Biology subjects

Jazayeri, M.

Publications and source records attributed to Jazayeri, M..

7 recordsLinked to original sources

bioRxiv2018

Entrainment and maintenance of an internal metronome in premotor cortex

To prepare timely motor actions we constantly predict future events. Regularly repeating events are often perceived as a rhythm to which we can readily synchronize our movements, just as in dancing to music. However, the neuronal mechanisms underlying the capacity to encode and maintain rhythms are not understood. We trained nonhuman primates to maintain the rhythm of a visual metronome of different tempos and then we recorded neural activity in the supplementary motor area (SMA). SMA exhibited rhythmic bursts of gamma band (30-40 Hz) reflecting an internal tempo that matched the extinguished visual metronome. Moreover, gamma amplitude increased throughout the trial and provided an estimate of total elapsed time. Notably, the timing and amplitude of gamma bursts reflected systematic timing biases and errors in the behavioral responses. Our results indicate that premotor areas use dynamic motor plans to encode a metronome for rhythms and a stopwatch for total elapsed time.

neuroscience↗

bioRxiv2018

A nonlinear updating algorithm captures suboptimal inference in the presence of signal-dependent noise

Bayesian models of behavior have advanced the idea that humans combine prior beliefs and sensory observations to minimize uncertainty. How the brain implements Bayes-optimal inference, however, remains poorly understood. Simple behavioral tasks suggest that the brain can flexibly represent and manipulate probability distributions. An alternative view is that brain relies on simple algorithms that can implement Bayes-optimal behavior only when the computational demands are low. To distinguish between these alternatives, we devised a task in which Bayes-optimal performance could not be matched by simple algorithms. We asked subjects to estimate and reproduce a time interval by combining prior information with one or two sequential measurements. In the domain of time, measurement noise increases with duration. This property makes the integration of multiple measurements beyond the reach of simple algorithms. We found that subjects were able to update their estimates using the second measurement but their performance was suboptimal, suggesting that they were unable to update full probability distributions. Instead, subjects behavior was consistent with an algorithm that predicts upcoming sensory signals, and applies a nonlinear function to errors in prediction to update estimates. These results indicate that inference strategies humans deploy may deviate from Bayes-optimal integration when the computational demands are high.

neuroscience↗

bioRxiv2018

Flexible sensorimotor computations through rapid reconfiguration of cortical dynamics

Sensorimotor computations can be flexibly adjusted according to internal states and contextual inputs. The mechanisms supporting this flexibility are not understood. Here, we tested the utility of a dynamical system perspective to approach this problem. In a dynamical system whose state is determined by interactions among neurons, computations can be rapidly and flexibly reconfigured by controlling the system s inputs and initial conditions. To investigate whether the brain employs such control strategies, we recorded from the dorsomedial frontal cortex (DMFC) of monkeys trained to measure time intervals and subsequently produce timed motor responses according to multiple context-specific stimulus-response rules. Analysis of the geometry of neural states revealed a control mechanism that relied on the system s inputs and initial conditions. A tonic input specified by the behavioral context adjusted firing rates throughout each trial, while the dynamics in the measurement epoch allowed the system to establish initial conditions for the ensuing production epoch. This initial condition in turn set the speed of neural dynamics in the production epoch allowing the animal to aim for the target interval. These results provide evidence that the language of dynamical systems can be used to parsimoniously link brain activity to sensorimotor computations.

neuroscience↗

bioRxiv2017

An integral role for timing in interception

Timing is critical for myriad behaviors in dynamic environments. For example, to intercept an object, the brain must compute a reliable estimate of time-to-contact (TTC). Prior work suggests that humans compute TTC using kinematic information such as distance and speed without explicitly relying on temporal cues, just as one would do in a physics classroom using kinematic equations. Considering the inherent uncertainty associated with estimates of speed and distance and the ability of human brain to combine different sources of information, we asked whether humans additionally rely on temporal cues. We found that humans actively integrate speed information with both explicit and implicit timing cues. Analysis of behavior in relation to a Bayesian model revealed that the additional temporal information helps subjects optimize their performance in the presence of measurement uncertainty. These findings suggest that brains timing mechanisms are actively engaged while interacting with dynamic stimuli.

animal behavior and cognition↗

bioRxiv2017

Flexible control of speed of cortical dynamics

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 flexible timing, we recorded from the medial frontal cortex of primates trained to produce different time intervals with different effectors. The activity of neurons was heterogeneous, nonlinear and complex. However, responses were unified under a remarkable form of invariance: firing rate profiles were temporally stretched for longer intervals and compressed for short ones. At the network level, this phenomenon was evident by flexible changes in the speed with which the population activity traced an invariant trajectory. To identify the origin of speed control, we recorded from both downstream caudate neurons and thalamic neurons projecting to the medial frontal cortex. Speed adjustments were a prominent feature in the caudate but not in the thalamus suggesting that this phenomenon originates within cortical networks. To understand the underlying mechanisms, we created recurrent neural network models at different levels of complexity that could explain flexible timing with speed control. Analysis of the models revealed that the key to flexible speed control was the action of an external input upon the nonlinearities of individual neurons whose recurrent interactions set the networks relaxation dynamics. These findings demonstrate a simple and general mechanism for conferring temporal flexibility upon sensorimotor and cognitive functions.

neuroscience↗

bioRxiv2017

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, however, is not known. Deriving from physiological and anatomical evidence for cerebellar learning, we develop a computational model that demonstrates how the cerebellum could learn prior distributions of time intervals and support Bayesian temporal estimation. The model shows that salient features observed in human Bayesian time interval estimates can be readily captured by learning in the cerebellar cortex and circuit level computations in the cerebellar deep nuclei.

neuroscience↗

bioRxiv2017

Late Bayesian inference in sensorimotor behavior

Sensorimotor skills rely on performing noisy sensorimotor computations on noisy sensory measurements. Bayesian models suggest that humans compensate for measurement noise and reduce behavioral variability by biasing perception toward prior expectations. Whether the same holds for noise in sensorimotor computations is not known. Testing human subjects in tasks with different levels of sensorimotor complexity, we found a similar bias-variance tradeoff associated with increased sensorimotor noise. This result was accurately captured by a model which implements Bayesian inference after - not before - sensorimotor transformation. These results indicate that humans perform \"late inference\" downstream of sensorimotor computations rather than, or in addition to, \"early inference\" in the perceptual domain. The brain thus possesses internal models of noise in both sensory measurements and sensorimotor computations.

animal behavior and cognition↗