A Point Process Model for Auditory Neurons Considering Both Their Intrinsic Dynamics and the Spectrotemporal Properties of an Extrinsic Signal
We propose a point process model of spiking activity from auditory neurons. The model takes account of the neuron's intrinsic dynamics as well as the spectrotemporal properties of an input stimulus. A discrete Volterra expansion is used to derive the form of the conditional intensity function....
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2014
|
| Online Access: | http://hdl.handle.net/1721.1/86313 https://orcid.org/0000-0003-2668-7819 https://orcid.org/0000-0003-1349-9608 |
| Summary: | We propose a point process model of spiking activity from auditory neurons. The model takes account of the neuron's intrinsic dynamics as well as the spectrotemporal properties of an input stimulus. A discrete Volterra expansion is used to derive the form of the conditional intensity function. The Volterra expansion models the neuron's baseline spike rate, its intrinsic dynamics-spiking history-and the stimulus effect which in this case is the analog of the spectrotemporal receptive field (STRF). We performed the model fitting efficiently in a generalized linear model framework using ridge regression to address properly this ill-posed maximum likelihood estimation problem. The model provides an excellent fit to spiking activity from 55 auditory nerve neurons. The STRF-like representation estimated jointly with the neuron's intrinsic dynamics may offer more accurate characterizations of neural activity in the auditory system than current ones based solely on the STRF. |
|---|