RATS: Unsupervised manifold learning using low-distortion alignment of tangent spaces.

With the ubiquity of high-dimensional datasets in various biological fields, identifying low-dimensional topological manifolds within such datasets may reveal principles connecting latent variables to measurable instances in the world. The reliable discovery of such manifold structure in high-dimensional datasets can prove challenging, however, largely due to the introduction of distortion by leading manifold learning methods. The problem is further exacerbated by the lack of consensus on how to evaluate the quality of the recovered manifolds.

Heterogeneous encoding of temporal stimuli in the cerebellar cortex.

Local feedforward and recurrent connectivity are rife in the frontal areas of the cerebral cortex, which gives rise to rich heterogeneous dynamics observed in such areas. Recently, similar local connectivity motifs have been discovered among Purkinje and molecular layer interneurons of the cerebellar cortex, however, task-related activity in these neurons has often been associated with relatively simple facilitation and suppression dynamics. Here, we show that the rodent cerebellar cortex supports heterogeneity in task-related neuronal activity at a scale similar to the cerebral cortex.

Neural implementations of Bayesian inference.

Bayesian inference has emerged as a general framework that captures how organisms make decisions under uncertainty. Recent experimental findings reveal disparate mechanisms for how the brain generates behaviors predicted by normative Bayesian theories. Here, we identify two broad classes of neural implementations for Bayesian inference: a modular class, where each probabilistic component of Bayesian computation is independently encoded and a transform class, where uncertain measurements are converted to Bayesian estimates through latent processes.

Visual pigment evolution in Characiformes: The dynamic interplay of teleost whole-genome duplication, surviving opsins and spectral tuning.

Vision represents an excellent model for studying adaptation, given the genotype-to-phenotype map that has been characterized in a number of taxa. Fish possess a diverse range of visual sensitivities and adaptations to underwater light, making them an excellent group to study visual system evolution. In particular, some speciose but understudied lineages can provide a unique opportunity to better understand aspects of visual system evolution such as opsin gene duplication and neofunctionalization.

Bayesian Computation through Cortical Latent Dynamics.

Statistical regularities in the environment create prior beliefs that we rely on to optimize our behavior when sensory information is uncertain. Bayesian theory formalizes how prior beliefs can be leveraged and has had a major impact on models of perception, sensorimotor function, and cognition. However, it is not known how recurrent interactions among neurons mediate Bayesian integration.

A Dynamical Systems Perspective on Flexible Motor Timing.

A hallmark of higher brain function is the ability to rapidly and flexibly adjust behavioral responses based on internal and external cues. Here, we examine the computational principles that allow decisions and actions to unfold flexibly in time. We adopt a dynamical systems perspective and outline how temporal flexibility in such a system can be achieved through manipulations of inputs and initial conditions.

Flexible Sensorimotor Computations through Rapid Reconfiguration of Cortical Dynamics.

Neural mechanisms that support flexible sensorimotor computations are not well understood. In a dynamical system whose state is determined by interactions among neurons, computations can be rapidly reconfigured by controlling the system's inputs and initial conditions. To investigate whether the brain employs such control mechanisms, we recorded from the dorsomedial frontal cortex of monkeys trained to measure and produce time intervals in two sensorimotor contexts.

A cerebellar mechanism for learning prior distributions of time intervals.

Knowledge about the statistical regularities of the world is essential for cognitive and sensorimotor function. In the domain of timing, prior statistics are crucial for optimal prediction, adaptation and planning. Where and how the nervous system encodes temporal statistics is, however, not known.

Flexible timing by temporal scaling of cortical responses.

Musicians can perform at different tempos, speakers can control the cadence of their speech, and children can flexibly vary their temporal expectations of events. To understand the neural basis of such flexibility, we recorded from the medial frontal cortex of nonhuman primates trained to produce different time intervals with different effectors. Neural responses were heterogeneous, nonlinear, and complex, and they exhibited a remarkable form of temporal invariance: firing rate profiles were temporally scaled to match the produced intervals.

Structure learning and the Occam's razor principle: a new view of human function acquisition.

We often encounter pairs of variables in the world whose mutual relationship can be described by a function. After training, human responses closely correspond to these functional relationships. Here we study how humans predict unobserved segments of a function that they have been trained on and we compare how human predictions differ to those made by various function-learning models in the literature.

How the statistics of sequential presentation influence the learning of structure.

Recent work has shown that humans can learn or detect complex dependencies among variables. Even learning a simple dependency involves the identification of an underlying model and the learning of its parameters. This process represents learning a structured problem.

Sensorimotor priors in nonstationary environments.

In the course of its interaction with the world, the human nervous system must constantly estimate various variables in the surrounding environment. Past research indicates that environmental variables may be represented as probabilistic distributions of a priori information (priors). Priors for environmental variables that do not change much over time have been widely studied.