Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density
Abstract This paper deals with the problem of inferring the signals and parameters that cause neural activity to occur. The ultimate challenge being to unveil brain’s connectivity, here we focus on a microscopic vision of the problem, where single neurons (potentially connected to a network of peers...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2017-09-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13634-017-0499-3 |
| _version_ | 1819237647432810496 |
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| author | Pau Closas Antoni Guillamon |
| author_facet | Pau Closas Antoni Guillamon |
| author_sort | Pau Closas |
| collection | DOAJ |
| description | Abstract This paper deals with the problem of inferring the signals and parameters that cause neural activity to occur. The ultimate challenge being to unveil brain’s connectivity, here we focus on a microscopic vision of the problem, where single neurons (potentially connected to a network of peers) are at the core of our study. The sole observation available are noisy, sampled voltage traces obtained from intracellular recordings. We design algorithms and inference methods using the tools provided by stochastic filtering that allow a probabilistic interpretation and treatment of the problem. Using particle filtering, we are able to reconstruct traces of voltages and estimate the time course of auxiliary variables. By extending the algorithm, through PMCMC methodology, we are able to estimate hidden physiological parameters as well, like intrinsic conductances or reversal potentials. Last, but not least, the method is applied to estimate synaptic conductances arriving at a target cell, thus reconstructing the synaptic excitatory/inhibitory input traces. Notably, the performance of these estimations achieve the theoretical lower bounds even in spiking regimes. |
| first_indexed | 2024-12-23T13:23:39Z |
| format | Article |
| id | doaj.art-25db4941d11e4d1caec2724f04f3c3b5 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-12-23T13:23:39Z |
| publishDate | 2017-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-25db4941d11e4d1caec2724f04f3c3b52022-12-21T17:45:23ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802017-09-012017112210.1186/s13634-017-0499-3Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance densityPau Closas0Antoni Guillamon1Northeastern UniversityUniversitat Politècnica de CatalunyaAbstract This paper deals with the problem of inferring the signals and parameters that cause neural activity to occur. The ultimate challenge being to unveil brain’s connectivity, here we focus on a microscopic vision of the problem, where single neurons (potentially connected to a network of peers) are at the core of our study. The sole observation available are noisy, sampled voltage traces obtained from intracellular recordings. We design algorithms and inference methods using the tools provided by stochastic filtering that allow a probabilistic interpretation and treatment of the problem. Using particle filtering, we are able to reconstruct traces of voltages and estimate the time course of auxiliary variables. By extending the algorithm, through PMCMC methodology, we are able to estimate hidden physiological parameters as well, like intrinsic conductances or reversal potentials. Last, but not least, the method is applied to estimate synaptic conductances arriving at a target cell, thus reconstructing the synaptic excitatory/inhibitory input traces. Notably, the performance of these estimations achieve the theoretical lower bounds even in spiking regimes.http://link.springer.com/article/10.1186/s13634-017-0499-3State-space modelsInference and learningParticle filteringSynaptic conductance estimationSpiking neuronConductance-based model |
| spellingShingle | Pau Closas Antoni Guillamon Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density EURASIP Journal on Advances in Signal Processing State-space models Inference and learning Particle filtering Synaptic conductance estimation Spiking neuron Conductance-based model |
| title | Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density |
| title_full | Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density |
| title_fullStr | Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density |
| title_full_unstemmed | Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density |
| title_short | Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density |
| title_sort | sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density |
| topic | State-space models Inference and learning Particle filtering Synaptic conductance estimation Spiking neuron Conductance-based model |
| url | http://link.springer.com/article/10.1186/s13634-017-0499-3 |
| work_keys_str_mv | AT pauclosas sequentialestimationofintrinsicactivityandsynapticinputinsingleneuronsbyparticlefilteringwithoptimalimportancedensity AT antoniguillamon sequentialestimationofintrinsicactivityandsynapticinputinsingleneuronsbyparticlefilteringwithoptimalimportancedensity |