Biology subjects

Angelaki, D. E.

Publications and source records attributed to Angelaki, D. E..

4 recordsLinked to original sources

bioRxiv2017

Speed tuning in the macaque posterior parietal cortex

Neurons in the macaque posterior parietal cortex are known to encode the direction of self-motion. But do they also encode ones speed? To test this, we performed neural recordings from area 7a while monkeys were passively translated or rotated at various speeds. Visual stimuli were delivered as optic flow fields and vestibular stimuli were generated by a motion platform. Under both conditions, the responses of a fraction of neurons scaled linearly with self-motion speed, and speed-selective neurons were not localized to specific layers or columns. We analyzed ensembles of simultaneously recorded neurons and found that the precision of speed representation was sufficient to support path integration over modest distances. Our findings describe a multisensory neural code for linear and angular self-motion speed in the posterior parietal cortex of the macaque brain, and suggest a potential role for this representation.

neuroscience↗

bioRxiv2017

A Dynamic Bayesian Observer Model Reveals Origins of Bias in Visual Path Integration

Path integration is a navigation strategy by which animals track their position by integrating their self-motion velocity over time. To identify the computational origins of bias in visual path integration, we asked human subjects to navigate in a virtual environment using optic flow, and found that they generally travelled beyond the goal location. Such a behaviour could stem from leaky integration of unbiased self-motion velocity estimates, or from a prior expectation favouring slower speeds that causes underestimation of velocity. We tested both alternatives using a probabilistic framework that maximizes expected reward, and found that subjects biases were better explained by a slow-speed prior than imperfect integration. When subjects integrate paths over long periods, this framework intriguingly predicts a distance-dependent bias reversal due to build-up of uncertainty, which we also confirmed experimentally. These results suggest that visual path integration performance is limited largely by biases in processing optic flow rather than by suboptimal signal integration.

neuroscience↗

bioRxiv2017

Bayesian Comparison of Explicit and Implicit Causal Inference Strategies in Multisensory Heading Perception

The precision of multisensory heading perception improves when visual and vestibular cues arising from the same cause, namely motion of the observer through a stationary environment, are integrated. Thus, in order to determine how the cues should be processed, the brain must infer the causal relationship underlying the multisensory cues. In heading perception, however, it is unclear whether observers follow the Bayesian strategy, a simpler non-Bayesian heuristic, or even perform causal inference at all. We developed an efficient and robust computational framework to perform Bayesian model comparison of causal inference strategies, which incorporates a number of alternative assumptions about the observers. With this framework, we investigated whether human observers performance in an explicit cause attribution and an implicit heading discrimination task can be modeled as a causal inference process. In the explicit inference task, all subjects accounted for cue disparity when reporting judgments of common cause, although not necessarily all in a Bayesian fashion. By contrast, but in agreement with previous findings, data from the heading discrimination task only could not rule out that several of the same observers were adopting a forced-fusion strategy, whereby cues are integrated regardless of disparity. Only when we combined evidence from both tasks we were able to rule out forced-fusion in the heading discrimination task. Crucially, findings were robust across a number of variants of models and analyses. Our results demonstrate that our proposed computational framework allows researchers to ask complex questions within a rigorous Bayesian framework that accounts for parameter and model uncertainty.

neuroscience↗

bioRxiv2017

A unified internal model theory to resolve the paradox of active versus passive self-motion sensation

Brainstem and cerebellar neurons implement an internal model to accurately estimate self-motion during externally-generated ( passive) movements. However, these neurons show reduced responses during self-generated ( active) movements, indicating that the brain computes the predicted sensory consequences of motor commands in order to cancel sensory signals. Remarkably, the computational processes underlying sensory prediction during active motion and their relationship to internal model computations established during passive movements remain unknown. Here we construct a Kalman filter that incorporates motor commands into a previously-established model of optimal passive self-motion estimation. We find that the simulated sensory error and feedback signals match experimentally measured neuronal response during active and passive head and trunk rotations and translations. We conclude that a single internal model of head motion can process motor commands and sensory afferent signals optimally, and we describe how previously identified neural responses in the brainstem and cerebellum may represent distinct nodes in these computations.

neuroscience↗