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Budak M, Roberts MT, Grosh K, Corfas G, Booth V, Zochowski M. Binaural Processing Deficits Due to Synaptopathy and Myelin Defects. Front Neural Circuits 2022; 16:856926. [PMID: 35498371 PMCID: PMC9050145 DOI: 10.3389/fncir.2022.856926] [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: 01/17/2022] [Accepted: 03/23/2022] [Indexed: 11/13/2022] Open
Abstract
Hidden hearing loss (HHL) is a deficit in auditory perception and speech intelligibility that occurs despite normal audiometric thresholds and results from noise exposure, aging, or myelin defects. While mechanisms causing perceptual deficits in HHL patients are still unknown, results from animal models indicate a role for peripheral auditory neuropathies in HHL. In humans, sound localization is particularly important for comprehending speech, especially in noisy environments, and its disruption may contribute to HHL. In this study, we hypothesized that neuropathies of cochlear spiral ganglion neurons (SGNs) that are observed in animal models of HHL disrupt the activity of neurons in the medial superior olive (MSO), a nucleus in the brainstem responsible for locating low-frequency sound in the horizontal plane using binaural temporal cues, leading to sound localization deficits. To test our hypothesis, we constructed a network model of the auditory processing system that simulates peripheral responses to sound stimuli and propagation of responses via SGNs to cochlear nuclei and MSO populations. To simulate peripheral auditory neuropathies, we used a previously developed biophysical SGN model with myelin defects at SGN heminodes (myelinopathy) and with loss of inner hair cell-SGN synapses (synaptopathy). Model results indicate that myelinopathy and synaptopathy in SGNs give rise to decreased interaural time difference (ITD) sensitivity of MSO cells, suggesting a possible mechanism for perceptual deficits in HHL patients. This model may be useful to understand downstream impacts of SGN-mediated disruptions on auditory processing and to eventually discover possible treatments for various mechanisms of HHL.
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Affiliation(s)
- Maral Budak
- Biophysics Program, University of Michigan, Ann Arbor, MI, United States
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Michael T. Roberts
- Department of Otolaryngology Head and Neck Surgery, University of Michigan, Ann Arbor, MI, United States
- Kresge Hearing Research Institute, University of Michigan, Ann Arbor, MI, United States
| | - Karl Grosh
- Kresge Hearing Research Institute, University of Michigan, Ann Arbor, MI, United States
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Gabriel Corfas
- Department of Otolaryngology Head and Neck Surgery, University of Michigan, Ann Arbor, MI, United States
- Kresge Hearing Research Institute, University of Michigan, Ann Arbor, MI, United States
| | - Victoria Booth
- Department of Mathematics and Anesthesiology, University of Michigan, Ann Arbor, MI, United States
| | - Michal Zochowski
- Biophysics Program, University of Michigan, Ann Arbor, MI, United States
- Department of Physics, University of Michigan, Ann Arbor, MI, United States
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2
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Pena RFO, Rotstein HG. The voltage and spiking responses of subthreshold resonant neurons to structured and fluctuating inputs: persistence and loss of resonance and variability. BIOLOGICAL CYBERNETICS 2022; 116:163-190. [PMID: 35038010 DOI: 10.1007/s00422-021-00919-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 12/21/2021] [Indexed: 06/14/2023]
Abstract
We systematically investigate the response of neurons to oscillatory currents and synaptic-like inputs and we extend our investigation to non-structured synaptic-like spiking inputs with more realistic distributions of presynaptic spike times. We use two types of chirp-like inputs consisting of (i) a sequence of cycles with discretely increasing frequencies over time, and (ii) a sequence having the same cycles arranged in an arbitrary order. We develop and use a number of frequency-dependent voltage response metrics to capture the different aspects of the voltage response, including the standard impedance (Z) and the peak-to-trough amplitude envelope ([Formula: see text]) profiles. We show that Z-resonant cells (cells that exhibit subthreshold resonance in response to sinusoidal inputs) also show [Formula: see text]-resonance in response to sinusoidal inputs, but generally do not (or do it very mildly) in response to square-wave and synaptic-like inputs. In the latter cases the resonant response using Z is not predictive of the preferred frequencies at which the neurons spike when the input amplitude is increased above subthreshold levels. We also show that responses to conductance-based synaptic-like inputs are attenuated as compared to the response to current-based synaptic-like inputs, thus providing an explanation to previous experimental results. These response patterns were strongly dependent on the intrinsic properties of the participating neurons, in particular whether the unperturbed Z-resonant cells had a stable node or a focus. In addition, we show that variability emerges in response to chirp-like inputs with arbitrarily ordered patterns where all signals (trials) in a given protocol have the same frequency content and the only source of uncertainty is the subset of all possible permutations of cycles chosen for a given protocol. This variability is the result of the multiple different ways in which the autonomous transient dynamics is activated across cycles in each signal (different cycle orderings) and across trials. We extend our results to include high-rate Poisson distributed current- and conductance-based synaptic inputs and compare them with similar results using additive Gaussian white noise. We show that the responses to both Poisson-distributed synaptic inputs are attenuated with respect to the responses to Gaussian white noise. For cells that exhibit oscillatory responses to Gaussian white noise (band-pass filters), the response to conductance-based synaptic inputs are low-pass filters, while the response to current-based synaptic inputs may remain band-pass filters, consistent with experimental findings. Our results shed light on the mechanisms of communication of oscillatory activity among neurons in a network via subthreshold oscillations and resonance and the generation of network resonance.
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Affiliation(s)
- Rodrigo F O Pena
- Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers University, Newark, USA
| | - Horacio G Rotstein
- Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers University, Newark, USA.
- Corresponding Investigator, CONICET, Buenos Aires, Argentina.
- Graduate Faculty, Behavioral Neurosciences Program, Rutgers University, Newark, USA.
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3
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Li BZ, Pun SH, Vai MI, Lei TC, Klug A. Predicting the Influence of Axon Myelination on Sound Localization Precision Using a Spiking Neural Network Model of Auditory Brainstem. Front Neurosci 2022; 16:840983. [PMID: 35360169 PMCID: PMC8964079 DOI: 10.3389/fnins.2022.840983] [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: 12/21/2021] [Accepted: 02/18/2022] [Indexed: 01/12/2023] Open
Abstract
Spatial hearing allows animals to rapidly detect and localize auditory events in the surrounding environment. The auditory brainstem plays a central role in processing and extracting binaural spatial cues through microsecond-precise binaural integration, especially for detecting interaural time differences (ITDs) of low-frequency sounds at the medial superior olive (MSO). A series of mechanisms exist in the underlying neural circuits for preserving accurate action potential timing across multiple fibers, synapses and nuclei along this pathway. One of these is the myelination of afferent fibers that ensures reliable and temporally precise action potential propagation in the axon. There are several reports of fine-tuned myelination patterns in the MSO circuit, but how specifically myelination influences the precision of sound localization remains incompletely understood. Here we present a spiking neural network (SNN) model of the Mongolian gerbil auditory brainstem with myelinated axons to investigate whether different axon myelination thicknesses alter the sound localization process. Our model demonstrates that axon myelin thickness along the contralateral pathways can substantially modulate ITD detection. Furthermore, optimal ITD sensitivity is reached when the MSO receives contralateral inhibition via thicker myelinated axons compared to contralateral excitation, a result that is consistent with previously reported experimental observations. Our results suggest specific roles of axon myelination for extracting temporal dynamics in ITD decoding, especially in the pathway of the contralateral inhibition.
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Affiliation(s)
- Ben-Zheng Li
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States,Department of Electrical Engineering, University of Colorado, Denver, Denver, CO, United States,State Key Laboratory of Analog and Mixed Signal Very-Large-Scale Integration (VLSI), University of Macau, Taipa, Macau SAR, China,Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - Sio Hang Pun
- State Key Laboratory of Analog and Mixed Signal Very-Large-Scale Integration (VLSI), University of Macau, Taipa, Macau SAR, China
| | - Mang I. Vai
- State Key Laboratory of Analog and Mixed Signal Very-Large-Scale Integration (VLSI), University of Macau, Taipa, Macau SAR, China,Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - Tim C. Lei
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States,Department of Electrical Engineering, University of Colorado, Denver, Denver, CO, United States
| | - Achim Klug
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States,*Correspondence: Achim Klug,
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Koert E, Kuenzel T. Small dendritic synapses enhance temporal coding in a model of cochlear nucleus bushy cells. J Neurophysiol 2021; 125:915-937. [PMID: 33471627 DOI: 10.1152/jn.00331.2020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Spherical bushy cells (SBCs) in the anteroventral cochlear nucleus receive a single or very few powerful axosomatic inputs from the auditory nerve. However, SBCs are also contacted by small regular bouton synapses of the auditory nerve, located in their dendritic tree. The function of these small inputs is unknown. It was speculated that the interaction of axosomatic inputs with small dendritic inputs improved temporal precision, but direct evidence for this is missing. In a compartment model of spherical bushy cells with a stylized or realistic three-dimensional (3-D) representation of the bushy dendrite, we explored this hypothesis. Phase-locked dendritic inputs caused both tonic depolarization and a modulation of the model SBC membrane potential at the frequency of the stimulus. For plausible model parameters, dendritic inputs were subthreshold. Instead, the tonic depolarization increased the excitability of the SBC model and the modulation of the membrane potential caused a phase-dependent increase in the efficacy of the main axosomatic input. This improved response rate and entrainment for low-input frequencies and temporal precision of output at and above the characteristic frequency. A careful exploration of morphological and biophysical parameters of the bushy dendrite suggested a functional explanation for the peculiar shape of the bushy dendrite. Our model for the first time directly implied a role for the small excitatory dendritic inputs in auditory processing: they modulate the efficacy of the main input and are thus a plausible mechanism for the improvement of temporal precision and fidelity in these central auditory neurons.NEW & NOTEWORTHY We modeled dendritic inputs from the auditory nerve that spherical bushy cells of the cochlear nucleus receive. Dendritic inputs caused both tonic depolarization and modulation of the membrane potential at the input frequency. This improved the rate, entrainment, and temporal precision of output action potentials. Our simulations suggest a role for small dendritic inputs in auditory processing: they modulate the efficacy of the main input supporting temporal precision and fidelity in these central auditory neurons.
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Affiliation(s)
- Elisabeth Koert
- Auditory Neurophysiology Group, Department of Chemosensation, RWTH Aachen University, Aachen, Germany
| | - Thomas Kuenzel
- Auditory Neurophysiology Group, Department of Chemosensation, RWTH Aachen University, Aachen, Germany
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5
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Ma H, Jia B, Li Y, Gu H. Excitability and Threshold Mechanism for Enhanced Neuronal Response Induced by Inhibition Preceding Excitation. Neural Plast 2021; 2021:6692411. [PMID: 33531892 PMCID: PMC7837794 DOI: 10.1155/2021/6692411] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 12/02/2020] [Accepted: 01/06/2021] [Indexed: 11/18/2022] Open
Abstract
Postinhibitory facilitation (PIF) of neural firing presents a paradoxical phenomenon that the inhibitory effect induces enhancement instead of reduction of the firing activity, which plays important roles in sound location of the auditory nervous system, awaited theoretical explanations. In the present paper, excitability and threshold mechanism for the PIF phenomenon is presented in the Morris-Lecar model with type I, II, and III excitabilities. Firstly, compared with the purely excitatory stimulations applied to the steady state, the inhibitory preceding excitatory stimulation to form pairs induces the firing rate increased for type II and III excitabilities instead of type I excitability, when the interval between the inhibitory and excitatory stimulation within each pair is suitable. Secondly, the threshold mechanism for the PIF phenomenon is acquired. For type II and III excitabilities, the inhibitory stimulation induces subthreshold oscillations around the steady state. During the middle and ending phase of the ascending part and the beginning phase of the descending part within a period of the subthreshold oscillations, the threshold to evoke an action potential by an excitatory stimulation becomes weaker, which is the cause for the PIF phenomenon. Last, a theoretical estimation for the range of the interval between the inhibitory and excitatory stimulation for the PIF phenomenon is acquired, which approximates half of the intrinsic period of the subthreshold oscillations for the relatively strong stimulations and becomes narrower for the relatively weak stimulations. The interval for the PIF phenomenon is much shorter for type III excitability, which is closer to the experiment observation, due to the shorter period of the subthreshold oscillations. The results present the excitability and threshold mechanism for the PIF phenomenon, which provide comprehensive and deep explanations to the PIF phenomenon.
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Affiliation(s)
- Hanqing Ma
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China
| | - Bing Jia
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China
| | - Yuye Li
- College of Mathematics and Computer Science, Chifeng University, Chifeng 024000, China
| | - Huaguang Gu
- School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China
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6
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Rotstein HG, Nadim F. Frequency-dependent responses of neuronal models to oscillatory inputs in current versus voltage clamp. BIOLOGICAL CYBERNETICS 2019; 113:373-395. [PMID: 31286211 PMCID: PMC6689413 DOI: 10.1007/s00422-019-00802-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 06/27/2019] [Indexed: 06/09/2023]
Abstract
Action potential generation in neurons depends on a membrane potential threshold and therefore on how subthreshold inputs influence this voltage. In oscillatory networks, for example, many neuron types have been shown to produce membrane potential ([Formula: see text]) resonance: a maximum subthreshold response to oscillatory inputs at a nonzero frequency. Resonance is usually measured by recording [Formula: see text] in response to a sinusoidal current ([Formula: see text]), applied at different frequencies (f), an experimental setting known as current clamp (I-clamp). Several recent studies, however, use the voltage clamp (V-clamp) method to control [Formula: see text] with a sinusoidal input at different frequencies [[Formula: see text]] and measure the total membrane current ([Formula: see text]). The two methods obey systems of differential equations of different dimensionality, and while I-clamp provides a measure of electrical impedance [[Formula: see text]], V-clamp measures admittance [[Formula: see text]]. We analyze the relationship between these two measurement techniques. We show that, despite different dimensionality, in linear systems the two measures are equivalent: [Formula: see text]. However, nonlinear model neurons produce different values for Z and [Formula: see text]. In particular, nonlinearities in the voltage equation produce a much larger difference between these two quantities than those in equations of recovery variables that describe activation and inactivation kinetics. Neurons are inherently nonlinear, and notably, with ionic currents that amplify resonance, the voltage clamp technique severely underestimates the current clamp response. We demonstrate this difference experimentally using the PD neurons in the crab stomatogastric ganglion. These findings are instructive for researchers who explore cellular mechanisms of neuronal oscillations.
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Affiliation(s)
- Horacio G Rotstein
- Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers University, Newark, NJ, 07102, USA
- Institute for Brain and Neuroscience Research, New Jersey Institute of Technology, Newark, NJ, 07102, USA
- Behavioral and Neural Systems, Rutgers University, Newark, NJ, USA
- CONICET, Buenos Aires, Argentina
| | - Farzan Nadim
- Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers University, Newark, NJ, 07102, USA.
- Institute for Brain and Neuroscience Research, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
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7
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Rankin J, Rinzel J. Computational models of auditory perception from feature extraction to stream segregation and behavior. Curr Opin Neurobiol 2019; 58:46-53. [PMID: 31326723 DOI: 10.1016/j.conb.2019.06.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 06/22/2019] [Indexed: 10/26/2022]
Abstract
Audition is by nature dynamic, from brainstem processing on sub-millisecond time scales, to segregating and tracking sound sources with changing features, to the pleasure of listening to music and the satisfaction of getting the beat. We review recent advances from computational models of sound localization, of auditory stream segregation and of beat perception/generation. A wealth of behavioral, electrophysiological and imaging studies shed light on these processes, typically with synthesized sounds having regular temporal structure. Computational models integrate knowledge from different experimental fields and at different levels of description. We advocate a neuromechanistic modeling approach that incorporates knowledge of the auditory system from various fields, that utilizes plausible neural mechanisms, and that bridges our understanding across disciplines.
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Affiliation(s)
- James Rankin
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Rd, Exeter EX4 4QF, UK.
| | - John Rinzel
- Center for Neural Science, New York University, 4 Washington Place, 10003 New York, NY, United States; Courant Institute of Mathematical Sciences, New York University, 251 Mercer St, 10012 New York, NY, United States
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8
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Dewell RB, Gabbiani F. Active membrane conductances and morphology of a collision detection neuron broaden its impedance profile and improve discrimination of input synchrony. J Neurophysiol 2019; 122:691-706. [PMID: 31268830 DOI: 10.1152/jn.00048.2019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
How neurons filter and integrate their complex patterns of synaptic inputs is central to their role in neural information processing. Synaptic filtering and integration are shaped by the frequency-dependent neuronal membrane impedance. Using single and dual dendritic recordings in vivo, pharmacology, and computational modeling, we characterized the membrane impedance of a collision detection neuron in the grasshopper Schistocerca americana. This neuron, the lobula giant movement detector (LGMD), exhibits consistent impedance properties across frequencies and membrane potentials. Two common active conductances gH and gM, mediated respectively by hyperpolarization-activated cyclic nucleotide-gated (HCN) channels and by muscarine-sensitive M-type K+ channels, promote broadband integration with high temporal precision over the LGMD's natural range of membrane potentials and synaptic input frequencies. Additionally, we found that a model based on the LGMD's branching morphology increased the gain and decreased the delay associated with the mapping of synaptic input currents to membrane potential. More generally, this was true for a wide range of model neuron morphologies, including those of neocortical pyramidal neurons and cerebellar Purkinje cells. These findings show the unexpected role played by two widespread active conductances and by dendritic morphology in shaping synaptic integration.NEW & NOTEWORTHY Neuronal filtering and integration of synaptic input patterns depend on the electrochemical properties of dendrites. We used an identified collision detection neuron in grasshoppers to examine how its morphology and two conductances affect its membrane impedance in relation to the computations it performs. The neuronal properties examined are ubiquitous and therefore promote a general understanding of neuronal computations, including those in the human brain.
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Affiliation(s)
- Richard B Dewell
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas
| | - Fabrizio Gabbiani
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas.,Department of Electrical and Computer Engineering, Rice University, Houston, Texas
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9
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Membrane potential resonance in non-oscillatory neurons interacts with synaptic connectivity to produce network oscillations. J Comput Neurosci 2019; 46:169-195. [PMID: 30895410 DOI: 10.1007/s10827-019-00710-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 01/21/2019] [Accepted: 01/25/2019] [Indexed: 01/05/2023]
Abstract
Several neuron types have been shown to exhibit (subthreshold) membrane potential resonance (MPR), defined as the occurrence of a peak in their voltage amplitude response to oscillatory input currents at a preferred (resonant) frequency. MPR has been investigated both experimentally and theoretically. However, whether MPR is simply an epiphenomenon or it plays a functional role for the generation of neuronal network oscillations and how the latent time scales present in individual, non-oscillatory cells affect the properties of the oscillatory networks in which they are embedded are open questions. We address these issues by investigating a minimal network model consisting of (i) a non-oscillatory linear resonator (band-pass filter) with 2D dynamics, (ii) a passive cell (low-pass filter) with 1D linear dynamics, and (iii) nonlinear graded synaptic connections (excitatory or inhibitory) with instantaneous dynamics. We demonstrate that (i) the network oscillations crucially depend on the presence of MPR in the resonator, (ii) they are amplified by the network connectivity, (iii) they develop relaxation oscillations for high enough levels of mutual inhibition/excitation, and (iv) the network frequency monotonically depends on the resonators resonant frequency. We explain these phenomena using a reduced adapted version of the classical phase-plane analysis that helps uncovering the type of effective network nonlinearities that contribute to the generation of network oscillations. We extend our results to networks having cells with 2D dynamics. Our results have direct implications for network models of firing rate type and other biological oscillatory networks (e.g, biochemical, genetic).
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10
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Pena RFO, Ceballos CC, Lima V, Roque AC. Interplay of activation kinetics and the derivative conductance determines resonance properties of neurons. Phys Rev E 2018; 97:042408. [PMID: 29758644 DOI: 10.1103/physreve.97.042408] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Indexed: 11/07/2022]
Abstract
In a neuron with hyperpolarization activated current (I_{h}), the correct input frequency leads to an enhancement of the output response. This behavior is known as resonance and is well described by the neuronal impedance. In a simple neuron model we derive equations for the neuron's resonance and we link its frequency and existence with the biophysical properties of I_{h}. For a small voltage change, the component of the ratio of current change to voltage change (dI/dV) due to the voltage-dependent conductance change (dg/dV) is known as derivative conductance (G_{h}^{Der}). We show that both G_{h}^{Der} and the current activation kinetics (characterized by the activation time constant τ_{h}) are mainly responsible for controlling the frequency and existence of resonance. The increment of both factors (G_{h}^{Der} and τ_{h}) greatly contributes to the appearance of resonance. We also demonstrate that resonance is voltage dependent due to the voltage dependence of G_{h}^{Der}. Our results have important implications and can be used to predict and explain resonance properties of neurons with the I_{h} current.
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Affiliation(s)
- Rodrigo F O Pena
- Department of Physics, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil
| | - Cesar C Ceballos
- Department of Physics, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil.,Department of Physiology, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil
| | - Vinicius Lima
- Department of Physics, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil
| | - Antonio C Roque
- Department of Physics, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil
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11
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Encke J, Hemmert W. Extraction of Inter-Aural Time Differences Using a Spiking Neuron Network Model of the Medial Superior Olive. Front Neurosci 2018; 12:140. [PMID: 29559886 PMCID: PMC5845713 DOI: 10.3389/fnins.2018.00140] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 02/21/2018] [Indexed: 11/13/2022] Open
Abstract
The mammalian auditory system is able to extract temporal and spectral features from sound signals at the two ears. One important cue for localization of low-frequency sound sources in the horizontal plane are inter-aural time differences (ITDs) which are first analyzed in the medial superior olive (MSO) in the brainstem. Neural recordings of ITD tuning curves at various stages along the auditory pathway suggest that ITDs in the mammalian brainstem are not represented in form of a Jeffress-type place code. An alternative is the hemispheric opponent-channel code, according to which ITDs are encoded as the difference in the responses of the MSO nuclei in the two hemispheres. In this study, we present a physiologically-plausible, spiking neuron network model of the mammalian MSO circuit and apply two different methods of extracting ITDs from arbitrary sound signals. The network model is driven by a functional model of the auditory periphery and physiological models of the cochlear nucleus and the MSO. Using a linear opponent-channel decoder, we show that the network is able to detect changes in ITD with a precision down to 10 μs and that the sensitivity of the decoder depends on the slope of the ITD-rate functions. A second approach uses an artificial neuronal network to predict ITDs directly from the spiking output of the MSO and ANF model. Using this predictor, we show that the MSO-network is able to reliably encode static and time-dependent ITDs over a large frequency range, also for complex signals like speech.
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Affiliation(s)
- Jörg Encke
- Bioanaloge-Informationsverarbeitung, Department of Electrical and Computer Engineering, Technical University Munich, Munich, Germany
| | - Werner Hemmert
- Bioanaloge-Informationsverarbeitung, Department of Electrical and Computer Engineering, Technical University Munich, Munich, Germany
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12
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Fischer L, Leibold C, Felmy F. Resonance Properties in Auditory Brainstem Neurons. Front Cell Neurosci 2018; 12:8. [PMID: 29416503 PMCID: PMC5787568 DOI: 10.3389/fncel.2018.00008] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 01/08/2018] [Indexed: 11/13/2022] Open
Abstract
Auditory signals carry relevant information on a large range of time scales from below milliseconds to several seconds. Different stages in the auditory brainstem are specialized to extract information in specific frequency domains. One biophysical mechanism to facilitate frequency specific processing are membrane potential resonances. Here, we provide data from three different brainstem nuclei that all exhibit high-frequency subthreshold membrane resonances that are all most likely based on low-threshold potassium currents. Fitting a linear model, we argue that, as long as neurons possess active subthreshold channels, the main determinant for their resonance behavior is the steady state membrane time constant. Tuning this leak conductance can shift membrane resonance frequencies over more than a magnitude and therefore provide a flexible mechanism to tune frequency-specific auditory processing.
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Affiliation(s)
- Linda Fischer
- Zoologisches Institut, Stiftung Tierärztliche Hochschule Hannover, Hannover, Germany
| | - Christian Leibold
- Department Biologie II, Ludwig-Maximilians-Universität München, Munich, Germany.,Bernstein Center for Computational Neuroscience Munich, Munich, Germany
| | - Felix Felmy
- Zoologisches Institut, Stiftung Tierärztliche Hochschule Hannover, Hannover, Germany
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13
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Rotstein HG. Spiking resonances in models with the same slow resonant and fast amplifying currents but different subthreshold dynamic properties. J Comput Neurosci 2017; 43:243-271. [PMID: 29064059 DOI: 10.1007/s10827-017-0661-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Revised: 09/09/2017] [Accepted: 09/18/2017] [Indexed: 01/20/2023]
Abstract
The generation of spiking resonances in neurons (preferred spiking responses to oscillatory inputs) requires the interplay of the intrinsic ionic currents that operate at the subthreshold voltage level and the spiking mechanisms. Combinations of the same types of ionic currents in different parameter regimes may give rise to different types of nonlinearities in the voltage equation (e.g., parabolic- and cubic-like), generating subthreshold (membrane potential) oscillations patterns with different properties. These nonlinearities are not apparent in the model equations, but can be uncovered by plotting the voltage nullclines in the phase-plane diagram. We investigate the spiking resonant properties of conductance-based models that are biophysically equivalent at the subthreshold level (same ionic currents), but dynamically different (parabolic- and cubic-like voltage nullclines). As a case study we consider a model having a persistent sodium and a hyperpolarization-activated (h-) currents, which exhibits subthreshold resonance in the theta frequency band. We unfold the concept of spiking resonance into evoked and output spiking resonance. The former focuses on the input frequencies that are able to generate spikes, while the latter focuses on the output spiking frequencies regardless of the input frequency that generated these spikes. A cell can exhibit one or both types of resonances. We also measure spiking phasonance, which is an extension of subthreshold phasonance (zero-phase-shift response to oscillatory inputs) to the spiking regime. The subthreshold resonant properties of both types of models are communicated to the spiking regime for low enough input amplitudes as the voltage response for the subthreshold resonant frequency band raises above threshold. For higher input amplitudes evoked spiking resonance is no longer present in these models, but output spiking resonance is present primarily in the parabolic-like model due to a cycle skipping mechanism (involving mixed-mode oscillations), while the cubic-like model shows a better 1:1 entrainment. We use dynamical systems tools to explain the underlying mechanisms and the mechanistic differences between the resonance types. Our results demonstrate that the effective time scales that operate at the subthreshold regime to generate intrinsic subthreshold oscillations, mixed-mode oscillations and subthreshold resonance do not necessarily determine the existence of a preferred spiking response to oscillatory inputs in the same frequency band. The results discussed in this paper highlight both the complexity of the suprathreshold responses to oscillatory inputs in neurons having resonant and amplifying currents with different time scales and the fact that the identity of the participating ionic currents is not enough to predict the resulting patterns, but additional dynamic information, captured by the geometric properties of the phase-space diagram, is needed.
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Affiliation(s)
- Horacio G Rotstein
- Federated Department of Biological Sciences, Rutgers University and New Jersey Institute of Technology, Newark, NJ, 07102, USA. .,Institute for Brain and Neuroscience Research, New Jersey Institute of Technology, Newark, NJ, 07102, USA.
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Huguet G, Meng X, Rinzel J. Phasic Firing and Coincidence Detection by Subthreshold Negative Feedback: Divisive or Subtractive or, Better, Both. Front Comput Neurosci 2017; 11:3. [PMID: 28210218 PMCID: PMC5288357 DOI: 10.3389/fncom.2017.00003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Accepted: 01/16/2017] [Indexed: 11/26/2022] Open
Abstract
Phasic neurons typically fire only for a fast-rising input, say at the onset of a step current, but not for steady or slow inputs, a property associated with type III excitability. Phasic neurons can show extraordinary temporal precision for phase locking and coincidence detection. Exemplars are found in the auditory brain stem where precise timing is used in sound localization. Phasicness at the cellular level arises from a dynamic, voltage-gated, negative feedback that can be recruited subthreshold, preventing the neuron from reaching spike threshold if the voltage does not rise fast enough. We consider two mechanisms for phasicness: a low threshold potassium current (subtractive mechanism) and a sodium current with subthreshold inactivation (divisive mechanism). We develop and analyze three reduced models with either divisive or subtractive mechanisms or both to gain insight into the dynamical mechanisms for the potentially high temporal precision of type III-excitable neurons. We compare their firing properties and performance for a range of stimuli. The models have characteristic non-monotonic input-output relations, firing rate vs. input intensity, for either stochastic current injection or Poisson-timed excitatory synaptic conductance trains. We assess performance according to precision of phase-locking and coincidence detection by the models' responses to repetitive packets of unitary excitatory synaptic inputs with more or less temporal coherence. We find that each mechanism contributes features but best performance is attained if both are present. The subtractive mechanism confers extraordinary precision for phase locking and coincidence detection but only within a restricted parameter range when the divisive mechanism of sodium inactivation is inoperative. The divisive mechanism guarantees robustness of phasic properties, without compromising excitability, although with somewhat less precision. Finally, we demonstrate that brief transient inhibition if properly timed can enhance the reliability of firing.
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Affiliation(s)
- Gemma Huguet
- Departament de Matemàtiques, Universitat Politècnica de Catalunya Barcelona, Spain
| | - Xiangying Meng
- Biology Department, University of Maryland College Park, MD, USA
| | - John Rinzel
- Center for Neural Science, New York UniversityNew York, NY, USA; Courant Institute of Mathematical Sciences, New York UniversityNew York, NY, USA
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