TY - JOUR
T1 - A systematic approach to selecting task relevant neurons
AU - Kahn, Kevin
AU - Saxena, Shreya
AU - Eskandar, Emad
AU - Thakor, Nitish
AU - Schieber, Marc
AU - Gale, John T.
AU - Averbeck, Bruno
AU - Eden, Uri
AU - Sarma, Sridevi V.
N1 - Funding Information:
K Kahn and SV Sarma were supported by the Burroughs Wellcome Fund ( 100727401 ) and the National Science Foundation ( 1137237 ).
Publisher Copyright:
© 2015.
PY - 2015/4/1
Y1 - 2015/4/1
N2 - Background: Since task related neurons cannot be specifically targeted during surgery, a critical decision to make is to select which neurons are task-related when performing data analysis. Including neurons unrelated to the task degrade decoding accuracy and confound neurophysiological results. Traditionally, task-related neurons are selected as those with significant changes in firing rate when a stimulus is applied. However, this assumes that neurons' encoding of stimuli are dominated by their firing rate with little regard to temporal dynamics. New method: This paper proposes a systematic approach for neuron selection, which uses a likelihood ratio test to capture the contribution of stimulus to spiking activity while taking into account task-irrelevant intrinsic dynamics that affect firing rates. This approach is denoted as the model deterioration excluding stimulus (MDES) test. Results: MDES is compared to firing rate selection in four case studies: a simulation, a decoding example, and two neurophysiology examples. Comparison with existing methods: The MDES rankings in the simulation match closely with ideal rankings, while firing rate rankings are skewed by task-irrelevant parameters. For decoding, 95% accuracy is achieved using the top 8 MDES-ranked neurons, while the top 12 firing-rate ranked neurons are needed. In the neurophysiological examples, MDES matches published results when firing rates do encode salient stimulus information, and uncovers oscillatory modulations in task-related neurons that are not captured when neurons are selected using firing rates. Conclusions: These case studies illustrate the importance of accounting for intrinsic dynamics when selecting task-related neurons and following the MDES approach accomplishes that. MDES selects neurons that encode task-related information irrespective of these intrinsic dynamics which can bias firing rate based selection.
AB - Background: Since task related neurons cannot be specifically targeted during surgery, a critical decision to make is to select which neurons are task-related when performing data analysis. Including neurons unrelated to the task degrade decoding accuracy and confound neurophysiological results. Traditionally, task-related neurons are selected as those with significant changes in firing rate when a stimulus is applied. However, this assumes that neurons' encoding of stimuli are dominated by their firing rate with little regard to temporal dynamics. New method: This paper proposes a systematic approach for neuron selection, which uses a likelihood ratio test to capture the contribution of stimulus to spiking activity while taking into account task-irrelevant intrinsic dynamics that affect firing rates. This approach is denoted as the model deterioration excluding stimulus (MDES) test. Results: MDES is compared to firing rate selection in four case studies: a simulation, a decoding example, and two neurophysiology examples. Comparison with existing methods: The MDES rankings in the simulation match closely with ideal rankings, while firing rate rankings are skewed by task-irrelevant parameters. For decoding, 95% accuracy is achieved using the top 8 MDES-ranked neurons, while the top 12 firing-rate ranked neurons are needed. In the neurophysiological examples, MDES matches published results when firing rates do encode salient stimulus information, and uncovers oscillatory modulations in task-related neurons that are not captured when neurons are selected using firing rates. Conclusions: These case studies illustrate the importance of accounting for intrinsic dynamics when selecting task-related neurons and following the MDES approach accomplishes that. MDES selects neurons that encode task-related information irrespective of these intrinsic dynamics which can bias firing rate based selection.
KW - Model based
KW - Neuron selection
KW - Point processes
KW - Task-related neurons
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U2 - 10.1016/j.jneumeth.2015.02.020
DO - 10.1016/j.jneumeth.2015.02.020
M3 - Article
C2 - 25746150
AN - SCOPUS:84925372637
SN - 0165-0270
VL - 245
SP - 156
EP - 168
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
ER -