscientists willing to increase the plausibility and performances of their models of audition.
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Affiliation(s)
- Ulysse Rançon
- CerCo UMR 5549, CNRS - Université Toulouse III, Toulouse, France
| | | | - Benoit R Cottereau
- CerCo UMR 5549, CNRS - Université Toulouse III, Toulouse, France
- IPAL, CNRS IRL62955, Singapore, Singapore
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2
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Kuzmina E, Kriukov D, Lebedev M. Neuronal travelling waves explain rotational dynamics in experimental datasets and modelling. Sci Rep 2024; 14:3566. [PMID: 38347042 PMCID: PMC10861525 DOI: 10.1038/s41598-024-53907-2] [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: 11/25/2023] [Accepted: 02/06/2024] [Indexed: 02/15/2024] Open
Abstract
Spatiotemporal properties of neuronal population activity in cortical motor areas have been subjects of experimental and theoretical investigations, generating numerous interpretations regarding mechanisms for preparing and executing limb movements. Two competing models, representational and dynamical, strive to explain the relationship between movement parameters and neuronal activity. A dynamical model uses the jPCA method that holistically characterizes oscillatory activity in neuron populations by maximizing the data rotational dynamics. Different rotational dynamics interpretations revealed by the jPCA approach have been proposed. Yet, the nature of such dynamics remains poorly understood. We comprehensively analyzed several neuronal-population datasets and found rotational dynamics consistently accounted for by a traveling wave pattern. For quantifying rotation strength, we developed a complex-valued measure, the gyration number. Additionally, we identified parameters influencing rotation extent in the data. Our findings suggest that rotational dynamics and traveling waves are typically the same phenomena, so reevaluation of the previous interpretations where they were considered separate entities is needed.
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Affiliation(s)
- Ekaterina Kuzmina
- Skolkovo Institute of Science and Technology, Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Moscow, Russia, 121205.
- Artificial Intelligence Research Institute (AIRI), Moscow, Russia.
| | - Dmitrii Kriukov
- Artificial Intelligence Research Institute (AIRI), Moscow, Russia
- Skolkovo Institute of Science and Technology, Center for Molecular and Cellular Biology, Moscow, Russia, 121205
| | - Mikhail Lebedev
- Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, Moscow, Russia, 119992
- Sechenov Institute of Evolutionary Physiology and Biochemistry of the Russian Academy of Sciences, Saint-Petersburg, Russia, 194223
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3
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Abbaspourazad H, Erturk E, Pesaran B, Shanechi MM. Dynamical flexible inference of nonlinear latent factors and structures in neural population activity. Nat Biomed Eng 2024; 8:85-108. [PMID: 38082181 DOI: 10.1038/s41551-023-01106-1] [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: 04/21/2022] [Accepted: 09/12/2023] [Indexed: 12/26/2023]
Abstract
Modelling the spatiotemporal dynamics in the activity of neural populations while also enabling their flexible inference is hindered by the complexity and noisiness of neural observations. Here we show that the lower-dimensional nonlinear latent factors and latent structures can be computationally modelled in a manner that allows for flexible inference causally, non-causally and in the presence of missing neural observations. To enable flexible inference, we developed a neural network that separates the model into jointly trained manifold and dynamic latent factors such that nonlinearity is captured through the manifold factors and the dynamics can be modelled in tractable linear form on this nonlinear manifold. We show that the model, which we named 'DFINE' (for 'dynamical flexible inference for nonlinear embeddings') achieves flexible inference in simulations of nonlinear dynamics and across neural datasets representing a diversity of brain regions and behaviours. Compared with earlier neural-network models, DFINE enables flexible inference, better predicts neural activity and behaviour, and better captures the latent neural manifold structure. DFINE may advance the development of neurotechnology and investigations in neuroscience.
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Affiliation(s)
- Hamidreza Abbaspourazad
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Eray Erturk
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Bijan Pesaran
- Departments of Neurosurgery, Neuroscience, and Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Maryam M Shanechi
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
- Thomas Lord Department of Computer Science, Alfred E. Mann Department of Biomedical Engineering, Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA.
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4
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Barbosa J, Proville R, Rodgers CC, DeWeese MR, Ostojic S, Boubenec Y. Early selection of task-relevant features through population gating. Nat Commun 2023; 14:6837. [PMID: 37884507 PMCID: PMC10603060 DOI: 10.1038/s41467-023-42519-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
Brains can gracefully weed out irrelevant stimuli to guide behavior. This feat is believed to rely on a progressive selection of task-relevant stimuli across the cortical hierarchy, but the specific across-area interactions enabling stimulus selection are still unclear. Here, we propose that population gating, occurring within primary auditory cortex (A1) but controlled by top-down inputs from prelimbic region of medial prefrontal cortex (mPFC), can support across-area stimulus selection. Examining single-unit activity recorded while rats performed an auditory context-dependent task, we found that A1 encoded relevant and irrelevant stimuli along a common dimension of its neural space. Yet, the relevant stimulus encoding was enhanced along an extra dimension. In turn, mPFC encoded only the stimulus relevant to the ongoing context. To identify candidate mechanisms for stimulus selection within A1, we reverse-engineered low-rank RNNs trained on a similar task. Our analyses predicted that two context-modulated neural populations gated their preferred stimulus in opposite contexts, which we confirmed in further analyses of A1. Finally, we show in a two-region RNN how population gating within A1 could be controlled by top-down inputs from PFC, enabling flexible across-area communication despite fixed inter-areal connectivity.
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Affiliation(s)
- Joao Barbosa
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure - PSL Research University, 75005, Paris, France.
| | - Rémi Proville
- Tailored Data Solutions, 192 Cours Gambetta, 84300, Cavaillon, France
| | - Chris C Rodgers
- Department of Neurosurgery, Emory University, Atlanta, GA, 30033, USA
| | - Michael R DeWeese
- Department of Physics, Helen Wills Neuroscience Institute, and Redwood Center for Theoretical Neuroscience, University of California, Berkeley, CA, USA
| | - Srdjan Ostojic
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure - PSL Research University, 75005, Paris, France
| | - Yves Boubenec
- Laboratoire des Systèmes Perceptifs, Département d'Études Cognitives, École Normale Supérieure PSL Research University, CNRS, Paris, France
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5
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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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6
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Abbaspourazad H, Erturk E, Pesaran B, Shanechi MM. Dynamical flexible inference of nonlinear latent structures in neural population activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.13.532479. [PMID: 36993605 PMCID: PMC10054986 DOI: 10.1101/2023.03.13.532479] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Inferring complex spatiotemporal dynamics in neural population activity is critical for investigating neural mechanisms and developing neurotechnology. These activity patterns are noisy observations of lower-dimensional latent factors and their nonlinear dynamical structure. A major unaddressed challenge is to model this nonlinear structure, but in a manner that allows for flexible inference, whether causally, non-causally, or in the presence of missing neural observations. We address this challenge by developing DFINE, a new neural network that separates the model into dynamic and manifold latent factors, such that the dynamics can be modeled in tractable form. We show that DFINE achieves flexible nonlinear inference across diverse behaviors and brain regions. Further, despite enabling flexible inference unlike prior neural network models of population activity, DFINE also better predicts the behavior and neural activity, and better captures the latent neural manifold structure. DFINE can both enhance future neurotechnology and facilitate investigations across diverse domains of neuroscience.
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7
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Sadagopan S, Kar M, Parida S. Quantitative models of auditory cortical processing. Hear Res 2023; 429:108697. [PMID: 36696724 PMCID: PMC9928778 DOI: 10.1016/j.heares.2023.108697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/17/2022] [Accepted: 01/12/2023] [Indexed: 01/15/2023]
Abstract
To generate insight from experimental data, it is critical to understand the inter-relationships between individual data points and place them in context within a structured framework. Quantitative modeling can provide the scaffolding for such an endeavor. Our main objective in this review is to provide a primer on the range of quantitative tools available to experimental auditory neuroscientists. Quantitative modeling is advantageous because it can provide a compact summary of observed data, make underlying assumptions explicit, and generate predictions for future experiments. Quantitative models may be developed to characterize or fit observed data, to test theories of how a task may be solved by neural circuits, to determine how observed biophysical details might contribute to measured activity patterns, or to predict how an experimental manipulation would affect neural activity. In complexity, quantitative models can range from those that are highly biophysically realistic and that include detailed simulations at the level of individual synapses, to those that use abstract and simplified neuron models to simulate entire networks. Here, we survey the landscape of recently developed models of auditory cortical processing, highlighting a small selection of models to demonstrate how they help generate insight into the mechanisms of auditory processing. We discuss examples ranging from models that use details of synaptic properties to explain the temporal pattern of cortical responses to those that use modern deep neural networks to gain insight into human fMRI data. We conclude by discussing a biologically realistic and interpretable model that our laboratory has developed to explore aspects of vocalization categorization in the auditory pathway.
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Affiliation(s)
- Srivatsun Sadagopan
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA; Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Manaswini Kar
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Satyabrata Parida
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
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8
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Shao Y, Ostojic S. Relating local connectivity and global dynamics in recurrent excitatory-inhibitory networks. PLoS Comput Biol 2023; 19:e1010855. [PMID: 36689488 PMCID: PMC9894562 DOI: 10.1371/journal.pcbi.1010855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 02/02/2023] [Accepted: 01/06/2023] [Indexed: 01/24/2023] Open
Abstract
How the connectivity of cortical networks determines the neural dynamics and the resulting computations is one of the key questions in neuroscience. Previous works have pursued two complementary approaches to quantify the structure in connectivity. One approach starts from the perspective of biological experiments where only the local statistics of connectivity motifs between small groups of neurons are accessible. Another approach is based instead on the perspective of artificial neural networks where the global connectivity matrix is known, and in particular its low-rank structure can be used to determine the resulting low-dimensional dynamics. A direct relationship between these two approaches is however currently missing. Specifically, it remains to be clarified how local connectivity statistics and the global low-rank connectivity structure are inter-related and shape the low-dimensional activity. To bridge this gap, here we develop a method for mapping local connectivity statistics onto an approximate global low-rank structure. Our method rests on approximating the global connectivity matrix using dominant eigenvectors, which we compute using perturbation theory for random matrices. We demonstrate that multi-population networks defined from local connectivity statistics for which the central limit theorem holds can be approximated by low-rank connectivity with Gaussian-mixture statistics. We specifically apply this method to excitatory-inhibitory networks with reciprocal motifs, and show that it yields reliable predictions for both the low-dimensional dynamics, and statistics of population activity. Importantly, it analytically accounts for the activity heterogeneity of individual neurons in specific realizations of local connectivity. Altogether, our approach allows us to disentangle the effects of mean connectivity and reciprocal motifs on the global recurrent feedback, and provides an intuitive picture of how local connectivity shapes global network dynamics.
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Affiliation(s)
- Yuxiu Shao
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure—PSL Research University, Paris, France
- * E-mail: (YS); (SO)
| | - Srdjan Ostojic
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure—PSL Research University, Paris, France
- * E-mail: (YS); (SO)
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9
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Anandakumar DB, Liu RC. More than the end: OFF response plasticity as a mnemonic signature of a sound’s behavioral salience. Front Comput Neurosci 2022; 16:974264. [PMID: 36148326 PMCID: PMC9485674 DOI: 10.3389/fncom.2022.974264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 08/17/2022] [Indexed: 11/29/2022] Open
Abstract
In studying how neural populations in sensory cortex code dynamically varying stimuli to guide behavior, the role of spiking after stimuli have ended has been underappreciated. This is despite growing evidence that such activity can be tuned, experience-and context-dependent and necessary for sensory decisions that play out on a slower timescale. Here we review recent studies, focusing on the auditory modality, demonstrating that this so-called OFF activity can have a more complex temporal structure than the purely phasic firing that has often been interpreted as just marking the end of stimuli. While diverse and still incompletely understood mechanisms are likely involved in generating phasic and tonic OFF firing, more studies point to the continuing post-stimulus activity serving a short-term, stimulus-specific mnemonic function that is enhanced when the stimuli are particularly salient. We summarize these results with a conceptual model highlighting how more neurons within the auditory cortical population fire for longer duration after a sound’s termination during an active behavior and can continue to do so even while passively listening to behaviorally salient stimuli. Overall, these studies increasingly suggest that tonic auditory cortical OFF activity holds an echoic memory of specific, salient sounds to guide behavioral decisions.
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Affiliation(s)
- Dakshitha B Anandakumar
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- Department of Biology, Emory University, Atlanta, GA, United States
| | - Robert C Liu
- Department of Biology, Emory University, Atlanta, GA, United States
- Center for Translational Social Neuroscience, Emory University, Atlanta, GA, United States
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10
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Valente A, Ostojic S, Pillow J. Probing the Relationship Between Latent Linear Dynamical Systems and Low-Rank Recurrent Neural Network Models. Neural Comput 2022; 34:1871-1892. [PMID: 35896161 DOI: 10.1162/neco_a_01522] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 04/15/2022] [Indexed: 11/04/2022]
Abstract
A large body of work has suggested that neural populations exhibit low-dimensional dynamics during behavior. However, there are a variety of different approaches for modeling low-dimensional neural population activity. One approach involves latent linear dynamical system (LDS) models, in which population activity is described by a projection of low-dimensional latent variables with linear dynamics. A second approach involves low-rank recurrent neural networks (RNNs), in which population activity arises directly from a low-dimensional projection of past activity. Although these two modeling approaches have strong similarities, they arise in different contexts and tend to have different domains of application. Here we examine the precise relationship between latent LDS models and linear low-rank RNNs. When can one model class be converted to the other, and vice versa? We show that latent LDS models can only be converted to RNNs in specific limit cases, due to the non-Markovian property of latent LDS models. Conversely, we show that linear RNNs can be mapped onto LDS models, with latent dimensionality at most twice the rank of the RNN. A surprising consequence of our results is that a partially observed RNN is better represented by an LDS model than by an RNN consisting of only observed units.
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Affiliation(s)
- Adrian Valente
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure-PSL Research University, 75005 Paris, France
| | - Srdjan Ostojic
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure-PSL Research University, 75005 Paris, France
| | - Jonathan Pillow
- Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ 08544, U.S.A.
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11
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Solyga M, Barkat TR. Emergence and function of cortical offset responses in sound termination detection. eLife 2021; 10:e72240. [PMID: 34910627 PMCID: PMC8673837 DOI: 10.7554/elife.72240] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 11/28/2021] [Indexed: 11/20/2022] Open
Abstract
Offset responses in auditory processing appear after a sound terminates. They arise in neuronal circuits within the peripheral auditory system, but their role in the central auditory system remains unknown. Here, we ask what the behavioral relevance of cortical offset responses is and what circuit mechanisms drive them. At the perceptual level, our results reveal that experimentally minimizing auditory cortical offset responses decreases the mouse performance to detect sound termination, assigning a behavioral role to offset responses. By combining in vivo electrophysiology in the auditory cortex and thalamus of awake mice, we also demonstrate that cortical offset responses are not only inherited from the periphery but also amplified and generated de novo. Finally, we show that offset responses code more than silence, including relevant changes in sound trajectories. Together, our results reveal the importance of cortical offset responses in encoding sound termination and detecting changes within temporally discontinuous sounds crucial for speech and vocalization.
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12
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Lee J, Rothschild G. Encoding of acquired sound-sequence salience by auditory cortical offset responses. Cell Rep 2021; 37:109927. [PMID: 34731615 DOI: 10.1016/j.celrep.2021.109927] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/19/2021] [Accepted: 10/12/2021] [Indexed: 11/25/2022] Open
Abstract
Behaviorally relevant sounds are often composed of distinct acoustic units organized into specific temporal sequences. The meaning of such sound sequences can therefore be fully recognized only when they have terminated. However, the neural mechanisms underlying the perception of sound sequences remain unclear. Here, we use two-photon calcium imaging in the auditory cortex of behaving mice to test the hypothesis that neural responses to termination of sound sequences ("Off-responses") encode their acoustic history and behavioral salience. We find that auditory cortical Off-responses encode preceding sound sequences and that learning to associate a sound sequence with a reward induces enhancement of Off-responses relative to responses during the sound sequence ("On-responses"). Furthermore, learning enhances network-level discriminability of sound sequences by Off-responses. Last, learning-induced plasticity of Off-responses but not On-responses lasts to the next day. These findings identify auditory cortical Off-responses as a key neural signature of acquired sound-sequence salience.
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Affiliation(s)
- Joonyeup Lee
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Gideon Rothschild
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA; Kresge Hearing Research Institute and Department of Otolaryngology - Head and Neck Surgery, University of Michigan, Ann Arbor, MI 48109, USA.
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