Name
Title | ||
|---|---|---|
Accounting for network effects in neuronal responses using L1 regularized point process models. |
Abstract | ||
|---|---|---|
Activity of a neuron, even in the early sensory areas, is not simply a function of its local receptive field or tuning properties, but depends on global context of the stimulus, as well as the neural context. This suggests the activity of the surrounding neurons and global brain states can exert considerable influence on the activity of a neuron. In this paper we implemented an L1 regularized point process model to assess the contribution of multiple factors to the firing rate of many individual units recorded simultaneously from V1 with a 96-electrode "Utah" array. We found that the spikes of surrounding neurons indeed provide strong predictions of a neuron's response, in addition to the neuron's receptive field transfer function. We also found that the same spikes could be accounted for with the local field potentials, a surrogate measure of global network states. This work shows that accounting for network fluctuations can improve estimates of single trial firing rate and stimulus-response transfer functions. |
Year | Venue | Field |
|---|---|---|
2010 | NIPS | Receptive field,Accounting,Computer science,Point process,Transfer function,Local field potential,Artificial intelligence,Stimulus (physiology),Sensory system,Neuron,Machine learning |
DocType | Volume | Issue |
Conference | 23 | 2 |
ISSN | Citations | PageRank |
1049-5258 | 9 | 0.57 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kelly, Ryan | 1 | 41 | 3.60 |
| Matthew A. Smith | 2 | 26 | 5.09 |
| Robert E. Kass | 3 | 328 | 43.43 |
| Tai Sing Lee | 4 | 794 | 88.73 |