Recurrent cerebellar architecture solves the motor-error problem

Current views of cerebellar function have been heavily influenced by the models of Marr and Albus, who suggested that the climbing fibre input to the cerebellum acts as a teaching signal for motor learning. It is commonly assumed that this teaching signal must be motor error (the difference between actual and correct motor command), but this approach requires complex neural structures to estimate unobservable motor error from its observed sensory consequences. We have proposed elsewhere a recurrent decorrelation control architecture in which Marr-Albus models learn without requiring motor error. Here, we prove convergence for this architecture and demonstrate important advantages for the modular control of systems with multiple degrees of freedom. These results are illustrated by modelling adaptive plant compensation for the three-dimensional vestibular ocular reflex. This provides a functional role for recurrent cerebellar connectivity, which may be a generic anatomical feature of projections between regions of cerebral and cerebellar cortex

Reconstructing the Engram: Simultaneous, Multisite, Many Single Neuron Recordings

AbstractLittle is known about the physiological principles that govern large-scale neuronal interactions in the mammalian brain. Here, we describe an electrophysiological paradigm capable of simultaneously recording the extracellular activity of large populations of single neurons, distributed across multiple cortical and subcortical structures in behaving and anesthetized animals. Up to 100 neurons were simultaneously recorded after 48 microwires were implanted in the brain stem, thalamus, and somatosensory cortex of rats. Overall, 86% of the implanted microwires yielded single neurons, and an average of 2.3 neurons were discriminated per microwire. Our population recordings remained stable for weeks, demonstrating that this method can be employed to investigate the dynamic and distributed neuronal ensemble interactions that underlie processes such as sensory perception, motor control, and sensorimotor learning in freely behaving animals

Spinal cord stimulation alleviates motor deficits in a primate model of Parkinson disease.

Although deep brain electrical stimulation can alleviate the motor symptoms of Parkinson disease (PD), just a small fraction of patients with PD can take advantage of this procedure due to its invasive nature. A significantly less invasive method-epidural spinal cord stimulation (SCS)-has been suggested as an alternative approach for symptomatic treatment of PD. However, the mechanisms underlying motor improvements through SCS are unknown. Here, we show that SCS reproducibly alleviates motor deficits in a primate model of PD. Simultaneous neuronal recordings from multiple structures of the cortico-basal ganglia-thalamic loop in parkinsonian monkeys revealed abnormal highly synchronized neuronal activity within each of these structures and excessive functional coupling among them. SCS disrupted this pathological circuit behavior in a manner that mimics the effects caused by pharmacological dopamine replacement therapy or deep brain stimulation. These results suggest that SCS should be considered as an additional treatment option for patients with PD

Coherence Resonance and Noise-Induced Synchronization in Globally Coupled Hodgkin-Huxley Neurons

The coherence resonance (CR) of globally coupled Hodgkin-Huxley neurons is studied. When the neurons are set in the subthreshold regime near the firing threshold, the additive noise induces limit cycles. The coherence of the system is optimized by the noise. A bell-shaped curve is found for the peak height of power spectra of the spike train, being significantly different from a monotonic behavior for the single neuron. The coupling of the network can enhance CR in two different ways. In particular, when the coupling is strong enough, the synchronization of the system is induced and optimized by the noise. This synchronization leads to a high and wide plateau in the local measure of coherence curve. The local-noise-induced limit cycle can evolve to a refined spatiotemporal order through the dynamical optimization among the autonomous oscillation of an individual neuron, the coupling of the network, and the local noise.Comment: five pages, five figure

Comprehensive Analysis of Tissue Preservation and Recording Quality from Chronic Multielectrode Implants

Multielectrodes have been used with great success to simultaneously record the activity of neuronal populations in awake, behaving animals. In particular, there is great promise in the use of this technique to allow the control of neuroprosthetic devices by human patients. However, it is crucial to fully characterize the tissue response to the chronic implants in animal models ahead of the initiation of human clinical trials. Here we evaluated the effects of unilateral multielectrode implants on the motor cortex of rats weekly recorded for 1–6 months using several histological methods to assess metabolic markers, inflammatory response, immediate-early gene (IEG) expression, cytoskeletal integrity and apoptotic profiles. We also investigated the correlations between each of these features and firing rates, to estimate the impact of post-implant time on neuronal recordings. Overall, limited neuronal loss and glial activation were observed on the implanted sites. Reactivity to enzymatic metabolic markers and IEG expression were not significantly different between implanted and non-implanted hemispheres. Multielectrode recordings remained viable for up to 6 months after implantation, and firing rates correlated well to the histochemical and immunohistochemical markers. Altogether, our results indicate that chronic tungsten multielectrode implants do not substantially alter the histological and functional integrity of target sites in the cerebral cortex

Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates

Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations