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

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

Coupled variability in primary sensory areas and the hippocampus during spontaneous activity

The cerebral cortex is an anatomically divided and functionally specialized structure. It includes distinct areas, which work on different states over time. The structural features of spiking activity in sensory cortices have been characterized during spontaneous and evoked activity. However, the coordination among cortical and sub-cortical neurons during spontaneous activity across different states remains poorly characterized. We addressed this issue by studying the temporal coupling of spiking variability recorded from primary sensory cortices and hippocampus of anesthetized or freely behaving rats. During spontaneous activity, spiking variability was highly correlated across primary cortical sensory areas at both small and large spatial scales, whereas the cortico-hippocampal correlation was modest. This general pattern of spiking variability was observed under urethane anesthesia, as well as during waking, slow-wave sleep and rapid-eye-movement sleep, and was unchanged by novel stimulation. These results support the notion that primary sensory areas are strongly coupled during spontaneous activity.project NORTE-01-0145-FEDER-000013, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). NAPV was supported by Centro Universitario do Rio Grande do Norte, Champalimaud Foundation, and Brazilian National Council for Scientific and Technological Development (CNPq, Grant 249991/2013-6), CC-S (SFRH/BD/51992/2012). AJR (IF/00883/2013). SR by UFRN, CNPq (Research Productivity Grant 308775/2015-5), and S. Paulo Research Foundation FAPESP - Center for Neuromathematics (Grant 2013/07699-0)info:eu-repo/semantics/publishedVersio

Regularized logistic regression and multi-objective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

Paralysis following spinal cord injury (SCI), brainstem stroke, amyotrophic lateral sclerosis (ALS) and other disorders can disconnect the brain from the body, eliminating the ability to carry out volitional movements. A neural interface system (NIS)1–5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with longstanding tetraplegia can use an NIS to move and click a computer cursor and to control physical devices6–8. Able-bodied monkeys have used an NIS to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here, we demonstrate the ability of two people with long-standing tetraplegia to use NIS-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor five years earlier, also used a robotic arm to drink coffee from a bottle. While robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after CNS injury, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals

A closed loop brain-machine interface for epilepsy control using dorsal column electrical stimulation

Although electrical neurostimulation has been proposed as an alternative treatment for drug-resistant cases of epilepsy, current procedures such as deep brain stimulation, vagus, and trigeminal nerve stimulation are effective only in a fraction of the patients. Here we demonstrate a closed loop brain-machine interface that delivers electrical stimulation to the dorsal column (DCS) of the spinal cord to suppress epileptic seizures. Rats were implanted with cortical recording microelectrodes and spinal cord stimulating electrodes, and then injected with pentylenetetrazole to induce seizures. Seizures were detected in real time from cortical local field potentials, after which DCS was applied. This method decreased seizure episode frequency by 44% and seizure duration by 38%. We argue that the therapeutic effect of DCS is related to modulation of cortical theta waves, and propose that this closed-loop interface has the potential to become an effective and semi-invasive treatment for refractory epilepsy and other neurological disorders.We are grateful for the assistance from Jim Meloy for the design and production of the multielectrode arrays as well as setup development and maintenance, Laura Oliveira, Terry Jones, and Susan Halkiotis for administrative assistance and preparation of the manuscript. This work was funded by a grant from The Hartwell Foundation.info:eu-repo/semantics/publishedVersio

Genetic dissection of an amygdala microcircuit that gates conditioned fear

The role of different amygdala nuclei (neuroanatomical subdivisions) in processing Pavlovian conditioned fear has been studied extensively, but the function of the heterogeneous neuronal subtypes within these nuclei remains poorly understood. Here we use molecular genetic approaches to map the functional connectivity of a subpopulation of GABA-containing neurons, located in the lateral subdivision of the central amygdala (CEl), which express protein kinase C-δ (PKC-δ). Channelrhodopsin-2-assisted circuit mapping in amygdala slices and cell-specific viral tracing indicate that PKC-δ^+ neurons inhibit output neurons in the medial central amygdala (CEm), and also make reciprocal inhibitory synapses with PKC-δ^− neurons in CEl. Electrical silencing of PKC-δ^+ neurons in vivo suggests that they correspond to physiologically identified units that are inhibited by the conditioned stimulus, called Cel_(off) units. This correspondence, together with behavioural data, defines an inhibitory microcircuit in CEl that gates CEm output to control the level of conditioned freezing

Role of neuronal reverberation during slow wave sleep in the consolidation of recently acquired memory traces

Evidence exists to state that the main sleep states, slow wave (SWS) and rapid eye movement (REM) periods perform two complementary roles for post-novelty memory consolidation: respectively neuronal reverberation for short-term recall (PLoS Bio., 2:0126, 2004); and synaptic plasticity for long-term storage (Learn. Mem. 6: 500, 1999). Moreover, memory traces gradually migrate from hippocampus (HP) to primary somatosensory cortex (SI) through consecutive waves of neuronal reverberation during SWS (Frontiers Neurosci., 1:43, 2007). Our goal is to test this hypothesis by assessing the importance of sleep reverberatory phenomena in memory consolidation. Adult male Long Evans rats (n = 3) received implants of electrode array (32 microwires) in SI for multi-site single-unit and local field potential (LFP) recordings; and a bilateral cannula in HP for local neuronal inhibition through muscimol micro-injection (0.5 μl; 1 mg/ml; 0.25 μl/min). Novel tactile stimulation was provided by a 20 minutes exposure to four objects at the end of the first third of a 12 hours recording session. Muscimol was injected after animals reached a criterion of 30 minutes of SWS in the post- exposure period, as assessed by an online state map algorithm (Frontiers Neurosci., 1:43, 2007). Memory consolidation was assessed by comparing exploration times of animals re-exposed to two familiar plus two new objects over several post-novelty days. Firing rates strongly increased during object exploration and remained elevated afterwards during waking. SI single-unit activity during SWS showed even greater Post/Pre differences in firing rates, displaying periodic waves of reverberation yet to be understood. Muscimol application in the hippocampus completely abolished cortical reverberation a few minutes after micro-injection. Hippocampal deactivation by muscimol induced an electrographic rhythm characterized by greater power in the delta band as revealed by LFP recordings. This rhythm was distinctly separated from the SWS spectral cluster in the state map. Importantly, the time spent in exploration of the new objects was not significantly different from the time spent exploring familiar objects, indicating impairment in the learning of object identities. The results suggest that inhibition of HP neuronal activity 1) impaired SI neuronal reverberation during SWS (possibly by altering slow wave stereotypical brain rhythm) and 2) led to decreased memory consolidation for familiar objects