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Liu P, Bo K, Ding M, Fang R. Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.16.537079. [PMID: 37163104 PMCID: PMC10168209 DOI: 10.1101/2023.04.16.537079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that (1) in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and (2) lesioning these neurons by setting their output to 0 or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.
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Affiliation(s)
- Peng Liu
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Ke Bo
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Mingzhou Ding
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA
| | - Ruogu Fang
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
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Liu P, Bo K, Ding M, Fang R. Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects. PLoS Comput Biol 2024; 20:e1011943. [PMID: 38547053 PMCID: PMC10977720 DOI: 10.1371/journal.pcbi.1011943] [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: 05/29/2023] [Accepted: 02/24/2024] [Indexed: 04/02/2024] Open
Abstract
Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and lesioning these neurons by setting their output to zero or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.
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Affiliation(s)
- Peng Liu
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Ke Bo
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, United States of America
| | - Mingzhou Ding
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Ruogu Fang
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, Florida, United States of America
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Naumann LB, Keijser J, Sprekeler H. Invariant neural subspaces maintained by feedback modulation. eLife 2022; 11:76096. [PMID: 35442191 PMCID: PMC9106332 DOI: 10.7554/elife.76096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 04/06/2022] [Indexed: 11/13/2022] Open
Abstract
Sensory systems reliably process incoming stimuli in spite of changes in context. Most recent models accredit this context invariance to an extraction of increasingly complex sensory features in hierarchical feedforward networks. Here, we study how context-invariant representations can be established by feedback rather than feedforward processing. We show that feedforward neural networks modulated by feedback can dynamically generate invariant sensory representations. The required feedback can be implemented as a slow and spatially diffuse gain modulation. The invariance is not present on the level of individual neurons, but emerges only on the population level. Mechanistically, the feedback modulation dynamically reorients the manifold of neural activity and thereby maintains an invariant neural subspace in spite of contextual variations. Our results highlight the importance of population-level analyses for understanding the role of feedback in flexible sensory processing.
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Gandolfi D, Boiani GM, Bigiani A, Mapelli J. Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders. Int J Mol Sci 2021; 22:4565. [PMID: 33925434 PMCID: PMC8123833 DOI: 10.3390/ijms22094565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/22/2021] [Accepted: 04/25/2021] [Indexed: 02/06/2023] Open
Abstract
The investigation of synaptic functions remains one of the most fascinating challenges in the field of neuroscience and a large number of experimental methods have been tuned to dissect the mechanisms taking part in the neurotransmission process. Furthermore, the understanding of the insights of neurological disorders originating from alterations in neurotransmission often requires the development of (i) animal models of pathologies, (ii) invasive tools and (iii) targeted pharmacological approaches. In the last decades, additional tools to explore neurological diseases have been provided to the scientific community. A wide range of computational models in fact have been developed to explore the alterations of the mechanisms involved in neurotransmission following the emergence of neurological pathologies. Here, we review some of the advancements in the development of computational methods employed to investigate neuronal circuits with a particular focus on the application to the most diffuse neurological disorders.
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Affiliation(s)
- Daniela Gandolfi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
| | - Giulia Maria Boiani
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
| | - Albertino Bigiani
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy
| | - Jonathan Mapelli
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy
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Tang Y, An L, Yuan Y, Pei Q, Wang Q, Liu JK. Modulation of the dynamics of cerebellar Purkinje cells through the interaction of excitatory and inhibitory feedforward pathways. PLoS Comput Biol 2021; 17:e1008670. [PMID: 33566820 PMCID: PMC7909957 DOI: 10.1371/journal.pcbi.1008670] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 02/26/2021] [Accepted: 01/04/2021] [Indexed: 01/08/2023] Open
Abstract
The dynamics of cerebellar neuronal networks is controlled by the underlying building blocks of neurons and synapses between them. For which, the computation of Purkinje cells (PCs), the only output cells of the cerebellar cortex, is implemented through various types of neural pathways interactively routing excitation and inhibition converged to PCs. Such tuning of excitation and inhibition, coming from the gating of specific pathways as well as short-term plasticity (STP) of the synapses, plays a dominant role in controlling the PC dynamics in terms of firing rate and spike timing. PCs receive cascade feedforward inputs from two major neural pathways: the first one is the feedforward excitatory pathway from granule cells (GCs) to PCs; the second one is the feedforward inhibition pathway from GCs, via molecular layer interneurons (MLIs), to PCs. The GC-PC pathway, together with short-term dynamics of excitatory synapses, has been a focus over past decades, whereas recent experimental evidence shows that MLIs also greatly contribute to controlling PC activity. Therefore, it is expected that the diversity of excitation gated by STP of GC-PC synapses, modulated by strong inhibition from MLI-PC synapses, can promote the computation performed by PCs. However, it remains unclear how these two neural pathways are interacted to modulate PC dynamics. Here using a computational model of PC network installed with these two neural pathways, we addressed this question to investigate the change of PC firing dynamics at the level of single cell and network. We show that the nonlinear characteristics of excitatory STP dynamics can significantly modulate PC spiking dynamics mediated by inhibition. The changes in PC firing rate, firing phase, and temporal spike pattern, are strongly modulated by these two factors in different ways. MLIs mainly contribute to variable delays in the postsynaptic action potentials of PCs while modulated by excitation STP. Notably, the diversity of synchronization and pause response in the PC network is governed not only by the balance of excitation and inhibition, but also by the synaptic STP, depending on input burst patterns. Especially, the pause response shown in the PC network can only emerge with the interaction of both pathways. Together with other recent findings, our results show that the interaction of feedforward pathways of excitation and inhibition, incorporated with synaptic short-term dynamics, can dramatically regulate the PC activities that consequently change the network dynamics of the cerebellar circuit. It is well known that the dynamics of neuronal networks are controlled by various types of neural pathways that are interactively routing excitation and inhibition converged to postsynaptic neurons. In addition, gating of a specific neural pathway is enhanced by short-term plasticity of the synapses between neurons. However, it remains unclear how a combination of these factors, the strengths of excitation and inhibition, and their short-term dynamics respectively, contributes to the dynamics of single cells and neuronal networks. Using a network model of cerebellar Purkinje cells embedded with the feedforward excitatory pathway from granule cells and feedforward inhibition pathway of molecular layer interneurons. We show that the dynamics of firing rate, firing phase, and temporal spike pattern are notably yet differently modulated by these two pathways. At the single cell level, excitatory short-term plasticity nonlinearly modulates the input-output relationship of firing activity. At the network level, the diversity of synchronization and pause response is governed not only by the balance of excitation and inhibition, but also by synaptic short-term dynamics. Only when both neural pathways are incorporated, there is a strong pause response shown in the network. Our results, together with recent in vivo experimental observations in the cerebellum, show that the interaction of feedforward pathways of excitation and inhibition, together with synaptic short-term dynamics, can dramatically change the network dynamics of Purkinje cells.
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Affiliation(s)
- Yuanhong Tang
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Lingling An
- School of Computer Science and Technology, Xidian University, Xi’an, China
- * E-mail: (LA); (JKL)
| | - Ye Yuan
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Qingqi Pei
- School of Telecommunication Engineering, Xidian University, Xi’an, China
| | - Quan Wang
- School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Jian K. Liu
- Centre for Systems Neuroscience, Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, United Kingdom
- * E-mail: (LA); (JKL)
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Kalil CDA, de Castro MCS, Silva D, Cortez CM. Applying Graph Theory and Mathematical-Computational Modelling to Study a Neurophysiological Circuit. OPEN JOURNAL OF MODELLING AND SIMULATION 2021; 09:159-171. [DOI: 10.4236/ojmsi.2021.92011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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The Input-Output Relation of Primary Nociceptive Neurons is Determined by the Morphology of the Peripheral Nociceptive Terminals. J Neurosci 2020; 40:9346-9363. [PMID: 33115929 DOI: 10.1523/jneurosci.1546-20.2020] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 10/19/2020] [Accepted: 10/21/2020] [Indexed: 12/22/2022] Open
Abstract
The output from the peripheral terminals of primary nociceptive neurons, which detect and encode the information regarding noxious stimuli, is crucial in determining pain sensation. The nociceptive terminal endings are morphologically complex structures assembled from multiple branches of different geometry, which converge in a variety of forms to create the terminal tree. The output of a single terminal is defined by the properties of the transducer channels producing the generation potentials and voltage-gated channels, translating the generation potentials into action potential (AP) firing. However, in the majority of cases, noxious stimuli activate multiple terminals; thus, the output of the nociceptive neuron is defined by the integration and computation of the inputs of the individual terminals. Here, we used a computational model of nociceptive terminal tree to study how the architecture of the terminal tree affects the input-output relation of the primary nociceptive neurons. We show that the input-output properties of the nociceptive neurons depend on the length, the axial resistance (Ra), and location of individual terminals. Moreover, we show that activation of multiple terminals by a capsaicin-like current allows summation of the responses from individual terminals, thus leading to increased nociceptive output. Stimulation of the terminals in simulated models of inflammatory or neuropathic hyperexcitability led to a change in the temporal pattern of AP firing, emphasizing the role of temporal code in conveying key information about changes in nociceptive output in pathologic conditions, leading to pain hypersensitivity.SIGNIFICANCE STATEMENT Noxious stimuli are detected by terminal endings of primary nociceptive neurons, which are organized into morphologically complex terminal trees. The information from multiple terminals is integrated along the terminal tree, computing the neuronal output, which propagates toward the CNS, thus shaping the pain sensation. Here, we revealed that the structure of the nociceptive terminal tree determines the output of nociceptive neurons. We show that the integration of noxious information depends on the morphology of the terminal trees and how this integration and, consequently, the neuronal output change under pathologic conditions. Our findings help to predict how nociceptive neurons encode noxious stimuli and how this encoding changes in pathologic conditions, leading to pain.
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Poirazi P, Papoutsi A. Illuminating dendritic function with computational models. Nat Rev Neurosci 2020; 21:303-321. [PMID: 32393820 DOI: 10.1038/s41583-020-0301-7] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/25/2020] [Indexed: 02/06/2023]
Abstract
Dendrites have always fascinated researchers: from the artistic drawings by Ramon y Cajal to the beautiful recordings of today, neuroscientists have been striving to unravel the mysteries of these structures. Theoretical work in the 1960s predicted important dendritic effects on neuronal processing, establishing computational modelling as a powerful technique for their investigation. Since then, modelling of dendrites has been instrumental in driving neuroscience research in a targeted manner, providing experimentally testable predictions that range from the subcellular level to the systems level, and their relevance extends to fields beyond neuroscience, such as machine learning and artificial intelligence. Validation of modelling predictions often requires - and drives - new technological advances, thus closing the loop with theory-driven experimentation that moves the field forward. This Review features the most important, to our understanding, contributions of modelling of dendritic computations, including those pending experimental verification, and highlights studies of successful interactions between the modelling and experimental neuroscience communities.
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Affiliation(s)
- Panayiota Poirazi
- Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology - Hellas, Heraklion, Crete, Greece.
| | - Athanasia Papoutsi
- Institute of Molecular Biology & Biotechnology, Foundation for Research & Technology - Hellas, Heraklion, Crete, Greece
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Treatment with Mesenchymal-Derived Extracellular Vesicles Reduces Injury-Related Pathology in Pyramidal Neurons of Monkey Perilesional Ventral Premotor Cortex. J Neurosci 2020; 40:3385-3407. [PMID: 32241837 DOI: 10.1523/jneurosci.2226-19.2020] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 03/11/2020] [Accepted: 03/16/2020] [Indexed: 02/06/2023] Open
Abstract
Functional recovery after cortical injury, such as stroke, is associated with neural circuit reorganization, but the underlying mechanisms and efficacy of therapeutic interventions promoting neural plasticity in primates are not well understood. Bone marrow mesenchymal stem cell-derived extracellular vesicles (MSC-EVs), which mediate cell-to-cell inflammatory and trophic signaling, are thought be viable therapeutic targets. We recently showed, in aged female rhesus monkeys, that systemic administration of MSC-EVs enhances recovery of function after injury of the primary motor cortex, likely through enhancing plasticity in perilesional motor and premotor cortices. Here, using in vitro whole-cell patch-clamp recording and intracellular filling in acute slices of ventral premotor cortex (vPMC) from rhesus monkeys (Macaca mulatta) of either sex, we demonstrate that MSC-EVs reduce injury-related physiological and morphologic changes in perilesional layer 3 pyramidal neurons. At 14-16 weeks after injury, vPMC neurons from both vehicle- and EV-treated lesioned monkeys exhibited significant hyperexcitability and predominance of inhibitory synaptic currents, compared with neurons from nonlesioned control brains. However, compared with vehicle-treated monkeys, neurons from EV-treated monkeys showed lower firing rates, greater spike frequency adaptation, and excitatory:inhibitory ratio. Further, EV treatment was associated with greater apical dendritic branching complexity, spine density, and inhibition, indicative of enhanced dendritic plasticity and filtering of signals integrated at the soma. Importantly, the degree of EV-mediated reduction of injury-related pathology in vPMC was significantly correlated with measures of behavioral recovery. These data show that EV treatment dampens injury-related hyperexcitability and restores excitatory:inhibitory balance in vPMC, thereby normalizing activity within cortical networks for motor function.SIGNIFICANCE STATEMENT Neuronal plasticity can facilitate recovery of function after cortical injury, but the underlying mechanisms and efficacy of therapeutic interventions promoting this plasticity in primates are not well understood. Our recent work has shown that intravenous infusions of mesenchymal-derived extracellular vesicles (EVs) that are involved in cell-to-cell inflammatory and trophic signaling can enhance recovery of motor function after injury in monkey primary motor cortex. This study shows that this EV-mediated enhancement of recovery is associated with amelioration of injury-related hyperexcitability and restoration of excitatory-inhibitory balance in perilesional ventral premotor cortex. These findings demonstrate the efficacy of mesenchymal EVs as a therapeutic to reduce injury-related pathologic changes in the physiology and structure of premotor pyramidal neurons and support recovery of function.
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Mechanisms underlying gain modulation in the cortex. Nat Rev Neurosci 2020; 21:80-92. [PMID: 31911627 DOI: 10.1038/s41583-019-0253-y] [Citation(s) in RCA: 120] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/25/2019] [Indexed: 01/19/2023]
Abstract
Cortical gain regulation allows neurons to respond adaptively to changing inputs. Neural gain is modulated by internal and external influences, including attentional and arousal states, motor activity and neuromodulatory input. These influences converge to a common set of mechanisms for gain modulation, including GABAergic inhibition, synaptically driven fluctuations in membrane potential, changes in cellular conductance and changes in other biophysical neural properties. Recent work has identified GABAergic interneurons as targets of neuromodulatory input and mediators of state-dependent gain modulation. Here, we review the engagement and effects of gain modulation in the cortex. We highlight key recent findings that link phenomenological observations of gain modulation to underlying cellular and circuit-level mechanisms. Finally, we place these cellular and circuit interactions in the larger context of their impact on perception and cognition.
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Brunk MGK, Deane KE, Kisse M, Deliano M, Vieweg S, Ohl FW, Lippert MT, Happel MFK. Optogenetic stimulation of the VTA modulates a frequency-specific gain of thalamocortical inputs in infragranular layers of the auditory cortex. Sci Rep 2019; 9:20385. [PMID: 31892726 PMCID: PMC6938496 DOI: 10.1038/s41598-019-56926-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 12/16/2019] [Indexed: 12/22/2022] Open
Abstract
Reward associations during auditory learning induce cortical plasticity in the primary auditory cortex. A prominent source of such influence is the ventral tegmental area (VTA), which conveys a dopaminergic teaching signal to the primary auditory cortex. Yet, it is unknown, how the VTA influences cortical frequency processing and spectral integration. Therefore, we investigated the temporal effects of direct optogenetic stimulation of the VTA onto spectral integration in the auditory cortex on a synaptic circuit level by current-source-density analysis in anesthetized Mongolian gerbils. While auditory lemniscal input predominantly terminates in the granular input layers III/IV, we found that VTA-mediated modulation of spectral processing is relayed by a different circuit, namely enhanced thalamic inputs to the infragranular layers Vb/VIa. Activation of this circuit yields a frequency-specific gain amplification of local sensory input and enhances corticocortical information transfer, especially in supragranular layers I/II. This effects persisted over more than 30 minutes after VTA stimulation. Altogether, we demonstrate that the VTA exhibits a long-lasting influence on sensory cortical processing via infragranular layers transcending the signaling of a mere reward-prediction error. We thereby demonstrate a cellular and circuit substrate for the influence of reinforcement-evaluating brain systems on sensory processing in the auditory cortex.
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Affiliation(s)
- Michael G K Brunk
- Department of Systems Physiology of Learning, Leibniz Institute for Neurobiology, 39118, Magdeburg, Germany.
| | - Katrina E Deane
- Department of Systems Physiology of Learning, Leibniz Institute for Neurobiology, 39118, Magdeburg, Germany
| | - Martin Kisse
- Department of Systems Physiology of Learning, Leibniz Institute for Neurobiology, 39118, Magdeburg, Germany
| | - Matthias Deliano
- Department of Systems Physiology of Learning, Leibniz Institute for Neurobiology, 39118, Magdeburg, Germany
| | - Silvia Vieweg
- Department of Systems Physiology of Learning, Leibniz Institute for Neurobiology, 39118, Magdeburg, Germany
| | - Frank W Ohl
- Department of Systems Physiology of Learning, Leibniz Institute for Neurobiology, 39118, Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), 39106, Magdeburg, Germany
- Institute for Biology, Otto-von-Guericke-University, 39120, Magdeburg, Germany
| | - Michael T Lippert
- Department of Systems Physiology of Learning, Leibniz Institute for Neurobiology, 39118, Magdeburg, Germany
- Center for Behavioral Brain Sciences (CBBS), 39106, Magdeburg, Germany
| | - Max F K Happel
- Department of Systems Physiology of Learning, Leibniz Institute for Neurobiology, 39118, Magdeburg, Germany.
- Institute for Biology, Otto-von-Guericke-University, 39120, Magdeburg, Germany.
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Busch SE, Khakhalin AS. Intrinsic temporal tuning of neurons in the optic tectum is shaped by multisensory experience. J Neurophysiol 2019; 122:1084-1096. [PMID: 31291161 DOI: 10.1152/jn.00099.2019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
For a biological neural network to be functional, its neurons need to be connected with synapses of appropriate strength, and each neuron needs to appropriately respond to its synaptic inputs. This second aspect of network tuning is maintained by intrinsic plasticity; yet it is often considered secondary to changes in connectivity and mostly limited to adjustments of overall excitability of each neuron. Here we argue that even nonoscillatory neurons can be tuned to inputs of different temporal dynamics and that they can routinely adjust this tuning to match the statistics of their synaptic activation. Using the dynamic clamp technique, we show that, in the tectum of Xenopus tadpole, neurons become selective for faster inputs when animals are exposed to fast visual stimuli but remain responsive to longer inputs in animals exposed to slower, looming, or multisensory stimulation. We also report a homeostatic cotuning between synaptic and intrinsic temporal properties of individual tectal cells. These results expand our understanding of intrinsic plasticity in the brain and suggest that there may exist an additional dimension of network tuning that has been so far overlooked.NEW & NOTEWORTHY We use dynamic clamp to show that individual neurons in the tectum of Xenopus tadpoles are selectively tuned to either shorter (more synchronous) or longer (less synchronous) synaptic inputs. We also demonstrate that this intrinsic temporal tuning is strongly shaped by sensory experiences. This new phenomenon, which is likely to be mediated by changes in sodium channel inactivation, is bound to have important consequences for signal processing and the development of local recurrent connections.
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Affiliation(s)
- Silas E Busch
- Biology Program, Bard College, Annandale-on-Hudson, New York
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13
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Abdellah M, Hernando J, Eilemann S, Lapere S, Antille N, Markram H, Schürmann F. NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks. Bioinformatics 2019; 34:i574-i582. [PMID: 29949998 PMCID: PMC6022592 DOI: 10.1093/bioinformatics/bty231] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Motivation From image stacks to computational models, processing digital representations of neuronal morphologies is essential to neuroscientific research. Workflows involve various techniques and tools, leading in certain cases to convoluted and fragmented pipelines. The existence of an integrated, extensible and free framework for processing, analysis and visualization of those morphologies is a challenge that is still largely unfulfilled. Results We present NeuroMorphoVis, an interactive, extensible and cross-platform framework for building, visualizing and analyzing digital reconstructions of neuronal morphology skeletons extracted from microscopy stacks. Our framework is capable of detecting and repairing tracing artifacts, allowing the generation of high fidelity surface meshes and high resolution volumetric models for simulation and in silico imaging studies. The applicability of NeuroMorphoVis is demonstrated with two case studies. The first simulates the construction of three-dimensional profiles of neuronal somata and the other highlights how the framework is leveraged to create volumetric models of neuronal circuits for simulating different types of in vitro imaging experiments. Availability and implementation The source code and documentation are freely available on https://github.com/BlueBrain/NeuroMorphoVis under the GNU public license. The morphological analysis, visualization and surface meshing are implemented as an extensible Python API (Application Programming Interface) based on Blender, and the volume reconstruction and analysis code is written in C++ and parallelized using OpenMP. The framework features are accessible from a user-friendly GUI (Graphical User Interface) and a rich CLI (Command Line Interface). Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Marwan Abdellah
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Juan Hernando
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Stefan Eilemann
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Samuel Lapere
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Nicolas Antille
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Felix Schürmann
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
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Gupta N, Bansal H, Roy S. Theoretical optimization of high-frequency optogenetic spiking of red-shifted very fast-Chrimson-expressing neurons. NEUROPHOTONICS 2019; 6:025002. [PMID: 31001567 PMCID: PMC6458485 DOI: 10.1117/1.nph.6.2.025002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Accepted: 03/15/2019] [Indexed: 05/03/2023]
Abstract
A detailed theoretical analysis and optimization of high-fidelity, high-frequency firing of the red-shifted very-fast-Chrimson (vf-Chrimson) expressing neurons is presented. A four-state model for vf-Chrimson photocycle has been formulated and incorporated in Hodgkin-Huxley and Wang-Buzsaki spiking neuron circuit models. The effect of various parameters that include irradiance, pulse width, frequency, expression level, and membrane capacitance has been studied in detail. Theoretical simulations are in excellent agreement with recently reported experimental results. The analysis and optimization bring out additional interesting features. A minimal pulse width of 1.7 ms at 23 mW / mm 2 induces a peak photocurrent of 1250 pA. Optimal irradiance ( 0.1 mW / mm 2 ) and pulse width ( 50 μ s ) to trigger action potential have been determined. At frequencies beyond 200 Hz, higher values of expression level and irradiance result in spike failure. Singlet and doublet spiking fidelity can be maintained up to 400 and 150 Hz, respectively. The combination of expression level and membrane capacitance is a crucial factor to achieve high-frequency firing above 500 Hz. Its optimization enables 100% spike probability of up to 1 kHz. The study is useful in designing new high-frequency optogenetic neural spiking experiments with desired spatiotemporal resolution, by providing insights into the temporal spike coding, plasticity, and curing neurodegenerative diseases.
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Affiliation(s)
- Neha Gupta
- Dayalbagh Educational Institute, Department of Physics and Computer Science, Agra, India
| | - Himanshu Bansal
- Dayalbagh Educational Institute, Department of Physics and Computer Science, Agra, India
| | - Sukhdev Roy
- Dayalbagh Educational Institute, Department of Physics and Computer Science, Agra, India
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