Extracting Kinematic Parameters for Monkey Bipedal Walking from Cortical Neuronal Ensemble Activity

The ability to walk may be critically impacted as the result of neurological injury or disease. While recent advances in brain–machine interfaces (BMIs) have demonstrated the feasibility of upper-limb neuroprostheses, BMIs have not been evaluated as a means to restore walking. Here, we demonstrate that chronic recordings from ensembles of cortical neurons can be used to predict the kinematics of bipedal walking in rhesus macaques – both offline and in real time. Linear decoders extracted 3D coordinates of leg joints and leg muscle electromyograms from the activity of hundreds of cortical neurons. As more complex patterns of walking were produced by varying the gait speed and direction, larger neuronal populations were needed to accurately extract walking patterns. Extraction was further improved using a switching decoder which designated a submodel for each walking paradigm. We propose that BMIs may one day allow severely paralyzed patients to walk again

Ensembles of Gustatory Cortical Neurons Anticipate and Discriminate Between Tastants in a Single Lick

The gustatory cortex (GC) processes chemosensory and somatosensory information and is involved in learning and anticipation. Previously we found that a subpopulation of GC neurons responded to tastants in a single lick (Stapleton et al., 2006). Here we extend this investigation to determine if small ensembles of GC neurons, obtained while rats received blocks of tastants on a fixed ratio schedule (FR5), can discriminate between tastants and their concentrations after a single 50 μL delivery. In the FR5 schedule subjects received tastants every fifth (reinforced) lick and the intervening licks were unreinforced. The ensemble firing patterns were analyzed with a Bayesian generalized linear model whose parameters included the firing rates and temporal patterns of the spike trains. We found that when both the temporal and rate parameters were included, 12 of 13 ensembles correctly identified single tastant deliveries. We also found that the activity during the unreinforced licks contained signals regarding the identity of the upcoming tastant, which suggests that GC neurons contain anticipatory information about the next tastant delivery. To support this finding we performed experiments in which tastant delivery was randomized within each block and found that the neural activity following the unreinforced licks did not predict the upcoming tastant. Collectively, these results suggest that after a single lick ensembles of GC neurons can discriminate between tastants, that they may utilize both temporal and rate information, and when the tastant delivery is repetitive ensembles contain information about the identity of the upcoming tastant delivery

A Brain-Machine Interface Instructed by Direct Intracortical Microstimulation

Brain–machine interfaces (BMIs) establish direct communication between the brain and artificial actuators. As such, they hold considerable promise for restoring mobility and communication in patients suffering from severe body paralysis. To achieve this end, future BMIs must also provide a means for delivering sensory signals from the actuators back to the brain. Prosthetic sensation is needed so that neuroprostheses can be better perceived and controlled. Here we show that a direct intracortical input can be added to a BMI to instruct rhesus monkeys in choosing the direction of reaching movements generated by the BMI. Somatosensory instructions were provided to two monkeys operating the BMI using either: (a) vibrotactile stimulation of the monkey's hands or (b) multi-channel intracortical microstimulation (ICMS) delivered to the primary somatosensory cortex (S1) in one monkey and posterior parietal cortex (PP) in the other. Stimulus delivery was contingent on the position of the computer cursor: the monkey placed it in the center of the screen to receive machine–brain recursive input. After 2 weeks of training, the same level of proficiency in utilizing somatosensory information was achieved with ICMS of S1 as with the stimulus delivered to the hand skin. ICMS of PP was not effective. These results indicate that direct, bi-directional communication between the brain and neuroprosthetic devices can be achieved through the combination of chronic multi-electrode recording and microstimulation of S1. We propose that in the future, bidirectional BMIs incorporating ICMS may become an effective paradigm for sensorizing neuroprosthetic devices

Depression at Thalamocortical Synapses The Key for Cortical Neuronal Adaptation?

AbstractNeuronal adaptation to repetitive sensory stimuli is ubiquitous in the mammalian cortex. Despite its prevalence, the cellular mechanisms underlying this basic physiological property remain a matter of dispute. In this issue of Neuron, Chung et al. provide conclusive evidence that depression of thalamocortical synapses may play a significant role in the expression of neuronal adaptation in the rat somatosensory cortex

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

The Insular Cortex Controls Food Preferences Independently of Taste Receptor Signaling

The insular cortex (IC) contains the primary sensory cortex for oral chemosensation including gustation, and its integrity is required for appropriate control of feeding behavior. However, it remains unknown whether the role of this brain area in food selection relies on the presence of peripheral taste input. Using multielectrode recordings, we found that the responses of populations of neurons in the IC of freely licking, sweet-blind Trpm5−/− mice are modulated by the rewarding postingestive effects of sucrose. FOS immunoreactivity analyses revealed that these responses are restricted to the dorsal insula. Furthermore, bilateral lesions in this area abolished taste-independent preferences for sucrose that can be conditioned in these Trpm5−/− animals while preserving their ability to detect sucrose. Overall, these findings demonstrate that, even in the absence of peripheral taste input, IC regulates food choices based on postingestive signals

Novel Experience Induces Persistent Sleep-Dependent Plasticity in the Cortex but not in the Hippocampus

Episodic and spatial memories engage the hippocampus during acquisition but migrate to the cerebral cortex over time. We have recently proposed that the interplay between slow-wave (SWS) and rapid eye movement (REM) sleep propagates recent synaptic changes from the hippocampus to the cortex. To test this theory, we jointly assessed extracellular neuronal activity, local field potentials (LFP), and expression levels of plasticity-related immediate-early genes (IEG) arc and zif-268 in rats exposed to novel spatio-tactile experience. Post-experience firing rate increases were strongest in SWS and lasted much longer in the cortex (hours) than in the hippocampus (minutes). During REM sleep, firing rates showed strong temporal dependence across brain areas: cortical activation during experience predicted hippocampal activity in the first post-experience hour, while hippocampal activation during experience predicted cortical activity in the third post-experience hour. Four hours after experience, IEG expression was specifically upregulated during REM sleep in the cortex, but not in the hippocampus. Arc gene expression in the cortex was proportional to LFP amplitude in the spindle-range (10–14 Hz) but not to firing rates, as expected from signals more related to dendritic input than to somatic output. The results indicate that hippocampo-cortical activation during waking is followed by multiple waves of cortical plasticity as full sleep cycles recur. The absence of equivalent changes in the hippocampus may explain its mnemonic disengagement over time

A ação gerencial face às dicotomias do processo decisório

Orientador: Maurício ServaMonografia (Especialização) - Universidade Federal do Paraná, Setor de Ciências Sociais Aplicadas, Curso de Especialização MBA em Estratégia e Gestão Empresarial.Inclui referências

Cortical Correlates of Fitts’ Law

Fitts’ law describes the fundamental trade-off between movement accuracy and speed: it states that the duration of reaching movements is a function of target size (TS) and distance. While Fitts’ law has been extensively studied in ergonomics and has guided the design of human–computer interfaces, there have been few studies on its neuronal correlates. To elucidate sensorimotor cortical activity underlying Fitts’ law, we implanted two monkeys with multielectrode arrays in the primary motor (M1) and primary somatosensory (S1) cortices. The monkeys performed reaches with a joystick-controlled cursor toward targets of different size. The reaction time (RT), movement time, and movement velocity changed with TS, and M1 and S1 activity reflected these changes. Moreover, modifications of cortical activity could not be explained by changes of movement parameters alone, but required TS as an additional parameter. Neuronal representation of TS was especially prominent during the early RT period where it influenced the slope of the firing rate rise preceding movement initiation. During the movement period, cortical activity was correlated with movement velocity. Neural decoders were applied to simultaneously decode TS and motor parameters from cortical modulations. We suggest that sensorimotor cortex activity reflects the characteristics of both the movement and the target. Classifiers that extract these parameters from cortical ensembles could improve neuroprosthetic control