When listening to music, humans readily perceive and move along with a periodic beat. Critically, perception of a periodic beat is commonly elicited by rhythmic stimuli with physical features arranged in a way that is not strictly periodic. Hence, beat perception must capitalize on mechanisms that transform stimulus features into a temporally recurrent format with emphasized beat periodicity. Here, I will present a line of work that aims to clarify the nature and neural basis of this transformation. In these studies, electrophysiological activity was recorded as participants listened to rhythms known to induce perception of a consistent beat across healthy Western adults. The results show that the human brain selectively emphasizes beat representation when it is not acoustically prominent in the stimulus, and this transformation (i) can be captured non-invasively using surface EEG in adult participants, (ii) is already in place in 5- to 6-month-old infants, and (iii) cannot be fully explained by subcortical auditory nonlinearities. Moreover, as revealed by human intracerebral recordings, a prominent beat representation emerges already in the primary auditory cortex. Finally, electrophysiological recordings from the auditory cortex of a rhesus monkey show a significant enhancement of beat periodicities in this area, similar to humans. Taken together, these findings indicate an early, general auditory cortical stage of processing by which rhythmic inputs are rendered more temporally recurrent than they are in reality. Already present in non-human primates and human infants, this "periodized" default format could then be shaped by higher-level associative sensory-motor areas and guide movement in individuals with strongly coupled auditory and motor systems. Together, this highlights the multiplicity of neural processes supporting coordinated musical behaviors widely observed across human cultures.The experiments herein include: a motor timing task comparing the effects of movement vs non-movement with and without feedback (Exp. 1A & 1B), a transcranial magnetic stimulation (TMS) study on the role of the supplementary motor area (SMA) in transforming temporal information (Exp. 2), and a perceptual timing task investigating the effect of noisy movement on time perception with both visual and auditory modalities (Exp. 3A & 3B). Together, the results of these studies support the Bayesian cue combination framework, in that: movement improves the precision of time perception not only in perceptual timing tasks but also motor timing tasks (Exp. 1A & 1B), stimulating the SMA appears to disrupt the transformation of temporal information (Exp. 2), and when movement becomes unreliable or noisy there is no longer an improvement in precision of time perception (Exp. 3A & 3B). Although there is support for the proposed framework, more studies (i.e., fMRI, TMS, EEG, etc.) need to be conducted in order to better understand where and how this may be instantiated in the brain; however, this work provides a starting point to better understanding the intrinsic connection between time and movement

Biological brains possess an exceptional ability to infer relevant behavioral responses to a wide range of stimuli from only a few examples. This capacity to generalize beyond the training set has been proven particularly challenging to realize in artificial systems. How neural processes enable this capacity to extrapolate to novel stimuli is a fundamental open question. A prominent but underexplored hypothesis suggests that generalization is facilitated by a low-dimensional organization of collective neural activity, yet evidence for the underlying neural mechanisms remains wanting. Combining network modeling, theory and neural data analysis, we tested this hypothesis in the framework of flexible timing tasks, which rely on the interplay between inputs and recurrent dynamics. We first trained recurrent neural networks on a set of timing tasks while minimizing the dimensionality of neural activity by imposing low-rank constraints on the connectivity, and compared the performance and generalization capabilities with networks trained without any constraint. We then examined the trained networks, characterized the dynamical mechanisms underlying the computations, and verified their predictions in neural recordings. Our key finding is that low-dimensional dynamics strongly increases the ability to extrapolate to inputs outside of the range used in training. Critically, this capacity to generalize relies on controlling the low-dimensional dynamics by a parametric contextual input. We found that this parametric control of extrapolation was based on a mechanism where tonic inputs modulate the dynamics along non-linear manifolds in activity space while preserving their geometry. Comparisons with neural recordings in the dorsomedial frontal cortex of macaque monkeys performing flexible timing tasks confirmed the geometric and dynamical signatures of this mechanism. Altogether, our results tie together a number of previous experimental findings and suggest that the low-dimensional organization of neural dynamics plays a central role in generalizable behaviors.

The pupil provides a rich, non-invasive measure of the neural bases of perception and cognition, and has been of particular value in uncovering the role of arousal-linked neuromodulation, which alters cortical processing as well as pupil size. But pupil size is subject to a multitude of influences, which complicates unique interpretation. We measured pupils of observers experiencing perceptual multistability -- an ever-changing subjective percept in the face of unchanging but inconclusive sensory input. In separate conditions the endogenously generated perceptual changes were either task-relevant or not, allowing a separation between perception-related and task-related pupil signals. Perceptual changes were marked by a complex pupil response that could be decomposed into two components: a dilation tied to task execution and plausibly indicative of an arousal-linked noradrenaline surge, and an overlapping constriction tied to the perceptual transient and plausibly a marker of altered visual cortical representation. Constriction, but not dilation, amplitude systematically depended on the time interval between perceptual changes, possibly providing an overt index of neural adaptation. These results show that the pupil provides a simultaneous reading on interacting but dissociable neural processes during perceptual multistability, and suggest that arousal-linked neuromodulation shapes action but not perception in these circumstances. This presentation covers work that was published in e-life

John O’Keefe is a Professor of Cognitive Neuroscience at UCL and he received the Nobel Prize in Physiology or Medicine in 2014 for his “discoveries of cells that constitute a positioning system in the brain". His revolutionary research on hippocampal place cells provided deeper insight into the neural processes underlying the sense of space. His lab in Sainsbury Wellcome Centre applies a wide range of methods to facilitate our understanding of the role of the entorhinal cortex and hippocampus in spatial memory and the neural mechanisms underlying short-term memories in the amygdala.

Classic economists proposed that economic choices rely on the computation and comparison of subjective values. This hypothesis continues to inform economic theory and experimental research, but behavioral measures are ultimately not sufficient to prove the proposal. Consistent with the hypothesis, when agents make choices, neurons in the orbitofrontal cortex (OFC) encode the subjective value of offered and chosen goods. Moreover, neuronal activity in this area suggests the formation of a decision. However, it is unclear whether these neural processes are causally related to choices. More generally, the evidence linking choices to value signals in the brain remains correlational. In my talk, I will present recent results showing that neuronal activity in OFC are causal to economic choices.