Predicting Spiking Activities in DLS Neurons with Linear-Nonlinear-Poisson Model

Sisi Ma, David J. Barker
Proceedings of the Neural Connectomics Workshop at ECML 2014, PMLR 46:85-92, 2015.

Abstract

Spike train generation in primary motor cortex (M1) and somatosensory cortex (S1) has been studied extensively and is relatively well understood. On the contrary, the functionality and physiology of the dorsolateral striatum (DLS), the immediate downstream region of M1 and S1 and a critical link in the motor circuit, still requires intensive investigation. In the current study, spike trains of individual DLS neurons were reconstructed using a Linear-Nonlinear-Poisson model with features from two modalities: (1) the head position modality, which contains information regarding head movement and proprioception of the animal’s head; (2) the spike history modality, which contains information regarding the intrinsic physiological properties of the neuron. For the majority of the neurons examined, viable reconstruction accuracy was achieved when the neural activity was modeled with either feature modality or the two feature modalities combined. Subpopulations of neurons were also identifiied that had better reconstruction accuracy when modeled with features from single modalities. This study demonstrates the feasibility of spike train reconstruction in DLS neurons and provides insights into the physiology of DLS neurons.

Cite this Paper

BibTeX
@InProceedings{pmlr-v46-ma15, title = {Predicting Spiking Activities in DLS Neurons with Linear-Nonlinear-Poisson Model}, author = {Ma, Sisi and Barker, David J.}, booktitle = {Proceedings of the Neural Connectomics Workshop at ECML 2014}, pages = {85--92}, year = {2015}, editor = {Battaglia, Demian and Guyon, Isabelle and Lemaire, Vincent and Soriano, Jordi}, volume = {46}, series = {Proceedings of Machine Learning Research}, month = {15 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v46/ma15.pdf}, url = {https://proceedings.mlr.press/v46/ma15.html}, abstract = {Spike train generation in primary motor cortex (M1) and somatosensory cortex (S1) has been studied extensively and is relatively well understood. On the contrary, the functionality and physiology of the dorsolateral striatum (DLS), the immediate downstream region of M1 and S1 and a critical link in the motor circuit, still requires intensive investigation. In the current study, spike trains of individual DLS neurons were reconstructed using a Linear-Nonlinear-Poisson model with features from two modalities: (1) the head position modality, which contains information regarding head movement and proprioception of the animal’s head; (2) the spike history modality, which contains information regarding the intrinsic physiological properties of the neuron. For the majority of the neurons examined, viable reconstruction accuracy was achieved when the neural activity was modeled with either feature modality or the two feature modalities combined. Subpopulations of neurons were also identifiied that had better reconstruction accuracy when modeled with features from single modalities. This study demonstrates the feasibility of spike train reconstruction in DLS neurons and provides insights into the physiology of DLS neurons. } }
Endnote
%0 Conference Paper %T Predicting Spiking Activities in DLS Neurons with Linear-Nonlinear-Poisson Model %A Sisi Ma %A David J. Barker %B Proceedings of the Neural Connectomics Workshop at ECML 2014 %C Proceedings of Machine Learning Research %D 2015 %E Demian Battaglia %E Isabelle Guyon %E Vincent Lemaire %E Jordi Soriano %F pmlr-v46-ma15 %I PMLR %P 85--92 %U https://proceedings.mlr.press/v46/ma15.html %V 46 %X Spike train generation in primary motor cortex (M1) and somatosensory cortex (S1) has been studied extensively and is relatively well understood. On the contrary, the functionality and physiology of the dorsolateral striatum (DLS), the immediate downstream region of M1 and S1 and a critical link in the motor circuit, still requires intensive investigation. In the current study, spike trains of individual DLS neurons were reconstructed using a Linear-Nonlinear-Poisson model with features from two modalities: (1) the head position modality, which contains information regarding head movement and proprioception of the animal’s head; (2) the spike history modality, which contains information regarding the intrinsic physiological properties of the neuron. For the majority of the neurons examined, viable reconstruction accuracy was achieved when the neural activity was modeled with either feature modality or the two feature modalities combined. Subpopulations of neurons were also identifiied that had better reconstruction accuracy when modeled with features from single modalities. This study demonstrates the feasibility of spike train reconstruction in DLS neurons and provides insights into the physiology of DLS neurons.
RIS
TY - CPAPER TI - Predicting Spiking Activities in DLS Neurons with Linear-Nonlinear-Poisson Model AU - Sisi Ma AU - David J. Barker BT - Proceedings of the Neural Connectomics Workshop at ECML 2014 DA - 2015/10/21 ED - Demian Battaglia ED - Isabelle Guyon ED - Vincent Lemaire ED - Jordi Soriano ID - pmlr-v46-ma15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 46 SP - 85 EP - 92 L1 - http://proceedings.mlr.press/v46/ma15.pdf UR - https://proceedings.mlr.press/v46/ma15.html AB - Spike train generation in primary motor cortex (M1) and somatosensory cortex (S1) has been studied extensively and is relatively well understood. On the contrary, the functionality and physiology of the dorsolateral striatum (DLS), the immediate downstream region of M1 and S1 and a critical link in the motor circuit, still requires intensive investigation. In the current study, spike trains of individual DLS neurons were reconstructed using a Linear-Nonlinear-Poisson model with features from two modalities: (1) the head position modality, which contains information regarding head movement and proprioception of the animal’s head; (2) the spike history modality, which contains information regarding the intrinsic physiological properties of the neuron. For the majority of the neurons examined, viable reconstruction accuracy was achieved when the neural activity was modeled with either feature modality or the two feature modalities combined. Subpopulations of neurons were also identifiied that had better reconstruction accuracy when modeled with features from single modalities. This study demonstrates the feasibility of spike train reconstruction in DLS neurons and provides insights into the physiology of DLS neurons. ER -
APA
Ma, S. & Barker, D.J.. (2015). Predicting Spiking Activities in DLS Neurons with Linear-Nonlinear-Poisson Model. Proceedings of the Neural Connectomics Workshop at ECML 2014, in Proceedings of Machine Learning Research 46:85-92 Available from https://proceedings.mlr.press/v46/ma15.html.

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