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Hung TY, Wu SN, Huang CW. The Integrated Effects of Brivaracetam, a Selective Analog of Levetiracetam, on Ionic Currents and Neuronal Excitability. Biomedicines 2021; 9:biomedicines9040369. [PMID: 33916190 PMCID: PMC8067033 DOI: 10.3390/biomedicines9040369] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 03/17/2021] [Accepted: 03/26/2021] [Indexed: 12/22/2022] Open
Abstract
Brivaracetam (BRV) is recognized as a novel third-generation antiepileptic drug approved for the treatment of epilepsy. Emerging evidence has demonstrated that it has potentially better efficacy and tolerability than its analog, Levetiracetam (LEV). This, however, cannot be explained by their common synaptic vesicle-binding mechanism. Whether BRV can affect different ionic currents and concert these effects to alter neuronal excitability remains unclear. With the aid of patch clamp technology, we found that BRV concentration dependently inhibited the depolarization-induced M-type K+ current (IK(M)), decreased the delayed-rectifier K+ current (IK(DR)), and decreased the hyperpolarization-activated cation current in GH3 neurons. However, it had a concentration-dependent inhibition on voltage-gated Na+ current (INa). Under an inside-out patch configuration, a bath application of BRV increased the open probability of large-conductance Ca2+-activated K+ channels. Furthermore, in mHippoE-14 hippocampal neurons, the whole-cell INa was effectively depressed by BRV. In simulated modeling of hippocampal neurons, BRV was observed to reduce the firing of the action potentials (APs) concurrently with decreases in the AP amplitude. In animal models, BRV ameliorated acute seizures in both OD-1 and lithium-pilocarpine epilepsy models. However, LEV had effects in the latter only. Collectively, our study demonstrated BRV’s multiple ionic mechanism in electrically excitable cells and a potential concerted effect on neuronal excitability and hyperexcitability disorders.
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Affiliation(s)
- Te-Yu Hung
- Department of Pediatrics, Chi-Mei Medical Center, Tainan 71004, Taiwan;
| | - Sheng-Nan Wu
- Department of Physiology, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan
- Correspondence: (S.-N.W.); (C.-W.H.)
| | - Chin-Wei Huang
- Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan
- Correspondence: (S.-N.W.); (C.-W.H.)
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Depannemaecker D, Canton Santos LE, Rodrigues AM, Scorza CA, Scorza FA, Almeida ACGD. Realistic spiking neural network: Non-synaptic mechanisms improve convergence in cell assembly. Neural Netw 2019; 122:420-433. [PMID: 31841876 DOI: 10.1016/j.neunet.2019.09.038] [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] [Received: 02/22/2019] [Revised: 09/17/2019] [Accepted: 09/23/2019] [Indexed: 01/26/2023]
Abstract
Learning in neural networks inspired by brain tissue has been studied for machine learning applications. However, existing works primarily focused on the concept of synaptic weight modulation, and other aspects of neuronal interactions, such as non-synaptic mechanisms, have been neglected. Non-synaptic interaction mechanisms have been shown to play significant roles in the brain, and four classes of these mechanisms can be highlighted: (i) electrotonic coupling; (ii) ephaptic interactions; (iii) electric field effects; and iv) extracellular ionic fluctuations. In this work, we proposed simple rules for learning inspired by recent findings in machine learning adapted to a realistic spiking neural network. We show that the inclusion of non-synaptic interaction mechanisms improves cell assembly convergence. By including extracellular ionic fluctuation represented by the extracellular electrodiffusion in the network, we showed the importance of these mechanisms to improve cell assembly convergence. Additionally, we observed a variety of electrophysiological patterns of neuronal activity, particularly bursting and synchronism when the convergence is improved.
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Affiliation(s)
- Damien Depannemaecker
- Laboratório de Neurociência Experimental e Computacional, Departamento de Engenharia de Biossistemas, Universidade Federal de São João del-Rei (UFSJ), Brazil; Disciplina de Neurociência, Departamento de Neurologia e Neurocirurgia, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Luiz Eduardo Canton Santos
- Laboratório de Neurociência Experimental e Computacional, Departamento de Engenharia de Biossistemas, Universidade Federal de São João del-Rei (UFSJ), Brazil; Disciplina de Neurociência, Departamento de Neurologia e Neurocirurgia, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Antônio Márcio Rodrigues
- Laboratório de Neurociência Experimental e Computacional, Departamento de Engenharia de Biossistemas, Universidade Federal de São João del-Rei (UFSJ), Brazil
| | - Carla Alessandra Scorza
- Laboratório de Neurociência Experimental e Computacional, Departamento de Engenharia de Biossistemas, Universidade Federal de São João del-Rei (UFSJ), Brazil
| | - Fulvio Alexandre Scorza
- Disciplina de Neurociência, Departamento de Neurologia e Neurocirurgia, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Antônio-Carlos Guimarães de Almeida
- Laboratório de Neurociência Experimental e Computacional, Departamento de Engenharia de Biossistemas, Universidade Federal de São João del-Rei (UFSJ), Brazil.
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Willems JGP, Wadman WJ, Cappaert NLM. Excitation-Inhibition Dynamics Regulate Activity Transmission Through the Perirhinal-Entorhinal Network. Neuroscience 2019; 411:222-236. [PMID: 31132396 DOI: 10.1016/j.neuroscience.2019.05.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 04/05/2019] [Accepted: 05/17/2019] [Indexed: 10/26/2022]
Abstract
The perirhinal (PER) - lateral entorhinal (LEC) network plays a pivotal role in the information transfer between the neocortex and the hippocampus. Anatomical studies have shown that the connectivity is organized bi-directionally: the superficial layers consist of projections running from the neocortex via the PER-LEC network to the hippocampus while the deep layers form the output pathway back to the neocortex. Although these pathways are characterized anatomically, the functional organization of the superficial and deep connections in the PER-LEC network remains to be revealed. We performed paired recordings of superficial and deep layer principal neurons and found that a larger population of superficial neurons responded with action potential firing in response to superficial cortical input, compared to the deep layer population. This suggested that the superficial network can carry information from the cortex towards the hippocampus. The relation between the excitatory and inhibitory input onto the deep and superficial principal neurons showed that the window of net excitability was larger in superficial principal neurons. We performed paired recordings in superficial layer principal neurons and parvalbumin (PV) expressing interneurons to address how this window of opportunity for spiking is affected in superficial principal neurons. The PV interneuron population initiated inhibition at a very consistent timing with increasing stimulus intensity, whereas the excitation temporally shifted to ensure action potential firing. These data indicate that superficial principal neurons can transmit cortical synaptic input through the PER-LEC network because these neurons have a favorable window of opportunity for spiking in contrast to deep neurons.
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Affiliation(s)
- Janske G P Willems
- Center for NeuroScience, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, the Netherlands
| | - Wytse J Wadman
- Center for NeuroScience, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, the Netherlands
| | - Natalie L M Cappaert
- Center for NeuroScience, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, the Netherlands.
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Zeldenrust F, Wadman WJ, Englitz B. Neural Coding With Bursts-Current State and Future Perspectives. Front Comput Neurosci 2018; 12:48. [PMID: 30034330 PMCID: PMC6043860 DOI: 10.3389/fncom.2018.00048] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 06/06/2018] [Indexed: 12/11/2022] Open
Abstract
Neuronal action potentials or spikes provide a long-range, noise-resistant means of communication between neurons. As point processes single spikes contain little information in themselves, i.e., outside the context of spikes from other neurons. Moreover, they may fail to cross a synapse. A burst, which consists of a short, high frequency train of spikes, will more reliably cross a synapse, increasing the likelihood of eliciting a postsynaptic spike, depending on the specific short-term plasticity at that synapse. Both the number and the temporal pattern of spikes in a burst provide a coding space that lies within the temporal integration realm of single neurons. Bursts have been observed in many species, including the non-mammalian, and in brain regions that range from subcortical to cortical. Despite their widespread presence and potential relevance, the uncertainties of how to classify bursts seems to have limited the research into the coding possibilities for bursts. The present series of research articles provides new insights into the relevance and interpretation of bursts across different neural circuits, and new methods for their analysis. Here, we provide a succinct introduction to the history of burst coding and an overview of recent work on this topic.
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Affiliation(s)
- Fleur Zeldenrust
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Wytse J Wadman
- Cellular and Systems Neurobiology Lab, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Bernhard Englitz
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
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Spike and burst coding in thalamocortical relay cells. PLoS Comput Biol 2018; 14:e1005960. [PMID: 29432418 PMCID: PMC5834212 DOI: 10.1371/journal.pcbi.1005960] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Revised: 03/02/2018] [Accepted: 01/08/2018] [Indexed: 11/19/2022] Open
Abstract
Mammalian thalamocortical relay (TCR) neurons switch their firing activity between a tonic spiking and a bursting regime. In a combined experimental and computational study, we investigated the features in the input signal that single spikes and bursts in the output spike train represent and how this code is influenced by the membrane voltage state of the neuron. Identical frozen Gaussian noise current traces were injected into TCR neurons in rat brain slices as well as in a validated three-compartment TCR model cell. The resulting membrane voltage traces and spike trains were analyzed by calculating the coherence and impedance. Reverse correlation techniques gave the Event-Triggered Average (ETA) and the Event-Triggered Covariance (ETC). This demonstrated that the feature selectivity started relatively long before the events (up to 300 ms) and showed a clear distinction between spikes (selective for fluctuations) and bursts (selective for integration). The model cell was fine-tuned to mimic the frozen noise initiated spike and burst responses to within experimental accuracy, especially for the mixed mode regimes. The information content carried by the various types of events in the signal as well as by the whole signal was calculated. Bursts phase-lock to and transfer information at lower frequencies than single spikes. On depolarization the neuron transits smoothly from the predominantly bursting regime to a spiking regime, in which it is more sensitive to high-frequency fluctuations. The model was then used to elucidate properties that could not be assessed experimentally, in particular the role of two important subthreshold voltage-dependent currents: the low threshold activated calcium current (IT) and the cyclic nucleotide modulated h current (Ih). The ETAs of those currents and their underlying activation/inactivation states not only explained the state dependence of the firing regime but also the long-lasting concerted dynamic action of the two currents. Finally, the model was used to investigate the more realistic “high-conductance state”, where fluctuations are caused by (synaptic) conductance changes instead of current injection. Under “standard” conditions bursts are difficult to initiate, given the high degree of inactivation of the T-type calcium current. Strong and/or precisely timed inhibitory currents were able to remove this inactivation. Neurons in the brain respond to (sensory) stimuli by generating electrical pulses called ‘spikes’ or ‘action potentials’. Spikes are organized in different temporal patterns, such as ‘bursts’ in which they occur at a high frequency followed by a period of silence. Bursts are ubiquitous in the nervous system: they occur in different parts of the brain and in different species. Different mechanisms that generate them have been pointed out. Why the nervous system uses bursts in its communication, or what type of information is represented by bursts, remains largely unknown. Here, we looked at bursting in thalamocortical relay (TCR) cells, neurons that form a bridge between early sensory processing and higher-order structures (cortex). These cells fire bursts as a result of the activation of two distinct subthreshold ionic currents: the T-type calcium current and the h-type current. We investigated experimentally and computationally what features in the input makes TCR cells respond with bursts, and what features with single spikes. Bursts are a response to low-frequency slowly increasing input; single spikes are a response to faster fluctuations. Moreover, bursts are rare and highly informative, in line with an earlier hypothesis that bursts could play a ‘wake-up call’ role in the nervous system.
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Willems JGP, Wadman WJ, Cappaert NLM. Parvalbumin interneuron mediated feedforward inhibition controls signal output in the deep layers of the perirhinal-entorhinal cortex. Hippocampus 2018; 28:281-296. [PMID: 29341361 PMCID: PMC5900730 DOI: 10.1002/hipo.22830] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Revised: 01/03/2018] [Accepted: 01/08/2018] [Indexed: 11/11/2022]
Abstract
The perirhinal (PER) and lateral entorhinal (LEC) cortex form an anatomical link between the neocortex and the hippocampus. However, neocortical activity is transmitted through the PER and LEC to the hippocampus with a low probability, suggesting the involvement of the inhibitory network. This study explored the role of interneuron mediated inhibition, activated by electrical stimulation in the agranular insular cortex (AiP), in the deep layers of the PER and LEC. Activated synaptic input by AiP stimulation rarely evoked action potentials in the PER‐LEC deep layer excitatory principal neurons, most probably because the evoked synaptic response consisted of a small excitatory and large inhibitory conductance. Furthermore, parvalbumin positive (PV) interneurons—a subset of interneurons projecting onto the axo‐somatic region of principal neurons—received synaptic input earlier than principal neurons, suggesting recruitment of feedforward inhibition. This synaptic input in PV interneurons evoked varying trains of action potentials, explaining the fast rising, long lasting synaptic inhibition received by deep layer principal neurons. Altogether, the excitatory input from the AiP onto deep layer principal neurons is overruled by strong feedforward inhibition. PV interneurons, with their fast, extensive stimulus‐evoked firing, are able to deliver this fast evoked inhibition in principal neurons. This indicates an essential role for PV interneurons in the gating mechanism of the PER‐LEC network.
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Affiliation(s)
- Janske G P Willems
- Center for Neuroscience, Sammerdam Institute for Life Sciences, University of Amsterdam, SciencePark 904, Amsterdam 1098 XH, The Netherlands
| | - Wytse J Wadman
- Center for Neuroscience, Sammerdam Institute for Life Sciences, University of Amsterdam, SciencePark 904, Amsterdam 1098 XH, The Netherlands
| | - Natalie L M Cappaert
- Center for Neuroscience, Sammerdam Institute for Life Sciences, University of Amsterdam, SciencePark 904, Amsterdam 1098 XH, The Netherlands
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Satuvuori E, Mulansky M, Bozanic N, Malvestio I, Zeldenrust F, Lenk K, Kreuz T. Measures of spike train synchrony for data with multiple time scales. J Neurosci Methods 2017; 287:25-38. [PMID: 28583477 PMCID: PMC5508708 DOI: 10.1016/j.jneumeth.2017.05.028] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2017] [Revised: 05/04/2017] [Accepted: 05/30/2017] [Indexed: 10/29/2022]
Abstract
BACKGROUND Measures of spike train synchrony are widely used in both experimental and computational neuroscience. Time-scale independent and parameter-free measures, such as the ISI-distance, the SPIKE-distance and SPIKE-synchronization, are preferable to time scale parametric measures, since by adapting to the local firing rate they take into account all the time scales of a given dataset. NEW METHOD In data containing multiple time scales (e.g. regular spiking and bursts) one is typically less interested in the smallest time scales and a more adaptive approach is needed. Here we propose the A-ISI-distance, the A-SPIKE-distance and A-SPIKE-synchronization, which generalize the original measures by considering the local relative to the global time scales. For the A-SPIKE-distance we also introduce a rate-independent extension called the RIA-SPIKE-distance, which focuses specifically on spike timing. RESULTS The adaptive generalizations A-ISI-distance and A-SPIKE-distance allow to disregard spike time differences that are not relevant on a more global scale. A-SPIKE-synchronization does not any longer demand an unreasonably high accuracy for spike doublets and coinciding bursts. Finally, the RIA-SPIKE-distance proves to be independent of rate ratios between spike trains. COMPARISON WITH EXISTING METHODS We find that compared to the original versions the A-ISI-distance and the A-SPIKE-distance yield improvements for spike trains containing different time scales without exhibiting any unwanted side effects in other examples. A-SPIKE-synchronization matches spikes more efficiently than SPIKE-synchronization. CONCLUSIONS With these proposals we have completed the picture, since we now provide adaptive generalized measures that are sensitive to firing rate only (A-ISI-distance), to timing only (ARI-SPIKE-distance), and to both at the same time (A-SPIKE-distance).
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Affiliation(s)
- Eero Satuvuori
- Institute for Complex Systems, CNR, Sesto Fiorentino, Italy; Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy; MOVE Research Institute, Department of Human Movement Sciences, Vrije Universiteit Amsterdam, The Netherlands.
| | - Mario Mulansky
- Institute for Complex Systems, CNR, Sesto Fiorentino, Italy.
| | - Nebojsa Bozanic
- Institute for Complex Systems, CNR, Sesto Fiorentino, Italy.
| | - Irene Malvestio
- Institute for Complex Systems, CNR, Sesto Fiorentino, Italy; Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy; Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Fleur Zeldenrust
- Donders Institute for Brain Cognition and Behaviour, Radboud Universiteit, Nijmegen, The Netherlands.
| | - Kerstin Lenk
- BioMediTech, Tampere University of Technology, Tampere, Finland; DFG-Center for Regenerative Therapies Dresden, Technische Universität Dresden, Dresden, Germany.
| | - Thomas Kreuz
- Institute for Complex Systems, CNR, Sesto Fiorentino, Italy.
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Mease RA, Kuner T, Fairhall AL, Groh A. Multiplexed Spike Coding and Adaptation in the Thalamus. Cell Rep 2017; 19:1130-1140. [PMID: 28494863 PMCID: PMC5554799 DOI: 10.1016/j.celrep.2017.04.050] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 03/18/2017] [Accepted: 04/18/2017] [Indexed: 11/26/2022] Open
Abstract
High-frequency "burst" clusters of spikes are a generic output pattern of many neurons. While bursting is a ubiquitous computational feature of different nervous systems across animal species, the encoding of synaptic inputs by bursts is not well understood. We find that bursting neurons in the rodent thalamus employ "multiplexing" to differentially encode low- and high-frequency stimulus features associated with either T-type calcium "low-threshold" or fast sodium spiking events, respectively, and these events adapt differently. Thus, thalamic bursts encode disparate information in three channels: (1) burst size, (2) burst onset time, and (3) precise spike timing within bursts. Strikingly, this latter "intraburst" encoding channel shows millisecond-level feature selectivity and adapts across statistical contexts to maintain stable information encoded per spike. Consequently, calcium events both encode low-frequency stimuli and, in parallel, gate a transient window for high-frequency, adaptive stimulus encoding by sodium spike timing, allowing bursts to efficiently convey fine-scale temporal information.
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Affiliation(s)
- Rebecca A Mease
- Department of Neurosurgery, Technische Universität München, Munich 81675, Germany; Neurobiology and Behavior Graduate Program, University of Washington, Seattle, WA 98195, USA.
| | - Thomas Kuner
- Department of Functional Neuroanatomy, Heidelberg University, Heidelberg 69120, Germany
| | - Adrienne L Fairhall
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Alexander Groh
- Department of Neurosurgery, Technische Universität München, Munich 81675, Germany
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Mathematical modeling of subthreshold resonant properties in pyloric dilator neurons. BIOMED RESEARCH INTERNATIONAL 2015; 2015:135787. [PMID: 25960999 PMCID: PMC4415491 DOI: 10.1155/2015/135787] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2014] [Accepted: 02/27/2015] [Indexed: 11/18/2022]
Abstract
Various types of neurons exhibit subthreshold resonance oscillation (preferred frequency response) to fluctuating sinusoidal input currents. This phenomenon is well known to influence the synaptic plasticity and frequency of neural network oscillation. This study evaluates the resonant properties of pacemaker pyloric dilator (PD) neurons in the central pattern generator network through mathematical modeling. From the pharmacological point of view, calcium currents cannot be blocked in PD neurons without removing the calcium-dependent potassium current. Thus, the effects of calcium (ICa) and calcium-dependent potassium (IKCa) currents on resonant properties remain unclear. By taking advantage of Hodgkin-Huxley-type model of neuron and its equivalent RLC circuit, we examine the effects of changing resting membrane potential and those ionic currents on the resonance. Results show that changing the resting membrane potential influences the amplitude and frequency of resonance so that the strength of resonance (Q-value) increases by both depolarization and hyperpolarization of the resting membrane potential. Moreover, hyperpolarization-activated inward current (Ih) and ICa (in association with IKCa) are dominant factors on resonant properties at hyperpolarized and depolarized potentials, respectively. Through mathematical analysis, results indicate that Ih and IKCa affect the resonant properties of PD neurons. However, ICa only has an amplifying effect on the resonance amplitude of these neurons.
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