Decision-making and brain states
How do brain states modulate sensory processing and decision-making?
Anticipatory activity and cortical state sequences
Sensory stimuli are processed faster when their presentation is expected compared to when they come as a surprise. Accordingly, neural activity evoked by sensory stimuli encodes information about the stimuli faster when the stimulus is expected. I investigated whether anticipatory neural activity is driven by i) firing rate modulations or ii) modulations of the network dynamics that leave firing rates unchanged. A model of anticipation based on a clustered network showed that, when the stimulus is preceded by a cue, stimulus-coding states show a faster onset. Using mean field theory. I showed that cue presentation lowers the potential barriers separating network attractors, leading to an acceleration of the network itinerant dynamics. In the presence of a stimulus, this acceleration leads to a shorter onset latency of coding states. This anticipatory effect is unrelated to changes of firing rates in stimulus-selective neurons, and is absent in homogeneous balanced networks, suggesting that a clustered organization is necessary to mediate the expectation of relevant events. A set of distracting cues recruiting inhibitory neurons increased the potential barriers between attractors and degraded stimulus coding. These results demonstrate a novel mechanism for speeding up sensory coding in cortical circuits, and suggest that observed state sequences are a consequence of the combinatorial and temporal coding properties of metastable coding states.
Neural dimensionality during spontaneous and evoked activity
The activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. A spiking network model with clustered architecture predicts a characteristic scaling with ensemble size that could be tested in high-density multi-electrode recordings. The theory predicted the existence of an upper bound on dimensionality. The bound is inversely proportional to the amount of pair-wise correlations, and directly to the number of clusters. The theory allows to estimate the dependence of such bounds on the number and duration of trials. Together, these results provide a framework to analyze neural dimensionality in alert animals, its behavior under stimulus presentation, and its theoretical dependence on ensemble size, number of clusters, and correlations in spiking network models.