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Homma NY, See JZ, Atencio CA, Hu C, Downer JD, Beitel RE, Cheung SW, Najafabadi MS, Olsen T, Bigelow J, Hasenstaub AR, Malone BJ, Schreiner CE. Receptive-field nonlinearities in primary auditory cortex: a comparative perspective. Cereb Cortex 2024; 34:bhae364. [PMID: 39270676 PMCID: PMC11398879 DOI: 10.1093/cercor/bhae364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 08/14/2024] [Accepted: 08/21/2024] [Indexed: 09/15/2024] Open
Abstract
Cortical processing of auditory information can be affected by interspecies differences as well as brain states. Here we compare multifeature spectro-temporal receptive fields (STRFs) and associated input/output functions or nonlinearities (NLs) of neurons in primary auditory cortex (AC) of four mammalian species. Single-unit recordings were performed in awake animals (female squirrel monkeys, female, and male mice) and anesthetized animals (female squirrel monkeys, rats, and cats). Neuronal responses were modeled as consisting of two STRFs and their associated NLs. The NLs for the STRF with the highest information content show a broad distribution between linear and quadratic forms. In awake animals, we find a higher percentage of quadratic-like NLs as opposed to more linear NLs in anesthetized animals. Moderate sex differences of the shape of NLs were observed between male and female unanesthetized mice. This indicates that the core AC possesses a rich variety of potential computations, particularly in awake animals, suggesting that multiple computational algorithms are at play to enable the auditory system's robust recognition of auditory events.
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Affiliation(s)
- Natsumi Y Homma
- John & Edward Coleman Memorial Laboratory, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, UK
| | - Jermyn Z See
- John & Edward Coleman Memorial Laboratory, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Craig A Atencio
- John & Edward Coleman Memorial Laboratory, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Congcong Hu
- John & Edward Coleman Memorial Laboratory, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Joshua D Downer
- John & Edward Coleman Memorial Laboratory, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
- Center of Neuroscience, University of California Davis, Newton Ct, Davis, CA, USA
| | - Ralph E Beitel
- John & Edward Coleman Memorial Laboratory, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Steven W Cheung
- John & Edward Coleman Memorial Laboratory, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Mina Sadeghi Najafabadi
- John & Edward Coleman Memorial Laboratory, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Timothy Olsen
- John & Edward Coleman Memorial Laboratory, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
| | - James Bigelow
- John & Edward Coleman Memorial Laboratory, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Andrea R Hasenstaub
- John & Edward Coleman Memorial Laboratory, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Brian J Malone
- John & Edward Coleman Memorial Laboratory, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
- Center of Neuroscience, University of California Davis, Newton Ct, Davis, CA, USA
| | - Christoph E Schreiner
- John & Edward Coleman Memorial Laboratory, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
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Powell NJ, Hein B, Kong D, Elpelt J, Mulholland HN, Kaschube M, Smith GB. Developmental maturation of millimeter-scale functional networks across brain areas. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.28.595371. [PMID: 38853883 PMCID: PMC11160666 DOI: 10.1101/2024.05.28.595371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Interacting with the environment to process sensory information, generate perceptions, and shape behavior engages neural networks in brain areas with highly varied representations, ranging from unimodal sensory cortices to higher-order association areas. Recent work suggests a much greater degree of commonality across areas, with distributed and modular networks present in both sensory and non-sensory areas during early development. However, it is currently unknown whether this initially common modular structure undergoes an equally common developmental trajectory, or whether such a modular functional organization persists in some areas-such as primary visual cortex-but not others. Here we examine the development of network organization across diverse cortical regions in ferrets of both sexes using in vivo widefield calcium imaging of spontaneous activity. We find that all regions examined, including both primary sensory cortices (visual, auditory, and somatosensory-V1, A1, and S1, respectively) and higher order association areas (prefrontal and posterior parietal cortices) exhibit a largely similar pattern of changes over an approximately 3 week developmental period spanning eye opening and the transition to predominantly externally-driven sensory activity. We find that both a modular functional organization and millimeter-scale correlated networks remain present across all cortical areas examined. These networks weakened over development in most cortical areas, but strengthened in V1. Overall, the conserved maintenance of modular organization across different cortical areas suggests a common pathway of network refinement, and suggests that a modular organization-known to encode functional representations in visual areas-may be similarly engaged in highly diverse brain areas.
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Affiliation(s)
- Nathaniel J Powell
- Optical Imaging and Brain Sciences Medical Discovery Team, Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Bettina Hein
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
| | - Deyue Kong
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
- Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany
- International Max Planck Research School for Neural Circuits, Frankfurt, Germany
| | - Jonas Elpelt
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
- Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany
| | - Haleigh N Mulholland
- Optical Imaging and Brain Sciences Medical Discovery Team, Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Matthias Kaschube
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
- Department of Computer Science and Mathematics, Goethe University, Frankfurt, Germany
| | - Gordon B Smith
- Optical Imaging and Brain Sciences Medical Discovery Team, Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
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Hullett PW, Leonard MK, Gorno-Tempini ML, Mandelli ML, Chang EF. Parallel Encoding of Speech in Human Frontal and Temporal Lobes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585648. [PMID: 38562883 PMCID: PMC10983886 DOI: 10.1101/2024.03.19.585648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Models of speech perception are centered around a hierarchy in which auditory representations in the thalamus propagate to primary auditory cortex, then to the lateral temporal cortex, and finally through dorsal and ventral pathways to sites in the frontal lobe. However, evidence for short latency speech responses and low-level spectrotemporal representations in frontal cortex raises the question of whether speech-evoked activity in frontal cortex strictly reflects downstream processing from lateral temporal cortex or whether there are direct parallel pathways from the thalamus or primary auditory cortex to the frontal lobe that supplement the traditional hierarchical architecture. Here, we used high-density direct cortical recordings, high-resolution diffusion tractography, and hemodynamic functional connectivity to evaluate for evidence of direct parallel inputs to frontal cortex from low-level areas. We found that neural populations in the frontal lobe show speech-evoked responses that are synchronous or occur earlier than responses in the lateral temporal cortex. These short latency frontal lobe neural populations encode spectrotemporal speech content indistinguishable from spectrotemporal encoding patterns observed in the lateral temporal lobe, suggesting parallel auditory speech representations reaching temporal and frontal cortex simultaneously. This is further supported by white matter tractography and functional connectivity patterns that connect the auditory nucleus of the thalamus (medial geniculate body) and the primary auditory cortex to the frontal lobe. Together, these results support the existence of a robust pathway of parallel inputs from low-level auditory areas to frontal lobe targets and illustrate long-range parallel architecture that works alongside the classical hierarchical speech network model.
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Powell NJ, Hein B, Kong D, Elpelt J, Mulholland HN, Kaschube M, Smith GB. Common modular architecture across diverse cortical areas in early development. Proc Natl Acad Sci U S A 2024; 121:e2313743121. [PMID: 38446851 PMCID: PMC10945769 DOI: 10.1073/pnas.2313743121] [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: 08/09/2023] [Accepted: 01/16/2024] [Indexed: 03/08/2024] Open
Abstract
In order to deal with a complex environment, animals form a diverse range of neural representations that vary across cortical areas, ranging from largely unimodal sensory input to higher-order representations of goals, outcomes, and motivation. The developmental origin of this diversity is currently unclear, as representations could arise through processes that are already area-specific from the earliest developmental stages or alternatively, they could emerge from an initially common functional organization shared across areas. Here, we use spontaneous activity recorded with two-photon and widefield calcium imaging to reveal the functional organization across the early developing cortex in ferrets, a species with a well-characterized columnar organization and modular structure of spontaneous activity in the visual cortex. We find that in animals 7 to 14 d prior to eye-opening and ear canal opening, spontaneous activity in both sensory areas (auditory and somatosensory cortex, A1 and S1, respectively), and association areas (posterior parietal and prefrontal cortex, PPC and PFC, respectively) showed an organized and modular structure that is highly similar to the organization in V1. In all cortical areas, this modular activity was distributed across the cortical surface, forming functional networks that exhibit millimeter-scale correlations. Moreover, this modular structure was evident in highly coherent spontaneous activity at the cellular level, with strong correlations among local populations of neurons apparent in all cortical areas examined. Together, our results demonstrate a common distributed and modular organization across the cortex during early development, suggesting that diverse cortical representations develop initially according to similar design principles.
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Affiliation(s)
- Nathaniel J. Powell
- Optical Imaging and Brain Sciences Medical Discovery Team, Department of Neuroscience, University of Minnesota, Minneapolis, MN55455
| | - Bettina Hein
- Center for Theoretical Neuroscience, Zuckerman Institute, Columbia University, New York, NY10027
| | - Deyue Kong
- Frankfurt Institute for Advanced Studies, Frankfurt am Main60438, Germany
- Department of Computer Science and Mathematics, Goethe University, Frankfurt am Main60629, Germany
- International Max Planck Research School for Neural Circuits, Frankfurt am Main60438, Germany
| | - Jonas Elpelt
- Frankfurt Institute for Advanced Studies, Frankfurt am Main60438, Germany
- Department of Computer Science and Mathematics, Goethe University, Frankfurt am Main60629, Germany
| | - Haleigh N. Mulholland
- Optical Imaging and Brain Sciences Medical Discovery Team, Department of Neuroscience, University of Minnesota, Minneapolis, MN55455
| | - Matthias Kaschube
- Frankfurt Institute for Advanced Studies, Frankfurt am Main60438, Germany
- Department of Computer Science and Mathematics, Goethe University, Frankfurt am Main60629, Germany
| | - Gordon B. Smith
- Optical Imaging and Brain Sciences Medical Discovery Team, Department of Neuroscience, University of Minnesota, Minneapolis, MN55455
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Lindeberg T. A time-causal and time-recursive scale-covariant scale-space representation of temporal signals and past time. BIOLOGICAL CYBERNETICS 2023; 117:21-59. [PMID: 36689001 PMCID: PMC10160219 DOI: 10.1007/s00422-022-00953-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 11/21/2022] [Indexed: 05/05/2023]
Abstract
This article presents an overview of a theory for performing temporal smoothing on temporal signals in such a way that: (i) temporally smoothed signals at coarser temporal scales are guaranteed to constitute simplifications of corresponding temporally smoothed signals at any finer temporal scale (including the original signal) and (ii) the temporal smoothing process is both time-causal and time-recursive, in the sense that it does not require access to future information and can be performed with no other temporal memory buffer of the past than the resulting smoothed temporal scale-space representations themselves. For specific subsets of parameter settings for the classes of linear and shift-invariant temporal smoothing operators that obey this property, it is shown how temporal scale covariance can be additionally obtained, guaranteeing that if the temporal input signal is rescaled by a uniform temporal scaling factor, then also the resulting temporal scale-space representations of the rescaled temporal signal will constitute mere rescalings of the temporal scale-space representations of the original input signal, complemented by a shift along the temporal scale dimension. The resulting time-causal limit kernel that obeys this property constitutes a canonical temporal kernel for processing temporal signals in real-time scenarios when the regular Gaussian kernel cannot be used, because of its non-causal access to information from the future, and we cannot additionally require the temporal smoothing process to comprise a complementary memory of the past beyond the information contained in the temporal smoothing process itself, which in this way also serves as a multi-scale temporal memory of the past. We describe how the time-causal limit kernel relates to previously used temporal models, such as Koenderink's scale-time kernels and the ex-Gaussian kernel. We do also give an overview of how the time-causal limit kernel can be used for modelling the temporal processing in models for spatio-temporal and spectro-temporal receptive fields, and how it more generally has a high potential for modelling neural temporal response functions in a purely time-causal and time-recursive way, that can also handle phenomena at multiple temporal scales in a theoretically well-founded manner. We detail how this theory can be efficiently implemented for discrete data, in terms of a set of recursive filters coupled in cascade. Hence, the theory is generally applicable for both: (i) modelling continuous temporal phenomena over multiple temporal scales and (ii) digital processing of measured temporal signals in real time. We conclude by stating implications of the theory for modelling temporal phenomena in biological, perceptual, neural and memory processes by mathematical models, as well as implications regarding the philosophy of time and perceptual agents. Specifically, we propose that for A-type theories of time, as well as for perceptual agents, the notion of a non-infinitesimal inner temporal scale of the temporal receptive fields has to be included in representations of the present, where the inherent nonzero temporal delay of such time-causal receptive fields implies a need for incorporating predictions from the actual time-delayed present in the layers of a perceptual hierarchy, to make it possible for a representation of the perceptual present to constitute a representation of the environment with timing properties closer to the actual present.
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Affiliation(s)
- Tony Lindeberg
- Computational Brain Science Lab, Division of Computational Science and Technology, KTH Royal Institute of Technology, 100 44, Stockholm, Sweden.
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Morrill RJ, Bigelow J, DeKloe J, Hasenstaub AR. Audiovisual task switching rapidly modulates sound encoding in mouse auditory cortex. eLife 2022; 11:e75839. [PMID: 35980027 PMCID: PMC9427107 DOI: 10.7554/elife.75839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 08/17/2022] [Indexed: 11/13/2022] Open
Abstract
In everyday behavior, sensory systems are in constant competition for attentional resources, but the cellular and circuit-level mechanisms of modality-selective attention remain largely uninvestigated. We conducted translaminar recordings in mouse auditory cortex (AC) during an audiovisual (AV) attention shifting task. Attending to sound elements in an AV stream reduced both pre-stimulus and stimulus-evoked spiking activity, primarily in deep-layer neurons and neurons without spectrotemporal tuning. Despite reduced spiking, stimulus decoder accuracy was preserved, suggesting improved sound encoding efficiency. Similarly, task-irrelevant mapping stimuli during inter-trial intervals evoked fewer spikes without impairing stimulus encoding, indicating that attentional modulation generalized beyond training stimuli. Importantly, spiking reductions predicted trial-to-trial behavioral accuracy during auditory attention, but not visual attention. Together, these findings suggest auditory attention facilitates sound discrimination by filtering sound-irrelevant background activity in AC, and that the deepest cortical layers serve as a hub for integrating extramodal contextual information.
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Affiliation(s)
- Ryan J Morrill
- Coleman Memorial Laboratory, University of California, San FranciscoSan FranciscoUnited States
- Neuroscience Graduate Program, University of California, San FranciscoSan FranciscoUnited States
- Department of Otolaryngology–Head and Neck Surgery, University of California, San FranciscoSan FranciscoUnited States
| | - James Bigelow
- Coleman Memorial Laboratory, University of California, San FranciscoSan FranciscoUnited States
- Department of Otolaryngology–Head and Neck Surgery, University of California, San FranciscoSan FranciscoUnited States
| | - Jefferson DeKloe
- Coleman Memorial Laboratory, University of California, San FranciscoSan FranciscoUnited States
- Department of Otolaryngology–Head and Neck Surgery, University of California, San FranciscoSan FranciscoUnited States
| | - Andrea R Hasenstaub
- Coleman Memorial Laboratory, University of California, San FranciscoSan FranciscoUnited States
- Neuroscience Graduate Program, University of California, San FranciscoSan FranciscoUnited States
- Department of Otolaryngology–Head and Neck Surgery, University of California, San FranciscoSan FranciscoUnited States
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Baratham VL, Dougherty ME, Hermiz J, Ledochowitsch P, Maharbiz MM, Bouchard KE. Columnar Localization and Laminar Origin of Cortical Surface Electrical Potentials. J Neurosci 2022; 42:3733-3748. [PMID: 35332084 PMCID: PMC9087723 DOI: 10.1523/jneurosci.1787-21.2022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 02/09/2022] [Accepted: 03/09/2022] [Indexed: 11/21/2022] Open
Abstract
Electrocorticography (ECoG) methodologically bridges basic neuroscience and understanding of human brains in health and disease. However, the localization of ECoG signals across the surface of the brain and the spatial distribution of their generating neuronal sources are poorly understood. To address this gap, we recorded from rat auditory cortex using customized μECoG, and simulated cortical surface electrical potentials with a full-scale, biophysically detailed cortical column model. Experimentally, μECoG-derived auditory representations were tonotopically organized and signals were anisotropically localized to less than or equal to ±200 μm, that is, a single cortical column. Biophysical simulations reproduce experimental findings and indicate that neurons in cortical layers V and VI contribute ∼85% of evoked high-gamma signal recorded at the surface. Cell number and synchrony were the primary biophysical properties determining laminar contributions to evoked μECoG signals, whereas distance was only a minimal factor. Thus, evoked μECoG signals primarily originate from neurons in the infragranular layers of a single cortical column.SIGNIFICANCE STATEMENT ECoG methodologically bridges basic neuroscience and understanding of human brains in health and disease. However, the localization of ECoG signals across the surface of the brain and the spatial distribution of their generating neuronal sources are poorly understood. We investigated the localization and origins of sensory-evoked ECoG responses. We experimentally found that ECoG responses were anisotropically localized to a cortical column. Biophysically detailed simulations revealed that neurons in layers V and VI were the primary sources of evoked ECoG responses. These results indicate that evoked ECoG high-gamma responses are primarily generated by the population spike rate of pyramidal neurons in layers V and VI of single cortical columns and highlight the possibility of understanding how microscopic sources produce mesoscale signals.
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Affiliation(s)
- Vyassa L Baratham
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
- Department of Physics, University of California-Berkeley, Berkeley, California 94720
| | - Maximilian E Dougherty
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - John Hermiz
- Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | - Michel M Maharbiz
- Center for Neural Engineering and Prosthesis, University of California-Berkeley/San Francisco, Berkeley, California 94720-3370
- Department of Electrical Engineering and Computer Science, University of California-Berkeley, Berkeley, California 94720
| | - Kristofer E Bouchard
- Center for Neural Engineering and Prosthesis, University of California-Berkeley/San Francisco, Berkeley, California 94720-3370
- Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience, University of California-Berkeley, Berkeley, California 94720
- Scientific Data Division, Lawerence Berkeley National Lab, Berkeley, California 94720
- Biological Systems and Engineering Division, Lawerence Berkeley National Lab, Berkeley, California 94720
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Homma NY, Atencio CA, Schreiner CE. Plasticity of Multidimensional Receptive Fields in Core Rat Auditory Cortex Directed by Sound Statistics. Neuroscience 2021; 467:150-170. [PMID: 33951506 DOI: 10.1016/j.neuroscience.2021.04.028] [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: 08/31/2020] [Revised: 04/09/2021] [Accepted: 04/24/2021] [Indexed: 11/17/2022]
Abstract
Sensory cortical neurons can nonlinearly integrate a wide range of inputs. The outcome of this nonlinear process can be approximated by more than one receptive field component or filter to characterize the ensuing stimulus preference. The functional properties of multidimensional filters are, however, not well understood. Here we estimated two spectrotemporal receptive fields (STRFs) per neuron using maximally informative dimension analysis. We compared their temporal and spectral modulation properties and determined the stimulus information captured by the two STRFs in core rat auditory cortical fields, primary auditory cortex (A1) and ventral auditory field (VAF). The first STRF is the dominant filter and acts as a sound feature detector in both fields. The second STRF is less feature specific, preferred lower modulations, and had less spike information compared to the first STRF. The information jointly captured by the two STRFs was larger than that captured by the sum of the individual STRFs, reflecting nonlinear interactions of two filters. This information gain was larger in A1. We next determined how the acoustic environment affects the structure and relationship of these two STRFs. Rats were exposed to moderate levels of spectrotemporally modulated noise during development. Noise exposure strongly altered the spectrotemporal preference of the first STRF in both cortical fields. The interaction between the two STRFs was reduced by noise exposure in A1 but not in VAF. The results reveal new functional distinctions between A1 and VAF indicating that (i) A1 has stronger interactions of the two STRFs than VAF, (ii) noise exposure diminishes modulation parameter representation contained in the noise more strongly for the first STRF in both fields, and (iii) plasticity induced by noise exposure can affect the strength of filter interactions in A1. Taken together, ascertaining two STRFs per neuron enhances the understanding of cortical information processing and plasticity effects in core auditory cortex.
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Affiliation(s)
- Natsumi Y Homma
- Coleman Memorial Laboratory, Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, USA; Center for Integrative Neuroscience, University of California San Francisco, San Francisco, USA.
| | - Craig A Atencio
- Coleman Memorial Laboratory, Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, USA
| | - Christoph E Schreiner
- Coleman Memorial Laboratory, Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, USA; Center for Integrative Neuroscience, University of California San Francisco, San Francisco, USA
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See JZ, Homma NY, Atencio CA, Sohal VS, Schreiner CE. Information diversity in individual auditory cortical neurons is associated with functionally distinct coordinated neuronal ensembles. Sci Rep 2021; 11:4064. [PMID: 33603027 PMCID: PMC7893178 DOI: 10.1038/s41598-021-83565-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 01/18/2021] [Indexed: 01/31/2023] Open
Abstract
Neuronal activity in auditory cortex is often highly synchronous between neighboring neurons. Such coordinated activity is thought to be crucial for information processing. We determined the functional properties of coordinated neuronal ensembles (cNEs) within primary auditory cortical (AI) columns relative to the contributing neurons. Nearly half of AI cNEs showed robust spectro-temporal receptive fields whereas the remaining cNEs showed little or no acoustic feature selectivity. cNEs can therefore capture either specific, time-locked information of spectro-temporal stimulus features or reflect stimulus-unspecific, less-time specific processing aspects. By contrast, we show that individual neurons can represent both of those aspects through membership in multiple cNEs with either high or absent feature selectivity. These associations produce functionally heterogeneous spikes identifiable by instantaneous association with different cNEs. This demonstrates that single neuron spike trains can sequentially convey multiple aspects that contribute to cortical processing, including stimulus-specific and unspecific information.
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Affiliation(s)
- Jermyn Z. See
- grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, Kavli Institute for Fundamental Neuroscience, and Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158-0444 USA ,Department of Otolaryngology-Head and Neck Surgery, Coleman Memorial Laboratory, University of Caliornia, San Francisco, USA
| | - Natsumi Y. Homma
- grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, Kavli Institute for Fundamental Neuroscience, and Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158-0444 USA ,Department of Otolaryngology-Head and Neck Surgery, Coleman Memorial Laboratory, University of Caliornia, San Francisco, USA
| | - Craig A. Atencio
- grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, Kavli Institute for Fundamental Neuroscience, and Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158-0444 USA ,Department of Otolaryngology-Head and Neck Surgery, Coleman Memorial Laboratory, University of Caliornia, San Francisco, USA
| | - Vikaas S. Sohal
- grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, Kavli Institute for Fundamental Neuroscience, and Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158-0444 USA ,grid.266102.10000 0001 2297 6811Department of Psychiatry, University of California, San Francisco, USA
| | - Christoph E. Schreiner
- grid.266102.10000 0001 2297 6811Weill Institute for Neuroscience, Kavli Institute for Fundamental Neuroscience, and Sloan-Swartz Center for Theoretical Neurobiology, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158-0444 USA ,Department of Otolaryngology-Head and Neck Surgery, Coleman Memorial Laboratory, University of Caliornia, San Francisco, USA
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10
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Beitel RE, Schreiner CE, Vollmer M. Spectral plasticity in monkey primary auditory cortex limits performance generalization in a temporal discrimination task. J Neurophysiol 2020; 124:1798-1814. [PMID: 32997564 DOI: 10.1152/jn.00278.2020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Auditory experience and behavioral training can modify perceptual performance. However, the consequences of temporal perceptual learning for temporal and spectral neural processing remain unclear. Specifically, the attributes of neural plasticity that underlie task generalization in behavioral performance remain uncertain. To assess the relationship between behavioral and neural plasticity, we evaluated neuronal temporal processing and spectral tuning in primary auditory cortex (AI) of anesthetized owl monkeys trained to discriminate increases in the envelope frequency (e.g., 4-Hz standard vs. >5-Hz targets) of sinusoidally amplitude-modulated (SAM) 1-kHz or 2-kHz carriers. Behavioral and neuronal performance generalization was evaluated for carriers ranging from 0.5 kHz to 8 kHz. Psychophysical thresholds revealed high SAM discrimination acuity for carriers from one octave below to ∼0.6 octave above the trained carrier frequency. However, generalization of SAM discrimination learning progressively declined for carrier frequencies >0.6 octave above the trained carrier frequency. Neural responses in AI showed that SAM discrimination training resulted in 1) increases in temporal modulation preference, especially at carriers close to the trained frequency, 2) narrowing of spectral tuning for neurons with characteristic frequencies near the trained carrier frequency, potentially limiting spectral generalization of temporal training effects, and 3) enhancement of firing-rate contrast for rewarded versus nonrewarded SAM frequencies, providing a potential cue for behavioral temporal discrimination near the trained carrier frequency. Our findings suggest that temporal training at a specific spectral location sharpens local frequency tuning, thus, confining the training effects to a narrow frequency range and limiting generalization of temporal discrimination learning across a wider frequency range.NEW & NOTEWORTHY Monkeys' ability to generalize amplitude modulation discrimination to nontrained carriers was limited to one octave below and 0.6 octave above the trained carrier frequency. Asymmetric generalization was paralleled by sharpening in cortical spectral tuning and enhanced firing-rate contrast between rewarded and nonrewarded SAM stimuli at carriers near the trained frequency. The spectral content of the training stimulus specified spectral and temporal plasticity that may provide a neural substrate for limitations in generalization of temporal discrimination learning.
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Affiliation(s)
- Ralph E Beitel
- Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, California
| | - Christoph E Schreiner
- Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, California
| | - Maike Vollmer
- Department of Otolaryngology-Head and Neck Surgery, University Hospital Magdeburg, Otto-von-Guericke University, Magdeburg, Germany.,Center for Learning and Memory Research, Leibniz Institute for Neurobiology, Magdeburg, Germany
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11
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Kim KX, Atencio CA, Schreiner CE. Stimulus dependent transformations between synaptic and spiking receptive fields in auditory cortex. Nat Commun 2020; 11:1102. [PMID: 32107370 PMCID: PMC7046699 DOI: 10.1038/s41467-020-14835-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 02/06/2020] [Indexed: 11/09/2022] Open
Abstract
Auditory cortex neurons nonlinearly integrate synaptic inputs from the thalamus and cortex, and generate spiking outputs for simple and complex sounds. Directly comparing synaptic and spiking activity can determine whether this input-output transformation is stimulus-dependent. We employ in vivo whole-cell recordings in the mouse primary auditory cortex, using pure tones and broadband dynamic moving ripple stimuli, to examine properties of functional integration in tonal (TRFs) and spectrotemporal (STRFs) receptive fields. Spectral tuning in STRFs derived from synaptic, subthreshold and spiking responses proves to be substantially more selective than for TRFs. We describe diverse spectral and temporal modulation preferences and distinct nonlinearities, and their modifications between the input and output stages of neural processing. These results characterize specific processing differences at the level of synaptic convergence, integration and spike generation resulting in stimulus-dependent transformation patterns in the primary auditory cortex. The authors compare receptive fields and nonlinearities of synaptic inputs, membrane potentials, and spiking activity in the auditory cortex for broadband stimuli revealing distinct differences, which lead to an increase in feature selectivity from neuron input to output. Frequency selectivity is distinctly higher for spectrotemporal receptive fields (STRFs) than for tonal receptive fields (TRFs).
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Affiliation(s)
- Kyunghee X Kim
- Coleman Memorial Laboratory, Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, USA.
| | - Craig A Atencio
- Coleman Memorial Laboratory, Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, USA
| | - Christoph E Schreiner
- Coleman Memorial Laboratory, Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, USA.,Center for Integrative Neuroscience, University of California San Francisco, San Francisco, USA
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12
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Abstract
Our ability to make sense of the auditory world results from neural processing that begins in the ear, goes through multiple subcortical areas, and continues in the cortex. The specific contribution of the auditory cortex to this chain of processing is far from understood. Although many of the properties of neurons in the auditory cortex resemble those of subcortical neurons, they show somewhat more complex selectivity for sound features, which is likely to be important for the analysis of natural sounds, such as speech, in real-life listening conditions. Furthermore, recent work has shown that auditory cortical processing is highly context-dependent, integrates auditory inputs with other sensory and motor signals, depends on experience, and is shaped by cognitive demands, such as attention. Thus, in addition to being the locus for more complex sound selectivity, the auditory cortex is increasingly understood to be an integral part of the network of brain regions responsible for prediction, auditory perceptual decision-making, and learning. In this review, we focus on three key areas that are contributing to this understanding: the sound features that are preferentially represented by cortical neurons, the spatial organization of those preferences, and the cognitive roles of the auditory cortex.
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Affiliation(s)
- Andrew J King
- Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, OX1 3PT, UK
| | - Sundeep Teki
- Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, OX1 3PT, UK
| | - Ben D B Willmore
- Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, OX1 3PT, UK
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13
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Cluster-based analysis improves predictive validity of spike-triggered receptive field estimates. PLoS One 2017; 12:e0183914. [PMID: 28877194 PMCID: PMC5587334 DOI: 10.1371/journal.pone.0183914] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Accepted: 08/14/2017] [Indexed: 11/19/2022] Open
Abstract
Spectrotemporal receptive field (STRF) characterization is a central goal of auditory physiology. STRFs are often approximated by the spike-triggered average (STA), which reflects the average stimulus preceding a spike. In many cases, the raw STA is subjected to a threshold defined by gain values expected by chance. However, such correction methods have not been universally adopted, and the consequences of specific gain-thresholding approaches have not been investigated systematically. Here, we evaluate two classes of statistical correction techniques, using the resulting STRF estimates to predict responses to a novel validation stimulus. The first, more traditional technique eliminated STRF pixels (time-frequency bins) with gain values expected by chance. This correction method yielded significant increases in prediction accuracy, including when the threshold setting was optimized for each unit. The second technique was a two-step thresholding procedure wherein clusters of contiguous pixels surviving an initial gain threshold were then subjected to a cluster mass threshold based on summed pixel values. This approach significantly improved upon even the best gain-thresholding techniques. Additional analyses suggested that allowing threshold settings to vary independently for excitatory and inhibitory subfields of the STRF resulted in only marginal additional gains, at best. In summary, augmenting reverse correlation techniques with principled statistical correction choices increased prediction accuracy by over 80% for multi-unit STRFs and by over 40% for single-unit STRFs, furthering the interpretational relevance of the recovered spectrotemporal filters for auditory systems analysis.
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14
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Atencio CA, Sharpee TO. Multidimensional receptive field processing by cat primary auditory cortical neurons. Neuroscience 2017; 359:130-141. [PMID: 28694174 DOI: 10.1016/j.neuroscience.2017.07.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 06/03/2017] [Accepted: 07/03/2017] [Indexed: 12/01/2022]
Abstract
The receptive fields of many auditory cortical neurons are multidimensional and are best represented by more than one stimulus feature. The number of these dimensions, their characteristics, and how they differ with stimulus context have been relatively unexplored. Standard methods that are often used to characterize multidimensional stimulus selectivity, such as spike-triggered covariance (STC) or maximally informative dimensions (MIDs), are either limited to Gaussian stimuli or are only able to recover a small number of stimulus features due to data limitations. An information theoretic extension of STC, the maximum noise entropy (MNE) model, can be used with non-Gaussian stimulus distributions to find an arbitrary number of stimulus dimensions. When we applied the MNE model to auditory cortical neurons, we often found more than two stimulus features that influenced neuronal firing. Excitatory and suppressive features coded different acoustic contexts: excitatory features encoded higher temporal and spectral modulations, while suppressive features had lower modulation frequency preferences. We found that the excitatory and suppressive features themselves were sensitive to stimulus context when we employed two stimuli that differed only in their short-term correlation structure: while the linear features were similar, the secondary features were strongly affected by stimulus statistics. These results show that multidimensional receptive field processing is influenced by feature type and stimulus context.
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Affiliation(s)
- Craig A Atencio
- Coleman Memorial Laboratory, UCSF Center for Integrative Neuroscience, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-HNS, University of California, San Francisco, USA.
| | - Tatyana O Sharpee
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA; Center for Theoretical Biological Physics and Department of Physics, University of California, San Diego, La Jolla, CA, USA
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15
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Phillips EAK, Schreiner CE, Hasenstaub AR. Diverse effects of stimulus history in waking mouse auditory cortex. J Neurophysiol 2017; 118:1376-1393. [PMID: 28566458 DOI: 10.1152/jn.00094.2017] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 05/10/2017] [Accepted: 05/29/2017] [Indexed: 11/22/2022] Open
Abstract
Responses to auditory stimuli are often strongly influenced by recent stimulus history. For example, in a paradigm called forward suppression, brief sounds can suppress the perception of, and the neural responses to, a subsequent sound, with the magnitude of this suppression depending on both the spectral and temporal distances between the sounds. As a step towards understanding the mechanisms that generate these adaptive representations in awake animals, we quantitatively characterize responses to two-tone sequences in the auditory cortex of waking mice. We find that cortical responses in a forward suppression paradigm are more diverse in waking mice than previously appreciated, that these responses vary between cells with different firing characteristics and waveform shapes, but that the variability in these responses is not substantially related to cortical depth or columnar location. Moreover, responses to the first tone in the sequence are often not linearly related to the suppression of the second tone response, suggesting that spike-frequency adaptation of cortical cells is not a large contributor to forward suppression or its variability. Instead, we use a simple multilayered model to show that cell-to-cell differences in the balance of intracortical inhibition and excitation will naturally produce such a diversity of forward interactions. We propose that diverse inhibitory connectivity allows the cortex to encode spectro-temporally fluctuating stimuli in multiple parallel ways.NEW & NOTEWORTHY Behavioral and neural responses to auditory stimuli are profoundly influenced by recent sounds, yet how this occurs is not known. Here, the authors show in the auditory cortex of awake mice that the quality of history-dependent effects is diverse and related to cell type, response latency, firing rates, and receptive field bandwidth. In a cortical model, differences in excitatory-inhibitory balance can produce this diversity, providing the cortex with multiple ways of representing temporally complex information.
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Affiliation(s)
- Elizabeth A K Phillips
- Coleman Memorial Laboratory, University of California, San Francisco, California.,Neuroscience Graduate Program, University of California, San Francisco, California.,Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, California.,Center for Integrative Neuroscience, University of California, San Francisco, California; and.,Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, California
| | - Christoph E Schreiner
- Coleman Memorial Laboratory, University of California, San Francisco, California.,Neuroscience Graduate Program, University of California, San Francisco, California.,Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, California.,Center for Integrative Neuroscience, University of California, San Francisco, California; and.,Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, California
| | - Andrea R Hasenstaub
- Coleman Memorial Laboratory, University of California, San Francisco, California; .,Neuroscience Graduate Program, University of California, San Francisco, California.,Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, California.,Center for Integrative Neuroscience, University of California, San Francisco, California; and.,Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, California
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16
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Synchrony, connectivity, and functional similarity in auditory midbrain local circuits. Neuroscience 2016; 335:30-53. [PMID: 27544405 DOI: 10.1016/j.neuroscience.2016.08.024] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Revised: 08/08/2016] [Accepted: 08/10/2016] [Indexed: 11/21/2022]
Abstract
The central nucleus of the inferior colliculus (ICC) contains a laminar structure that functions as an organizing substrate of ascending inputs and local processing. While topographic distributions of ICC response parameters within and across laminae have been reported, the functional micro-organization of the ICC is less well understood. For pairs of neighboring ICC neurons, we examined the nature of functional connectivity and receptive field preferences to gain a better understanding of the structure and function of local circuits. By recording from pairs of adjacent neurons and presenting pure-tone and dynamic broad-band stimulation, we estimated functional connectivity and local differences in frequency response areas (FRAs), spectrotemporal receptive fields (STRFs), nonlinear input/output functions, and single-spike information. From the cross-covariance functions we identified putative unidirectional as well as bidirectional excitatory/inhibitory interactions. STRFs of neighboring neurons strongly conserve best frequency, and moderately agree in STRF similarity, bandwidth, temporal response type, best modulation frequency, nonlinearity structure, and degree of information processing. Excitatory connectivity was stronger and temporally more precise than for inhibitory connections. Neither connection strength nor degree of synchrony correlated with receptive field parameters. The functional similarity of local pairs of ICC neurons was substantially less than for local pairs in the granular layers of primary auditory cortex (AI). These results imply that while the ICC is an obligatory nexus of ascending information, local neurons are comparatively weakly connected and exhibit considerable receptive field variability, potentially reflecting the heterogeneity of converging inputs to ICC functional zones.
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17
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Human Superior Temporal Gyrus Organization of Spectrotemporal Modulation Tuning Derived from Speech Stimuli. J Neurosci 2016; 36:2014-26. [PMID: 26865624 DOI: 10.1523/jneurosci.1779-15.2016] [Citation(s) in RCA: 95] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
UNLABELLED The human superior temporal gyrus (STG) is critical for speech perception, yet the organization of spectrotemporal processing of speech within the STG is not well understood. Here, to characterize the spatial organization of spectrotemporal processing of speech across human STG, we use high-density cortical surface field potential recordings while participants listened to natural continuous speech. While synthetic broad-band stimuli did not yield sustained activation of the STG, spectrotemporal receptive fields could be reconstructed from vigorous responses to speech stimuli. We find that the human STG displays a robust anterior-posterior spatial distribution of spectrotemporal tuning in which the posterior STG is tuned for temporally fast varying speech sounds that have relatively constant energy across the frequency axis (low spectral modulation) while the anterior STG is tuned for temporally slow varying speech sounds that have a high degree of spectral variation across the frequency axis (high spectral modulation). This work illustrates organization of spectrotemporal processing in the human STG, and illuminates processing of ethologically relevant speech signals in a region of the brain specialized for speech perception. SIGNIFICANCE STATEMENT Considerable evidence has implicated the human superior temporal gyrus (STG) in speech processing. However, the gross organization of spectrotemporal processing of speech within the STG is not well characterized. Here we use natural speech stimuli and advanced receptive field characterization methods to show that spectrotemporal features within speech are well organized along the posterior-to-anterior axis of the human STG. These findings demonstrate robust functional organization based on spectrotemporal modulation content, and illustrate that much of the encoded information in the STG represents the physical acoustic properties of speech stimuli.
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18
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Atencio CA, Schreiner CE. Functional congruity in local auditory cortical microcircuits. Neuroscience 2016; 316:402-19. [PMID: 26768399 DOI: 10.1016/j.neuroscience.2015.12.057] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 12/16/2015] [Accepted: 12/30/2015] [Indexed: 12/19/2022]
Abstract
Functional columns of primary auditory cortex (AI) are arranged in layers, each composed of highly connected fine-scale networks. The basic response properties and interactions within these local subnetworks have only begun to be assessed. We examined the functional diversity of neurons within the laminar microarchitecture of cat AI to determine the relationship of spectrotemporal processing between neighboring neurons. Neuronal activity was recorded across the cortical layers while presenting a dynamically modulated broadband noise. Spectrotemporal receptive fields (STRFs) and their nonlinear input/output functions (nonlinearities) were constructed for each neuron and compared for pairs of neurons simultaneously recorded at the same contact site. Properties of these local neuron pairs showed greater similarity than non-paired neurons within the same column for all considered parameters including firing rate, envelope-phase precision, preferred spectral and temporal modulation frequency, as well as for the threshold and transition of the response nonlinearity. This higher functional similarity of paired versus non-paired neurons was most apparent in infragranular neuron pairs, and less for local supragranular and granular pairs. The functional similarity of local paired neurons for firing rate, best temporal modulation frequency and two nonlinearity aspects was laminar dependent, with infragranular local pair-wise differences larger than for granular or supragranular layers. Synchronous spiking events between pairs of neurons revealed that simultaneous 'Bicellular' spikes, in addition to carrying higher stimulus information than non-synchronized spikes, encoded faster modulation frequencies. Bicellular functional differences to the best matched of the paired neurons could be substantial. Bicellular nonlinearities showed that synchronous spikes act to transmit stimulus information with higher fidelity and precision than non-synchronous spikes of the individual neurons, thus, likely enhancing stimulus feature selectivity in their target neurons. Overall, the well-correlated and temporally precise processing within local subnetworks of cat AI showed laminar-dependent functional diversity in spectrotemporal processing, despite high intra-columnar congruity in frequency preference.
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Affiliation(s)
- C A Atencio
- Coleman Memorial Laboratory, UCSF Center for Integrative Neuroscience, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-HNS, University of California, San Francisco, United States.
| | - C E Schreiner
- Coleman Memorial Laboratory, UCSF Center for Integrative Neuroscience, Kavli Institute for Fundamental Neuroscience, Department of Otolaryngology-HNS, University of California, San Francisco, United States
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19
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Thorson IL, Liénard J, David SV. The Essential Complexity of Auditory Receptive Fields. PLoS Comput Biol 2015; 11:e1004628. [PMID: 26683490 PMCID: PMC4684325 DOI: 10.1371/journal.pcbi.1004628] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 10/26/2015] [Indexed: 12/05/2022] Open
Abstract
Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models. Understanding how the brain solves sensory problems can provide useful insight for the development of automated systems such as speech recognizers and image classifiers. Recent developments in nonlinear regression and machine learning have produced powerful algorithms for characterizing the input-output relationship of complex systems. However, the complexity of sensory neural systems, combined with practical limitations on experimental data, make it difficult to apply arbitrarily complex analyses to neural data. In this study we pushed analysis in the opposite direction, toward simpler models. We asked how simple a model can be while still capturing the essential sensory properties of neurons in auditory cortex. We found that substantially simpler formulations of the widely-used spectro-temporal receptive field are able to perform as well as the best current models. These simpler formulations define new basis sets that can be incorporated into state-of-the-art machine learning algorithms for a more exhaustive exploration of sensory processing.
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Affiliation(s)
- Ivar L. Thorson
- Oregon Hearing Research Center, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Jean Liénard
- Department of Mathematics, Washington State University, Vancouver, Washington, United States of America
| | - Stephen V. David
- Oregon Hearing Research Center, Oregon Health & Science University, Portland, Oregon, United States of America
- * E-mail:
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20
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Lindeberg T, Friberg A. Idealized computational models for auditory receptive fields. PLoS One 2015; 10:e0119032. [PMID: 25822973 PMCID: PMC4379182 DOI: 10.1371/journal.pone.0119032] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2014] [Accepted: 01/24/2015] [Indexed: 11/19/2022] Open
Abstract
We present a theory by which idealized models of auditory receptive fields can be derived in a principled axiomatic manner, from a set of structural properties to (i) enable invariance of receptive field responses under natural sound transformations and (ii) ensure internal consistency between spectro-temporal receptive fields at different temporal and spectral scales. For defining a time-frequency transformation of a purely temporal sound signal, it is shown that the framework allows for a new way of deriving the Gabor and Gammatone filters as well as a novel family of generalized Gammatone filters, with additional degrees of freedom to obtain different trade-offs between the spectral selectivity and the temporal delay of time-causal temporal window functions. When applied to the definition of a second-layer of receptive fields from a spectrogram, it is shown that the framework leads to two canonical families of spectro-temporal receptive fields, in terms of spectro-temporal derivatives of either spectro-temporal Gaussian kernels for non-causal time or a cascade of time-causal first-order integrators over the temporal domain and a Gaussian filter over the logspectral domain. For each filter family, the spectro-temporal receptive fields can be either separable over the time-frequency domain or be adapted to local glissando transformations that represent variations in logarithmic frequencies over time. Within each domain of either non-causal or time-causal time, these receptive field families are derived by uniqueness from the assumptions. It is demonstrated how the presented framework allows for computation of basic auditory features for audio processing and that it leads to predictions about auditory receptive fields with good qualitative similarity to biological receptive fields measured in the inferior colliculus (ICC) and primary auditory cortex (A1) of mammals.
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Affiliation(s)
- Tony Lindeberg
- Department of Computational Biology, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Anders Friberg
- Department of Speech, Music and Hearing, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden
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21
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King J, Insanally M, Jin M, Martins ARO, D'amour JA, Froemke RC. Rodent auditory perception: Critical band limitations and plasticity. Neuroscience 2015; 296:55-65. [PMID: 25827498 DOI: 10.1016/j.neuroscience.2015.03.053] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Revised: 03/20/2015] [Accepted: 03/22/2015] [Indexed: 10/23/2022]
Abstract
What do animals hear? While it remains challenging to adequately assess sensory perception in animal models, it is important to determine perceptual abilities in model systems to understand how physiological processes and plasticity relate to perception, learning, and cognition. Here we discuss hearing in rodents, reviewing previous and recent behavioral experiments querying acoustic perception in rats and mice, and examining the relation between behavioral data and electrophysiological recordings from the central auditory system. We focus on measurements of critical bands, which are psychoacoustic phenomena that seem to have a neural basis in the functional organization of the cochlea and the inferior colliculus. We then discuss how behavioral training, brain stimulation, and neuropathology impact auditory processing and perception.
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Affiliation(s)
- J King
- Skirball Institute for Biomolecular Medicine, New York University School of Medicine, New York, NY, USA; Department of Otolaryngology, New York University School of Medicine, New York, NY, USA; Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA; Center for Neural Science, New York University, New York, NY, USA
| | - M Insanally
- Skirball Institute for Biomolecular Medicine, New York University School of Medicine, New York, NY, USA; Department of Otolaryngology, New York University School of Medicine, New York, NY, USA; Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA; Center for Neural Science, New York University, New York, NY, USA
| | - M Jin
- Skirball Institute for Biomolecular Medicine, New York University School of Medicine, New York, NY, USA; Department of Otolaryngology, New York University School of Medicine, New York, NY, USA; Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA; Center for Neural Science, New York University, New York, NY, USA
| | - A R O Martins
- Skirball Institute for Biomolecular Medicine, New York University School of Medicine, New York, NY, USA; Department of Otolaryngology, New York University School of Medicine, New York, NY, USA; Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA; Center for Neural Science, New York University, New York, NY, USA; PhD Programme in Experimental Biology and Biomedicine, Center for Neurosciences and Cell Biology, University of Coimbra, Portugal
| | - J A D'amour
- Skirball Institute for Biomolecular Medicine, New York University School of Medicine, New York, NY, USA; Department of Otolaryngology, New York University School of Medicine, New York, NY, USA; Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA; Center for Neural Science, New York University, New York, NY, USA
| | - R C Froemke
- Skirball Institute for Biomolecular Medicine, New York University School of Medicine, New York, NY, USA; Department of Otolaryngology, New York University School of Medicine, New York, NY, USA; Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA; Center for Neural Science, New York University, New York, NY, USA.
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22
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Spectrotemporal response properties of core auditory cortex neurons in awake monkey. PLoS One 2015; 10:e0116118. [PMID: 25680187 PMCID: PMC4332665 DOI: 10.1371/journal.pone.0116118] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Accepted: 12/03/2014] [Indexed: 11/19/2022] Open
Abstract
So far, most studies of core auditory cortex (AC) have characterized the spectral and temporal tuning properties of cells in non-awake, anesthetized preparations. As experiments in awake animals are scarce, we here used dynamic spectral-temporal broadband ripples to study the properties of the spectrotemporal receptive fields (STRFs) of AC cells in awake monkeys. We show that AC neurons were typically most sensitive to low ripple densities (spectral) and low velocities (temporal), and that most cells were not selective for a particular spectrotemporal sweep direction. A substantial proportion of neurons preferred amplitude-modulated sounds (at zero ripple density) to dynamic ripples (at non-zero densities). The vast majority (>93%) of modulation transfer functions were separable with respect to spectral and temporal modulations, indicating that time and spectrum are independently processed in AC neurons. We also analyzed the linear predictability of AC responses to natural vocalizations on the basis of the STRF. We discuss our findings in the light of results obtained from the monkey midbrain inferior colliculus by comparing the spectrotemporal tuning properties and linear predictability of these two important auditory stages.
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23
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Escabí MA, Read HL, Viventi J, Kim DH, Higgins NC, Storace DA, Liu ASK, Gifford AM, Burke JF, Campisi M, Kim YS, Avrin AE, Spiegel Jan VD, Huang Y, Li M, Wu J, Rogers JA, Litt B, Cohen YE. A high-density, high-channel count, multiplexed μECoG array for auditory-cortex recordings. J Neurophysiol 2014; 112:1566-83. [PMID: 24920021 DOI: 10.1152/jn.00179.2013] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Our understanding of the large-scale population dynamics of neural activity is limited, in part, by our inability to record simultaneously from large regions of the cortex. Here, we validated the use of a large-scale active microelectrode array that simultaneously records 196 multiplexed micro-electrocortigraphical (μECoG) signals from the cortical surface at a very high density (1,600 electrodes/cm(2)). We compared μECoG measurements in auditory cortex using a custom "active" electrode array to those recorded using a conventional "passive" μECoG array. Both of these array responses were also compared with data recorded via intrinsic optical imaging, which is a standard methodology for recording sound-evoked cortical activity. Custom active μECoG arrays generated more veridical representations of the tonotopic organization of the auditory cortex than current commercially available passive μECoG arrays. Furthermore, the cortical representation could be measured efficiently with the active arrays, requiring as little as 13.5 s of neural data acquisition. Next, we generated spectrotemporal receptive fields from the recorded neural activity on the active μECoG array and identified functional organizational principles comparable to those observed using intrinsic metabolic imaging and single-neuron recordings. This new electrode array technology has the potential for large-scale, temporally precise monitoring and mapping of the cortex, without the use of invasive penetrating electrodes.
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Affiliation(s)
- Monty A Escabí
- Department of Psychology, University of Connecticut, Storrs, Connecticut; Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut; Department of Electrical Engineering, University of Connecticut, Storrs, Connecticut
| | - Heather L Read
- Department of Psychology, University of Connecticut, Storrs, Connecticut; Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut
| | - Jonathan Viventi
- Center for Neural Science, New York University, New York, New York; Department of Electrical and Computer Engineering, Polytechnic Institute of New York University, Brooklyn, New York
| | - Dae-Hyeong Kim
- Center for Nanoparticle Research of Institute for Basic Science, School of Chemical and Biological Engineering, Seoul National University, Seoul, Republic of Korea
| | - Nathan C Higgins
- Department of Psychology, University of Connecticut, Storrs, Connecticut
| | - Douglas A Storace
- Department of Psychology, University of Connecticut, Storrs, Connecticut
| | - Andrew S K Liu
- Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Adam M Gifford
- Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, Pennsylvania
| | - John F Burke
- Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Matthew Campisi
- Department of Electrical and Computer Engineering, Polytechnic Institute of New York University, Brooklyn, New York
| | - Yun-Soung Kim
- Department of Materials Science and Engineering, Beckman Institute for Advanced Science and Technology and Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Andrew E Avrin
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Van der Spiegel Jan
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yonggang Huang
- Departments of Mechanical Engineering and Civil and Environmental Engineering, Northwestern University, Evanston, Illinois
| | - Ming Li
- State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, China
| | - Jian Wu
- Department of Engineering Mechanics, Tsinghua University, Beijing, China
| | - John A Rogers
- Department of Materials Science and Engineering, Beckman Institute for Advanced Science and Technology and Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana-Champaign, Urbana, Illinois
| | - Brian Litt
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yale E Cohen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; and Department of Otorhinolaryngology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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Shamma S, Fritz J. Adaptive auditory computations. Curr Opin Neurobiol 2014; 25:164-8. [PMID: 24525107 DOI: 10.1016/j.conb.2014.01.011] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2013] [Revised: 01/17/2014] [Accepted: 01/20/2014] [Indexed: 11/26/2022]
Abstract
The auditory system analyses acoustic signals, extracting their perceptual attributes, and exploiting them to navigate complex auditory environments. While many of the basic transformations that give rise to the early auditory representations are well studied and understood, little is known about the latter cognitive functions that bind, organize, and give meaning to them. They include the ability to attend to, segregate, and track one of many sound sources, to learn its identity, commit it to memory, robustly recognize it, and utilize it to make decisions. This review hints at the profound adaptive influences and contextual effects induced by cognitive functions during these behaviors, and the need for robust tractable mathematical models to understand them.
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Affiliation(s)
- Shihab Shamma
- Department of Electrical and Computer Engineering, Institute for Systems Research, University of Maryland, College Park, United States; Department of Cognitive Studies, Ecole Normale Superieure, Paris, France.
| | - Jonathan Fritz
- Department of Electrical and Computer Engineering, Institute for Systems Research, University of Maryland, College Park, United States
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