Real-time field-programmable gate array-based closed-loop deep brain stimulation platform targeting cerebellar circuitry rescues motor deficits in a mouse model of cerebellar ataxia.

AIMS: The open-loop nature of conventional deep brain stimulation (DBS) produces continuous and excessive stimulation to patients which contributes largely to increased prevalence of adverse side effects. Cerebellar ataxia is characterized by abnormal Purkinje cells (PCs) dendritic arborization, loss of PCs and motor coordination, and muscle weakness with no effective treatment. We aim to develop a real-time field-programmable gate array (FPGA) prototype targeting the deep cerebellar nuclei (DCN) to close the loop for ataxia using conditional double knockout mice with deletion of PC-specific LIM homeobox (Lhx)1 and Lhx5, resulting in abnormal dendritic arborization and motor deficits. METHODS: We implanted multielectrode array in the DCN and muscles of ataxia mice. The beneficial effect of open-loop DCN-DBS or closed-loop DCN-DBS was compared by motor behavioral assessments, electromyography (EMG), and neural activities (neurospike and electroencephalogram) in freely moving mice. FPGA board, which performed complex real-time computation, was used for closed-loop DCN-DBS system. RESULTS: Closed-loop DCN-DBS was triggered only when symptomatic muscle EMG was detected in a real-time manner, which restored motor activities, electroencephalogram activities and neurospike properties completely in ataxia mice. Closed-loop DCN-DBS was more effective than an open-loop paradigm as it reduced the frequency of DBS. CONCLUSION: Our real-time FPGA-based DCN-DBS system could be a potential clinical strategy for alleviating cerebellar ataxia and other movement disorders.

Self-supervised dynamic learning for long-term high-fidelity image transmission through unstabilized diffusive media.

Multimode fiber (MMF) which supports parallel transmission of spatially distributed information is a promising platform for remote imaging and capacity-enhanced optical communication. However, the variability of the scattering MMF channel poses a challenge for achieving long-term accurate transmission over long distances, of which static optical propagation modeling with calibrated transmission matrix or data-driven learning will inevitably degenerate. In this paper, we present a self-supervised dynamic learning approach that achieves long-term, high-fidelity transmission of arbitrary optical fields through unstabilized MMFs. Multiple networks carrying both long- and short-term memory of the propagation model variations are adaptively updated and ensembled to achieve robust image recovery. We demonstrate >99.9% accuracy in the transmission of 1024 spatial degree-of-freedom over 1 km length MMFs lasting over 1000 seconds. The long-term high-fidelity capability enables compressive encoded transfer of high-resolution video with orders of throughput enhancement, offering insights for artificial intelligence promoted diffusive spatial transmission in practical applications.

Effective and efficient neural networks for spike inference from in vivo calcium imaging.

Calcium imaging provides advantages in monitoring large populations of neuronal activities simultaneously. However, it lacks the signal quality provided by neural spike recording in traditional electrophysiology. To address this issue, we developed a supervised data-driven approach to extract spike information from calcium signals. We propose the ENS2 (effective and efficient neural networks for spike inference from calcium signals) system for spike-rate and spike-event predictions using ΔF/F0 calcium inputs based on a U-Net deep neural network. When testing on a large, ground-truth public database, it consistently outperformed state-of-the-art algorithms in both spike-rate and spike-event predictions with reduced computational load. We further demonstrated that ENS2 can be applied to analyses of orientation selectivity in primary visual cortex neurons. We conclude that it would be a versatile inference system that may benefit diverse neuroscience studies.

Positive feedback of efferent copy via pontine nucleus facilitates cerebellum-mediated associative learning.

The cerebellum is critical for motor coordination and learning. However, the role of feedback circuitry in this brain region has not been fully explored. Here, we characterize a nucleo-ponto-cortical feedback pathway in classical delayed eyeblink conditioning (dEBC) of rats. We find that the efference copy is conveyed from the interposed cerebellar nucleus (Int) to cerebellar cortex through pontine nucleus (PN). Inhibiting or exciting the projection from the Int to the PN can decelerate or speed up acquisition of dEBC, respectively. Importantly, we identify two subpopulations of PN neurons (PN1 and PN2) that convey and integrate the feedback signals with feedforward sensory signals. We also show that the feedforward and feedback pathways via different types of PN neurons contribute to the plastic changes and cooperate synergistically to the learning of dEBC. Our results suggest that this excitatory nucleo-ponto-cortical feedback plays a significant role in modulating associative motor learning in cerebellum.

A Cerebellar Spiking Neural Model for Phase Reversal of Vestibulo-ocular Reflex.

Cerebellum possesses very rich motor control and learning capability which is critical for animals. In this study, we proposed a spiking neural network model of cerebellum for gain and phase adaptation in vestibulo-ocular reflex (VOR). VOR is a critical adaptive reflexive eye movement for maintaining a stable visual field. In this model (with neuron number at the order of 104), synaptic plasticity at parallel fiber-Purkinje cell synapses was considered. In particular, we have shown that the inhibitory inputs from molecular layer interneurons on Purkinje cells play a critical role in phase adaptation of VOR. The inhibitory input from interneurons indirectly affects the strength of long-term potentiation (LTP) and long-term depression (LTD), resulting in more drastic phase shift upon learning and hence allowing phase reversal of VOR. The strength of inhibitory input also affects the maximum phase shift that can be achieved. Our result is consistent with experiments in mutant mice with blocked inhibitory inputs.