Computational NeuroscienceA systematic approach to selecting task relevant neurons

•Selecting task relevant neurons just from firing rate ignores temporal dynamics.•Modeling spiking based on spike history and stimulus includes temporal information.•Neuron selection based on likelihood ratio test between stimulus and non-stimulus models focuses on contribution of stimulus on neuron activity.•Model based selection performs better than firing rate based selection in simulations and finger movement decoding with other results being equivalent or unverifiable.•Different neurophysiological results can be seen when analyzing model selected neurons as opposed to firing rate selected neurons.

BackgroundSince 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 methodThis 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.ResultsMDES is compared to firing rate selection in four case studies: a simulation, a decoding example, and two neurophysiology examples.Comparison with existing methodsThe 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.ConclusionsThese 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.