Name
Title | ||
|---|---|---|
A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data. |
Abstract | ||
|---|---|---|
Point processes are popular models of neural spiking behavior as they provide a statistical distribution over temporal sequences of spikes and help to reveal the complexities underlying a series of recorded action potentials. However, the most common neural point process models, the Poisson process and the gamma renewal process, do not capture interactions and correlations that are critical to modeling populations of neurons. We develop a novel model based on a determinantal point process over latent embeddings of neurons that effectively captures and helps visualize complex inhibitory and competitive interaction. We show that this model is a natural extension of the popular generalized linear model to sets of interacting neurons. The model is extended to incorporate gain control or divisive normalization, and the modulation of neural spiking based on periodic phenomena. Applied to neural spike recordings from the rat hippocampus, we see that the model captures inhibitory relationships, a dichotomy of classes of neurons, and a periodic modulation by the theta rhythm known to be present in the data. |
Year | Venue | Field |
|---|---|---|
2013 | NIPS | Determinantal point process,Normalization (statistics),Renewal theory,Random neural network,Computer science,Latent variable model,Point process,Generalized linear model,Artificial intelligence,Spiking neural network,Machine learning |
DocType | Citations | PageRank |
Conference | 11 | 0.57 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jasper Snoek | 1 | 1051 | 62.71 |
| Richard S. Zemel | 2 | 4958 | 425.68 |
| Ryan P. Adams | 3 | 2286 | 131.88 |