A potentially organizing goal of the brain and cognitive sciences is to accurately explain domains of human intelligence as executable, neurally mechanistic models. Years of research have led to models that capture experimental results in individual behavioral tasks and individual brain regions. We here advocate for taking the next step: integrating experimental results from many laboratories into suites of benchmarks that, when considered together, push mechanistic models toward explaining entire domains of intelligence, such as vision, language, and motor control. Given recent successes of neurally mechanistic models and the surging availability of neural, anatomical, and behavioral data, we believe that now is the time to create integrative benchmarking platforms that incentivize ambitious, unified models. This perspective discusses the advantages and the challenges of this approach and proposes specific steps to achieve this goal in the domain of visual intelligence with the case study of an integrative benchmarking platform called Brain-Score.Each such integrative, executable network model would be an account of how the brain might accomplish the domain of ll

During the process of skill learning, synaptic connections in our brains are modified to form motor memories of learned sensorimotor acts. The more plastic the adult brain is, the easier it is to learn new skills or adapt to neurological injury. However, if the brain is too plastic and the pattern of synaptic connectivity is constantly changing, new memories will overwrite old memories, and learning becomes unstable. This trade-off is known as the stability-plasticity dilemma. Here a theory of sensorimotor learning and memory is developed whereby synaptic strengths are perpetually fluctuating without causing instability in motor memory recall, as long as the underlying neural networks are sufficiently noisy and massively redundant. The theory implies two distinct stages of learning-preasymptotic and postasymptotic-because once the error drops to a level comparable to that of the noiseinduced error, further error reduction requires altered network dynamics. A key behavioral prediction derived from this analysis is tested in a visuomotor adaptation experiment, and the resultant learning curves are modeled with a nonstationary neural network. Next, the theory is used to model two-photon microscopy data that show, in animals, high rates of dendritic spine turnover, even in the absence of overt behavioral learning. Finally, the theory predicts enhanced task selectivity in the responses of individual motor cortical neurons as the level of task expertise increases. From these considerations, a unique interpretation of sensorimotor memory is proposed-memories are defined not by fixed patterns of synaptic weights but, rather, by nonstationary synaptic patterns that fluctuate coherently.hyperplastic | neural tuning S ensorimotor skill learning, like other types of learning, occurs through the general mechanism of experience-dependent synaptic plasticity (1, 2). As we learn a new skill (such as a tennis stroke) through extensive practice, synapses in our brain are modified to form a lasting motor memory of that skill. However, if synapses are overly pliable and in a state of perpetual flux, memories may not stabilize properly as new learning can overwrite previous learning. Thus, for any distributed learning system, there is inherent tension between the competing requirements of stability and plasticity (3): Synapses must be sufficiently plastic to support the formation of new memories, while changing in a manner that preserves the traces of old memories. The specific learning mechanisms by which these contradictory constraints are simultaneously fulfilled are one of neuroscience's great mysteries.The inescapability of the stability-plasticity dilemma, as faced by any distributed learning system, is shown in the cartoon neural network in Fig. 1A. Suppose that the input pattern of [0.6, 0.4] must be transformed into the activation pattern [0.5, 0.7] at the output layer. Given the initial connectivity of the network, the input transforms to the incorrect output [0.8, 0.2]. Through practice and a learning mechanism, the weig...

Professional athletes involved in sports that require the execution of fine motor skills must practice for a considerable length of time before competing in an event. Why is such practice necessary? Is it merely to warm-up the muscles, tendons, and ligaments, or does the athlete's sensorimotor network need to be constantly recalibrated? In this article, the authors present a point of view in which the human sensorimotor system is characterized by: (a) a high noise level and (b) a high learning rate at the synaptic level (which, because of the noise, does not equate to a high learning rate at the behavioral level). They argue that many heuristics of human skill learning, including the need for a prolonged period of warm-up in experts, follow from these assumptions.

How is an evanescent wish to move translated into a concrete action? This simple question and puzzling miracle remains a focal point of motor systems neuroscience. Where does the difficulty lie? A great deal has been known about biomechanics for quite some time. More recently, there have been significant advances in our understanding of how the spinal system is organized into modules corresponding to spinal synergies, which are fixed patterns of multi-muscle recruitment. But much less is known about how the supraspinal system recruits these synergies in the correct spatiotemporal pattern to effectively control movement. We argue that what makes the problem of supraspinal control so difficult is that it emerges as a result of multiple convergent and redundant sensorimotor loops. Because these loops are convergent, multiple modes of information are mixed before being sent to the spinal system; because they are redundant, information is overlapping such that a mechanism must exist to eliminate the redundancy before the signal is sent to the spinal system. Given these complex interactions, simple correlation analyses between movement variables and neural activity are likely to render a confusing and inconsistent picture. Here, we suggest that the perspective of sensorimotor loops might help in achieving a better systems-level understanding. Further, state-of-the-art techniques in neurotechnology, such as optogenetics, appear to be well suited for investigating the problem of motor control at the level of loops.