Abstract：
:Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (reducing the mean square error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications.
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
Lawhern V,Wu W,Hatsopoulos N,Paninski Ldoi
10.1016/j.jneumeth.2010.03.024subject
Has Abstractpub_date
2010-06-15 00:00:00pages
267-80issue
2eissn
0165-0270issn
1872-678Xpii
S0165-0270(10)00158-5journal_volume
189pub_type
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