Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics
Determining the structure of a network is of central importance to understanding its function in both neuroscience and applied mathematics. However, recovering the structural connectivity of neuronal networks remains a fundamental challenge both theoretically and experimentally. While neuronal netwo...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2019-10-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2019.01101/full |
| _version_ | 1828417975642750976 |
|---|---|
| author | Victor J. Barranca Douglas Zhou Douglas Zhou Douglas Zhou |
| author_facet | Victor J. Barranca Douglas Zhou Douglas Zhou Douglas Zhou |
| author_sort | Victor J. Barranca |
| collection | DOAJ |
| description | Determining the structure of a network is of central importance to understanding its function in both neuroscience and applied mathematics. However, recovering the structural connectivity of neuronal networks remains a fundamental challenge both theoretically and experimentally. While neuronal networks operate in certain dynamical regimes, which may influence their connectivity reconstruction, there is widespread experimental evidence of a balanced neuronal operating state in which strong excitatory and inhibitory inputs are dynamically adjusted such that neuronal voltages primarily remain near resting potential. Utilizing the dynamics of model neurons in such a balanced regime in conjunction with the ubiquitous sparse connectivity structure of neuronal networks, we develop a compressive sensing theoretical framework for efficiently reconstructing network connections by measuring individual neuronal activity in response to a relatively small ensemble of random stimuli injected over a short time scale. By tuning the network dynamical regime, we determine that the highest fidelity reconstructions are achievable in the balanced state. We hypothesize the balanced dynamics observed in vivo may therefore be a result of evolutionary selection for optimal information encoding and expect the methodology developed to be generalizable for alternative model networks as well as experimental paradigms. |
| first_indexed | 2024-12-10T14:30:07Z |
| format | Article |
| id | doaj.art-22cfb1b1bdea4c88a785a727b0be9b4c |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-10T14:30:07Z |
| publishDate | 2019-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-22cfb1b1bdea4c88a785a727b0be9b4c2022-12-22T01:44:58ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2019-10-011310.3389/fnins.2019.01101492216Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal DynamicsVictor J. Barranca0Douglas Zhou1Douglas Zhou2Douglas Zhou3Department of Mathematics and Statistics, Swarthmore College, Swarthmore, PA, United StatesSchool of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, ChinaMinistry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai, ChinaInstitute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, ChinaDetermining the structure of a network is of central importance to understanding its function in both neuroscience and applied mathematics. However, recovering the structural connectivity of neuronal networks remains a fundamental challenge both theoretically and experimentally. While neuronal networks operate in certain dynamical regimes, which may influence their connectivity reconstruction, there is widespread experimental evidence of a balanced neuronal operating state in which strong excitatory and inhibitory inputs are dynamically adjusted such that neuronal voltages primarily remain near resting potential. Utilizing the dynamics of model neurons in such a balanced regime in conjunction with the ubiquitous sparse connectivity structure of neuronal networks, we develop a compressive sensing theoretical framework for efficiently reconstructing network connections by measuring individual neuronal activity in response to a relatively small ensemble of random stimuli injected over a short time scale. By tuning the network dynamical regime, we determine that the highest fidelity reconstructions are achievable in the balanced state. We hypothesize the balanced dynamics observed in vivo may therefore be a result of evolutionary selection for optimal information encoding and expect the methodology developed to be generalizable for alternative model networks as well as experimental paradigms.https://www.frontiersin.org/article/10.3389/fnins.2019.01101/fullneuronal networksbalanced networkssignal processingnetwork dynamicsconnectivity reconstruction |
| spellingShingle | Victor J. Barranca Douglas Zhou Douglas Zhou Douglas Zhou Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics Frontiers in Neuroscience neuronal networks balanced networks signal processing network dynamics connectivity reconstruction |
| title | Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics |
| title_full | Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics |
| title_fullStr | Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics |
| title_full_unstemmed | Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics |
| title_short | Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics |
| title_sort | compressive sensing inference of neuronal network connectivity in balanced neuronal dynamics |
| topic | neuronal networks balanced networks signal processing network dynamics connectivity reconstruction |
| url | https://www.frontiersin.org/article/10.3389/fnins.2019.01101/full |
| work_keys_str_mv | AT victorjbarranca compressivesensinginferenceofneuronalnetworkconnectivityinbalancedneuronaldynamics AT douglaszhou compressivesensinginferenceofneuronalnetworkconnectivityinbalancedneuronaldynamics AT douglaszhou compressivesensinginferenceofneuronalnetworkconnectivityinbalancedneuronaldynamics AT douglaszhou compressivesensinginferenceofneuronalnetworkconnectivityinbalancedneuronaldynamics |