000196103 001__ 196103
000196103 005__ 20181203040043.0
000196103 0247_ $$2doi$$a10.3389/fncir.2013.00201
000196103 022__ $$a1662-5110
000196103 02470 $$2ISI$$a000328753400001
000196103 037__ $$aARTICLE
000196103 245__ $$aInference of neuronal network spike dynamics and topology from calcium imaging data
000196103 269__ $$a2013
000196103 260__ $$aLausanne$$bFrontiers Research Foundation$$c2013
000196103 300__ $$a20
000196103 336__ $$aJournal Articles
000196103 520__ $$aTwo-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP) occurrence ("spike trains") from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR) and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties.
000196103 6531_ $$acalcium
000196103 6531_ $$aaction potential
000196103 6531_ $$areconstruction
000196103 6531_ $$aconnectivity
000196103 6531_ $$ascale-free
000196103 6531_ $$ahub neurons
000196103 700__ $$aLuetcke, Henry$$uUniv Zurich, Brain Res Inst, Lab Neural Circuit Dynam, CH-8057 Zurich, Switzerland
000196103 700__ $$aGerhard, Felipe$$uEcole Polytech Fed Lausanne, Sch Comp & Commun Sci, Lausanne, Switzerland
000196103 700__ $$0242042$$aZenke, Friedemann$$g194781$$uEcole Polytech Fed Lausanne, Sch Comp & Commun Sci, Lausanne, Switzerland
000196103 700__ $$0240007$$aGerstner, Wulfram$$g111732$$uEcole Polytech Fed Lausanne, Sch Comp & Commun Sci, Lausanne, Switzerland
000196103 700__ $$aHelmchen, Fritjof$$uUniv Zurich, Brain Res Inst, Lab Neural Circuit Dynam, CH-8057 Zurich, Switzerland
000196103 773__ $$j7$$tFrontiers In Neural Circuits
000196103 8564_ $$s1528277$$uhttps://infoscience.epfl.ch/record/196103/files/fncir-07-00201.pdf$$yn/a$$zn/a
000196103 909C0 $$0252006$$pLCN
000196103 909CO $$ooai:infoscience.tind.io:196103$$pIC$$particle$$pSV$$qGLOBAL_SET
000196103 917Z8 $$x180892
000196103 917Z8 $$x180892
000196103 937__ $$aEPFL-ARTICLE-196103
000196103 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000196103 980__ $$aARTICLE