Optogenetics in primates: monkey see monkey look

Optogenetics has emerged as a powerful tool for studying the neural basis of simple behaviors in rodents and small animals. In the primate model, however, optogenetics has had limited utility because optical methods have not been able to drive behavior. Here, we report that monkeys reliably shift their gaze toward the receptive field of optically driven channelrhodopsin-2-expressing V1 neurons. This result establishes optogenetics as a viable means for the causal analysis of behavior in the primate model

Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity

The current exponential rise in recording capacity calls for new approaches for analysing and interpreting neural data. Effective dimensionality has emerged as a key concept for describing neural activity at the collective level, yet different studies rely on a variety of definitions of it. Here we focus on the complementary notions of intrinsic and embedding dimensionality, and argue that they provide a useful framework for extracting computational principles from data. Reviewing recent works, we propose that the intrinsic dimensionality reflects information about the latent variables encoded in collective activity, while embedding dimensionality reveals the manner in which this information is processed. Network models form an ideal substrate for testing more specifically the hypotheses on the computational principles reflected through intrinsic and embedding dimensionality

A Neural Mechanism for Sensing and Reproducing a Time Interval

Timing plays a crucial role in sensorimotor function. However, the neural mechanisms that enable the brain to flexibly measure and reproduce time intervals are not known. We recorded neural activity in parietal cortex of monkeys in a time reproduction task. Monkeys were trained to measure and immediately afterward reproduce different sample intervals. While measuring an interval, neural responses had a nonlinear profile that increased with the duration of the sample interval. Activity was reset during the transition from measurement to production and was followed by a ramping activity whose slope encoded the previously measured sample interval. We found that firing rates at the end of the measurement epoch were correlated with both the slope of the ramp and the monkey's corresponding production interval on a trial-by-trial basis. Analysis of response dynamics further linked the rate of change of firing rates in the measurement epoch to the slope of the ramp in the production epoch. These observations suggest that, during time reproduction, an interval is measured prospectively in relation to the desired motor plan to reproduce that interval

A Neural Mechanism for Sensing and Reproducing a Time Interval

SummaryTiming plays a crucial role in sensorimotor function. However, the neural mechanisms that enable the brain to flexibly measure and reproduce time intervals are not known. We recorded neural activity in parietal cortex of monkeys in a time reproduction task. Monkeys were trained to measure and immediately afterward reproduce different sample intervals. While measuring an interval, neural responses had a nonlinear profile that increased with the duration of the sample interval. Activity was reset during the transition from measurement to production and was followed by a ramping activity whose slope encoded the previously measured sample interval. We found that firing rates at the end of the measurement epoch were correlated with both the slope of the ramp and the monkey’s corresponding production interval on a trial-by-trial basis. Analysis of response dynamics further linked the rate of change of firing rates in the measurement epoch to the slope of the ramp in the production epoch. These observations suggest that, during time reproduction, an interval is measured prospectively in relation to the desired motor plan to reproduce that interval

Decoding the activity of neuronal populations in macaque primary visual cortex

Visual function depends on the accuracy of signals carried by visual cortical neurons. Combining information across neurons should improve this accuracy because single neuron activity is variable. We examined the reliability of information inferred from populations of simultaneously recorded neurons in macaque primary visual cortex. We considered a decoding framework that computes the likelihood of visual stimuli from a pattern of population activity by linearly combining neuronal responses and tested this framework for orientation estimation and discrimination. We derived a simple parametric decoder assuming neuronal independence and a more sophisticated empirical decoder that learned the structure of the measured neuronal response distributions, including their correlated variability. The empirical decoder used the structure of these response distributions to perform better than its parametric variant, indicating that their structure contains critical information for sensory decoding. These results show how neuronal responses can best be used to inform perceptual decision-making

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.Alfred P. Sloan Foundation (BR-2014-102)Esther A. and Joseph Klingenstein FundSimons Foundation (542993SPI)McKnight Endowment Fund for NeuroscienceMcGovern Institute for Brain Research at MI

Representation of Accumulating Evidence for a Decision in Two Parietal Areas

Decisions are often made by accumulating evidence for and against the alternatives. The momentary evidence represented by sensory neurons is accumulated by downstream structures to form a decision variable, linking the evolving decision to the formation of a motor plan. When decisions are communicated by eye movements, neurons in the lateral intraparietal area (LIP) represent the accumulation of evidence bearing on the potential targets for saccades. We now show that reach-related neurons from the medial intraparietal area (MIP) exhibit a gradual modulation of their firing rates consistent with the representation of an evolving decision variable. When decisions were communicated by saccades instead of reaches, decision-related activity was attenuated in MIP, whereas LIP neurons were active while monkeys communicated decisions by saccades or reaches. Thus, for decisions communicated by a hand movement, a parallel flow of sensory information is directed to parietal areas MIP and LIP during decision formation

Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes

Humans and animals have a rich and flexible understanding of the physical world, which enables them to infer the underlying dynamical trajectories of objects and events, plausible future states, and use that to plan and anticipate the consequences of actions. However, the neural mechanisms underlying these computations are unclear. We combine a goal-driven modeling approach with dense neurophysiological data and high-throughput human behavioral readouts to directly impinge on this question. Specifically, we construct and evaluate several classes of sensory-cognitive networks to predict the future state of rich, ethologically-relevant environments, ranging from self-supervised end-to-end models with pixel-wise or object-centric objectives, to models that future predict in the latent space of purely static image-based or dynamic video-based pretrained foundation models. We find strong differentiation across these model classes in their ability to predict neural and behavioral data both within and across diverse environments. In particular, we find that neural responses are currently best predicted by models trained to predict the future state of their environment in the latent space of pretrained foundation models optimized for dynamic scenes in a self-supervised manner. Notably, models that future predict in the latent space of video foundation models that are optimized to support a diverse range of sensorimotor tasks, reasonably match both human behavioral error patterns and neural dynamics across all environmental scenarios that we were able to test. Overall, these findings suggest that the neural mechanisms and behaviors of primate mental simulation are thus far most consistent with being optimized to future predict on dynamic, reusable visual representations that are useful for embodied AI more generally.Comment: 17 pages, 6 figure

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

A theoretical investigation of the generation of a spontaneous slow rhythm in hippocampus CA1

grantor: University of TorontoDifferent rhythmic activities in CA1 characterize the neuronal correlates of several behavioral states. Recently, in an in vitro preparation of the whole hippocampus, spontaneous slow rhythms (<4 Hz) similar to the hippocampal EEG seen in behaving animals, have been recorded. Based on the experimental data and by using numerical simulations, we suggest a mechanism in which feedback from populations of synchronized interneurons entrains an increasing and/or more synchronized activity in a spatially dynamic pyramidal cell population. In this scenario, two network properties, i.e. the number of the excitatory cells and the excitability of the interneurons, determine the frequency and robustness of these slow rhythms. Furthermore, using a stochastic phenomenological model, we show that in the face of a stochastic basal activity, these two properties enable the network to differentially amplify/suppress the modal structure of the emergent rhythmic activity, hence, act as a dynamically tunable resonant network.M.Sc