Sound localization acuity of the common marmoset (Callithrix jacchus).

The common marmoset (Callithrix jacchus) is a small arboreal New World primate which has emerged as a promising model in auditory neuroscience. One potentially useful application of this model system is in the study of the neural mechanism underlying spatial hearing in primate species, as the marmosets need to localize sounds to orient their head to events of interest and identify their vocalizing conspecifics that are not visible. However, interpretation of neurophysiological data on sound localization requires an understanding of perceptual abilities, and the sound localization behavior of marmosets has not been well studied. The present experiment measured sound localization acuity using an operant conditioning procedure in which marmosets were trained to discriminate changes in sound location in the horizontal (azimuth) or vertical (elevation) dimension. Our results showed that the minimum audible angle (MAA) for horizontal and vertical discrimination was 13.17° and 12.53°, respectively, for 2 to 32 kHz Gaussian noise. Removing the monaural spectral cues tended to increase the horizontal localization acuity (11.31°). Marmosets have larger horizontal MAA (15.54°) in the rear than the front. Removing the high-frequency (> 26 kHz) region of the head-related transfer function (HRTF) affected vertical acuity mildly (15.76°), but removing the first notch (12-26 kHz) region of HRTF substantially reduced the vertical acuity (89.01°). In summary, our findings indicate that marmosets' spatial acuity is on par with other species of similar head size and field of best vision, and they do not appear to use monaural spectral cues for horizontal discrimination but rely heavily on first notch region of HRTF for vertical discrimination.

Internal models of sensorimotor integration regulate cortical dynamics.

Sensorimotor control during overt movements is characterized in terms of three building blocks: a controller, a simulator and a state estimator. We asked whether the same framework could explain the control of internal states in the absence of movements. Recently, it was shown that the brain controls the timing of future movements by adjusting an internal speed command. We trained monkeys in a novel task in which the speed command had to be dynamically controlled based on the timing of a sequence of flashes. Recordings from the frontal cortex provided evidence that the brain updates the internal speed command after each flash based on the error between the timing of the flash and the anticipated timing of the flash derived from a simulated motor plan. These findings suggest that cognitive control of internal states may be understood in terms of the same computational principles as motor control.

Neural Representations of the Full Spatial Field in Auditory Cortex of Awake Marmoset (Callithrix jacchus).

Unlike visual signals, sound can reach the ears from any direction, and the ability to localize sounds from all directions is essential for survival in a natural environment. Previous studies have largely focused on the space in front of a subject that is also covered by vision and were often limited to measuring spatial tuning along the horizontal (azimuth) plane. As a result, we know relatively little about how the auditory cortex responds to sounds coming from spatial locations outside the frontal space where visual information is unavailable. By mapping single-neuron responses to the full spatial field in awake marmoset (Callithrix jacchus), an arboreal animal for which spatial processing is vital in its natural habitat, we show that spatial receptive fields in several auditory areas cover all spatial locations. Several complementary measures of spatial tuning showed that neurons were tuned to both frontal space and rear space (outside the coverage of vision), as well as the space above and below the horizontal plane. Together, these findings provide valuable new insights into the representation of all spatial locations by primate auditory cortex.

Late Bayesian inference in mental transformations.

Many skills rely on performing noisy mental 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 a similar strategy is employed to compensate for noise in downstream mental and sensorimotor computations is not known. We tested humans in a battery of tasks and found that tasks which involved more complex mental transformations resulted in increased bias, suggesting that humans are able to mitigate the effect of noise in both sensorimotor and mental transformations. These results indicate that humans delay inference in order to account for both measurement noise and noise in downstream computations.

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. We then review evidence from experiments in nonhuman primates that support this interpretation. Finally, we explore the broader utility and limitations of the dynamical systems perspective as a general framework for addressing open questions related to the temporal control of movements, as well as in the domains of learning and sequence generation.

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. The geometry of neural trajectories during the production epoch was consistent with a mechanism wherein the measured interval and sensorimotor context exerted control over cortical dynamics by adjusting the system's initial condition and input, respectively. These adjustments, in turn, set the speed at which activity evolved in the production epoch, allowing the animal to flexibly produce different time intervals. These results provide evidence that the language of dynamical systems can be used to parsimoniously link brain activity to sensorimotor computations.

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. Based on 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. We test human behavior in two cerebellar timing tasks and find prior-dependent biases in timing that are consistent with the predictions of the cerebellar model.

An operant conditioning method for studying auditory behaviors in marmoset monkeys.

The common marmoset (Callithrix jacchus) is a small New World primate that has increasingly been used as a non-human model in the fields of sensory, motor, and cognitive neuroscience. However, little knowledge exists regarding behavioral methods in this species. Developing an understanding of the neural basis of perception and cognition in an animal model requires measurement of both brain activity and behavior. Here we describe an operant conditioning behavioral training method developed to allow controlled psychoacoustic measurements in marmosets. We demonstrate that marmosets can be trained to consistently perform a Go/No-Go auditory task in which a subject licks at a feeding tube when it detects a sound. Correct responses result in delivery of a food reward. Crucially, this operant conditioning task generates little body movement and is well suited for pairing behavior with single-unit electrophysiology. Successful implementation of an operant conditioning behavior opens the door to a wide range of new studies in the field of auditory neuroscience using the marmoset as a model system.