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Calcini N, Silva Lantyer AD, Zeldenrust F, Celikel T. Nonlinear super-resolution signal processing allows intracellular tracking of calcium dynamics. J Neural Eng 2024; 21:036008. [PMID: 38648784 DOI: 10.1088/1741-2552/ad417c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 04/22/2024] [Indexed: 04/25/2024]
Abstract
Objective.Traditional quantification of fluorescence signals, such asΔF/F, relies on ratiometric measures that necessitate a baseline for comparison, limiting their applicability in dynamic analyses. Our goal here is to develop a baseline-independent method for analyzing fluorescence data that fully exploits temporal dynamics to introduce a novel approach for dynamical super-resolution analysis, including in subcellular resolution.Approach.We introduce ARES (Autoregressive RESiduals), a novel method that leverages the temporal aspect of fluorescence signals. By focusing on the quantification of residuals following linear autoregression, ARES obviates the need for a predefined baseline, enabling a more nuanced analysis of signal dynamics.Main result.We delineate the foundational attributes of ARES, illustrating its capability to enhance both spatial and temporal resolution of calcium fluorescence activity beyond the conventional ratiometric measure (ΔF/F). Additionally, we demonstrate ARES's utility in elucidating intracellular calcium dynamics through the detailed observation of calcium wave propagation within a dendrite.Significance.ARES stands out as a robust and precise tool for the quantification of fluorescence signals, adept at analyzing both spontaneous and evoked calcium dynamics. Its ability to facilitate the subcellular localization of calcium signals and the spatiotemporal tracking of calcium dynamics-where traditional ratiometric measures falter-underscores its potential to revolutionize baseline-independent analyses in the field.
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Affiliation(s)
- Niccolò Calcini
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, Nijmegen 6525 HJ, The Netherlands
| | - Angelica da Silva Lantyer
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, Nijmegen 6525 HJ, The Netherlands
| | - Fleur Zeldenrust
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, Nijmegen 6525 HJ, The Netherlands
| | - Tansu Celikel
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, Nijmegen 6525 HJ, The Netherlands
- School of Psychology, Georgia Institute of Technology, 654 Cherry Street, Atlanta, GA 30332, United States of America
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Abstract
With its six layers and ~ 12,000 neurons, a cortical column is a complex network whose function is plausibly greater than the sum of its constituents'. Functional characterization of its network components will require going beyond the brute-force modulation of the neural activity of a small group of neurons. Here we introduce an open-source, biologically inspired, computationally efficient network model of the somatosensory cortex's granular and supragranular layers after reconstructing the barrel cortex in soma resolution. Comparisons of the network activity to empirical observations showed that the in silico network replicates the known properties of touch representations and whisker deprivation-induced changes in synaptic strength induced in vivo. Simulations show that the history of the membrane potential acts as a spatial filter that determines the presynaptic population of neurons contributing to a post-synaptic action potential; this spatial filtering might be critical for synaptic integration of top-down and bottom-up information.
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Affiliation(s)
- Chao Huang
- grid.9647.c0000 0004 7669 9786Department of Biology, University of Leipzig, Leipzig, Germany
| | - Fleur Zeldenrust
- grid.5590.90000000122931605Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Tansu Celikel
- grid.5590.90000000122931605Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands ,grid.213917.f0000 0001 2097 4943School of Psychology, Georgia Institute of Technology, Atlanta, GA USA
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Zeldenrust F, Gutkin B, Denéve S. Efficient and robust coding in heterogeneous recurrent networks. PLoS Comput Biol 2021; 17:e1008673. [PMID: 33930016 PMCID: PMC8115785 DOI: 10.1371/journal.pcbi.1008673] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 05/12/2021] [Accepted: 04/07/2021] [Indexed: 11/19/2022] Open
Abstract
Cortical networks show a large heterogeneity of neuronal properties. However, traditional coding models have focused on homogeneous populations of excitatory and inhibitory neurons. Here, we analytically derive a class of recurrent networks of spiking neurons that close to optimally track a continuously varying input online, based on two assumptions: 1) every spike is decoded linearly and 2) the network aims to reduce the mean-squared error between the input and the estimate. From this we derive a class of predictive coding networks, that unifies encoding and decoding and in which we can investigate the difference between homogeneous networks and heterogeneous networks, in which each neurons represents different features and has different spike-generating properties. We find that in this framework, 'type 1' and 'type 2' neurons arise naturally and networks consisting of a heterogeneous population of different neuron types are both more efficient and more robust against correlated noise. We make two experimental predictions: 1) we predict that integrators show strong correlations with other integrators and resonators are correlated with resonators, whereas the correlations are much weaker between neurons with different coding properties and 2) that 'type 2' neurons are more coherent with the overall network activity than 'type 1' neurons.
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Affiliation(s)
- Fleur Zeldenrust
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Boris Gutkin
- Group for Neural Theory, INSERM U960, Département d’Études Cognitives, École Normal Supérieure PSL University, Paris, France
- Center for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia
| | - Sophie Denéve
- Group for Neural Theory, INSERM U960, Département d’Études Cognitives, École Normal Supérieure PSL University, Paris, France
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da Silva Lantyer A, Calcini N, Bijlsma A, Kole K, Emmelkamp M, Peeters M, Scheenen WJJ, Zeldenrust F, Celikel T. A databank for intracellular electrophysiological mapping of the adult somatosensory cortex. Gigascience 2018; 7:5232232. [PMID: 30521020 PMCID: PMC6302958 DOI: 10.1093/gigascience/giy147] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 11/18/2018] [Indexed: 02/04/2023] Open
Abstract
Background Neurons in the supragranular layers of the somatosensory cortex integrate sensory (bottom-up) and cognitive/perceptual (top-down) information as they orchestrate communication across cortical columns. It has been inferred, based on intracellular recordings from juvenile animals, that supragranular neurons are electrically mature by the fourth postnatal week. However, the dynamics of the neuronal integration in adulthood is largely unknown. Electrophysiological characterization of the active properties of these neurons throughout adulthood will help to address the biophysical and computational principles of the neuronal integration. Findings Here, we provide a database of whole-cell intracellular recordings from 315 neurons located in the supragranular layers (L2/3) of the primary somatosensory cortex in adult mice (9–45 weeks old) from both sexes (females, N = 195; males, N = 120). Data include 361 somatic current-clamp (CC) and 476 voltage-clamp (VC) experiments, recorded using a step-and-hold protocol (CC, N = 257; VC, N = 46), frozen noise injections (CC, N = 104) and triangular voltage sweeps (VC, 10 (N = 132), 50 (N = 146) and 100 ms (N = 152)), from regular spiking (N = 169) and fast-spiking neurons (N = 66). Conclusions The data can be used to systematically study the properties of somatic integration and the principles of action potential generation across sexes and across electrically characterized neuronal classes in adulthood. Understanding the principles of the somatic transformation of postsynaptic potentials into action potentials will shed light onto the computational principles of intracellular information transfer in single neurons and information processing in neuronal networks, helping to recreate neuronal functions in artificial systems.
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Affiliation(s)
- Angelica da Silva Lantyer
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Niccolò Calcini
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Ate Bijlsma
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Koen Kole
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Melanie Emmelkamp
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Manon Peeters
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Wim J J Scheenen
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Fleur Zeldenrust
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
| | - Tansu Celikel
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyedaalseweg 135, 6525 HJ, Nijmegen - the Netherlands
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Azarfar A, Calcini N, Huang C, Zeldenrust F, Celikel T. Neural coding: A single neuron's perspective. Neurosci Biobehav Rev 2018; 94:238-247. [PMID: 30227142 DOI: 10.1016/j.neubiorev.2018.09.007] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 08/27/2018] [Accepted: 09/07/2018] [Indexed: 12/15/2022]
Abstract
What any sensory neuron knows about the world is one of the cardinal questions in Neuroscience. Information from the sensory periphery travels across synaptically coupled neurons as each neuron encodes information by varying the rate and timing of its action potentials (spikes). Spatiotemporally correlated changes in this spiking regimen across neuronal populations are the neural basis of sensory representations. In the somatosensory cortex, however, spiking of individual (or pairs of) cortical neurons is only minimally informative about the world. Recent studies showed that one solution neurons implement to counteract this information loss is adapting their rate of information transfer to the ongoing synaptic activity by changing the membrane potential at which spike is generated. Here we first introduce the principles of information flow from the sensory periphery to the primary sensory cortex in a model sensory (whisker) system, and subsequently discuss how the adaptive spike threshold gates the intracellular information transfer from the somatic post-synaptic potential to action potentials, controlling the information content of communication across somatosensory cortical neurons.
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Affiliation(s)
- Alireza Azarfar
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour Radboud University, the Netherlands
| | - Niccoló Calcini
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour Radboud University, the Netherlands
| | - Chao Huang
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour Radboud University, the Netherlands
| | - Fleur Zeldenrust
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour Radboud University, the Netherlands
| | - Tansu Celikel
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour Radboud University, the Netherlands.
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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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Zeldenrust F, de Knecht S, Wadman WJ, Denève S, Gutkin B. Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series. Front Comput Neurosci 2017; 11:49. [PMID: 28663729 PMCID: PMC5471316 DOI: 10.3389/fncom.2017.00049] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 05/19/2017] [Indexed: 11/30/2022] Open
Abstract
Understanding the relation between (sensory) stimuli and the activity of neurons (i.e., “the neural code”) lies at heart of understanding the computational properties of the brain. However, quantifying the information between a stimulus and a spike train has proven to be challenging. We propose a new (in vitro) method to measure how much information a single neuron transfers from the input it receives to its output spike train. The input is generated by an artificial neural network that responds to a randomly appearing and disappearing “sensory stimulus”: the hidden state. The sum of this network activity is injected as current input into the neuron under investigation. The mutual information between the hidden state on the one hand and spike trains of the artificial network or the recorded spike train on the other hand can easily be estimated due to the binary shape of the hidden state. The characteristics of the input current, such as the time constant as a result of the (dis)appearance rate of the hidden state or the amplitude of the input current (the firing frequency of the neurons in the artificial network), can independently be varied. As an example, we apply this method to pyramidal neurons in the CA1 of mouse hippocampi and compare the recorded spike trains to the optimal response of the “Bayesian neuron” (BN). We conclude that like in the BN, information transfer in hippocampal pyramidal cells is non-linear and amplifying: the information loss between the artificial input and the output spike train is high if the input to the neuron (the firing of the artificial network) is not very informative about the hidden state. If the input to the neuron does contain a lot of information about the hidden state, the information loss is low. Moreover, neurons increase their firing rates in case the (dis)appearance rate is high, so that the (relative) amount of transferred information stays constant.
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Affiliation(s)
- Fleur Zeldenrust
- Department of Neurophysiology, Faculty of Science, Donders Institute for Brain, Cognition and Behaviour, Radboud UniversityNijmegen, Netherlands
| | - Sicco de Knecht
- Cellular and Systems Neurobiology, Swammerdam Institute for Life Sciences, University of AmsterdamAmsterdam, Netherlands
| | - Wytse J Wadman
- Cellular and Systems Neurobiology, Swammerdam Institute for Life Sciences, University of AmsterdamAmsterdam, Netherlands
| | - Sophie Denève
- Group for Neural Theory, Institut National de la Santé et de la Recherche Médicale U960, Institute of Cognitive Studies, École Normale SupérieureParis, France
| | - Boris Gutkin
- Group for Neural Theory, Institut National de la Santé et de la Recherche Médicale U960, Institute of Cognitive Studies, École Normale SupérieureParis, France.,Department of Psychology, Center for Cognition and Decision Making, National Research University Higher School of EconomicsMoscow, Russia
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Zeldenrust F, Chameau PJP, Wadman WJ. Reliability of spike and burst firing in thalamocortical relay cells. J Comput Neurosci 2013; 35:317-34. [PMID: 23708878 DOI: 10.1007/s10827-013-0454-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2012] [Accepted: 04/16/2013] [Indexed: 10/26/2022]
Abstract
The reliability and precision of the timing of spikes in a spike train is an important aspect of neuronal coding. We investigated reliability in thalamocortical relay (TCR) cells in the acute slice and also in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimposed on a DC current, was injected into the TCR cell soma. The neuron responded with spike trains that showed trial-to-trial variability, due to amongst others slow changes in its internal state and the experimental setup. The DC current allowed to bring the neuron in different states, characterized by a well defined membrane voltage (between -80 and -50 mV) and by a specific firing regime that on depolarization gradually shifted from a predominantly bursting regime to a tonic spiking regime. The filtered frozen white noise generated a spike pattern output with a broad spike interval distribution. The coincidence factor and the Hunter and Milton measure were used as reliability measures of the output spike train. In the experimental TCR cell as well as the Morris-Lecar model cell the reliability depends on the shape (steepness) of the current input versus spike frequency output curve. The model also allowed to study the contribution of three relevant ionic membrane currents to reliability: a T-type calcium current, a cation selective h-current and a calcium dependent potassium current in order to allow bursting, investigate the consequences of a more complex current-frequency relation and produce realistic firing rates. The reliability of the output of the TCR cell increases with depolarization. In hyperpolarized states bursts are more reliable than single spikes. The analytically derived relations were capable to predict several of the experimentally recorded spike features.
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Affiliation(s)
- Fleur Zeldenrust
- Swammerdam Institute for Life Sciences, Center for Neuroscience, University of Amsterdam, P.O. Box 94215, 1090, GE, Amsterdam, The Netherlands,
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Zeldenrust F, Wadman WJ. Modulation of spike and burst rate in a minimal neuronal circuit with feed-forward inhibition. Neural Netw 2013; 40:1-17. [DOI: 10.1016/j.neunet.2012.12.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2012] [Revised: 12/18/2012] [Accepted: 12/19/2012] [Indexed: 02/07/2023]
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Zeldenrust F, Wadman WJ. Inhibitory feedback in a small CA3-network. BMC Neurosci 2009. [DOI: 10.1186/1471-2202-10-s1-p172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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