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.