Adaptive stimulus selection for optimizing neural population responses
Adaptive stimulus selection methods in neuroscience have primarily focused on maximizing the firing rate of a single recorded neuron. When recording from a population of neurons, it is usually not possible to find a single stimulus that maximizes the firing rates of all neurons. This motivates optimizing an objective function that takes into account the responses of all recorded neurons together. We propose "Adept," an adaptive stimulus selection method that can optimize population objective functions. In simulations, we first confirmed that population objective functions elicited more diverse stimulus responses than single-neuron objective functions. Then, we tested Adept in a closed-loop electrophysiological experiment in which population activity was recorded from macaque V4, a cortical area known for mid-level visual processing. To predict neural responses, we used the outputs of a deep convolutional neural network model as feature embeddings. Natural images chosen by Adept elicited mean neural responses that were 20% larger than those for randomly-chosen natural images, and also evoked a larger diversity of neural responses. Such adaptive stimulus selection methods can facilitate experiments that involve neurons far from the sensory periphery, for which it is often unclear which stimuli to present.
The authors present an algorithm for selecting stimuli in a closed-loop experiment to maximize the variance in firing across a population of neurons (Adept). This is an important step in closed-loop stimulus selection, which have focused on learning the response properties of single neurons. The algorithm uses pre-computed features of the stimuli given by a neural network to predict firing rates to new stimuli. The methods were presented clearly and the stimulus selection is efficient enough to be useful to real experiments.
There are a few limiting points of the Adept method (which may be addressed in future work). The applicability of this algorithm is limited, as mentioned by the authors, in that it depends on having a good set of features to describe the stimulus space. Such features may not always be available outside select areas such as V4. It would be valuable to explore the Adept algorithm's behavior when the features chosen do not provide a good basis for prediction spike rates (perhaps for only a portion of the population - would the algorithm learn less for those cells than random stimulus selection?).
Additionally, the algorithm assumes a limited discrete set of stimuli. Finally, it is not clear how the stimulus selection may be affected by different types of noise (in particular, correlated noise across cells or time) in the responses.