Biomedical subjects
Brian Lau
Publications and source records attributed to Brian Lau.
Dynamic response-by-response models of matching behavior in rhesus monkeys.
We studied the choice behavior of 2 monkeys in a discrete-trial task with reinforcement contingencies similar to those Herrnstein (1961) used when he described the matching law. In each session, the monkeys experienced blocks of discrete trials at different relative-reinforcer frequencies or magnitudes with unsignalled transitions between the blocks. Steady-state data following adjustment to each transition were well characterized by the generalized matching law; response ratios undermatched reinforcer frequency ratios but matched reinforcer magnitude ratios. We modelled response-by-response behavior with linear models that used past reinforcers as well as past choices to predict the monkeys' choices on each trial. We found that more recently obtained reinforcers more strongly influenced choice behavior. Perhaps surprisingly, we also found that the monkeys' actions were influenced by the pattern of their own past choices. It was necessary to incorporate both past reinforcers and past choices in order to accurately capture steady-state behavior as well as the fluctuations during block transitions and the response-by-response patterns of behavior. Our results suggest that simple reinforcement learning models must account for the effects of past choices to accurately characterize behavior in this task, and that models with these properties provide a conceptual tool for studying how both past reinforcers and past choices are integrated by the neural systems that generate behavior.
Isolation of relevant visual features from random stimuli for cortical complex cells.
A crucial step in understanding the function of a neural circuit in visual processing is to know what stimulus features are represented in the spiking activity of the neurons. For neurons with complex, nonlinear response properties, characterization of feature representation requires measurement of their responses to a large ensemble of visual stimuli and an analysis technique that allows identification of relevant features in the stimuli. In the present study, we recorded the responses of complex cells in the primary visual cortex of the cat to spatiotemporal random-bar stimuli and applied spike-triggered correlation analysis of the stimulus ensemble. For each complex cell, we were able to isolate a small number of relevant features from a large number of null features in the random-bar stimuli. Using these features as visual stimuli, we found that each relevant feature excited the neuron effectively in isolation and contributed to the response additively when combined with other features. In contrast, the null features evoked little or no response in isolation and divisively suppressed the responses to relevant features. Thus, for each cortical complex cell, visual inputs can be decomposed into two distinct types of features (relevant and null), and additive and divisive interactions between these features may constitute the basic operations in visual cortical processing.
Computational subunits of visual cortical neurons revealed by artificial neural networks.
A crucial step toward understanding visual processing is to obtain a comprehensive description of the relationship between visual stimuli and neuronal responses. Many neurons in the visual cortex exhibit nonlinear responses, making it difficult to characterize their stimulus-response relationships. Here, we recorded the responses of primary visual cortical neurons of the cat to spatiotemporal random-bar stimuli and trained artificial neural networks to predict the response of each neuron. The random initial connections in the networks consistently converged to regular patterns. Analyses of these connection patterns showed that the response of each complex cell to the random-bar stimuli could be well approximated by the sum of a small number of subunits resembling simple cells. The direction selectivity of each complex cell measured with drifting gratings was also well predicted by the combination of these subunits, indicating the generality of the model. These results are consistent with a simple functional model for complex cells and demonstrate the usefulness of the neural network method for revealing the stimulus-response transformations of nonlinear neurons.