Biomedical subjects

N Schweighofer

Publications and source records attributed to N Schweighofer.

12 recordsLinked to original sources

PubMed2006

Humans can adopt optimal discounting strategy under real-time constraints.

Critical to our many daily choices between larger delayed rewards, and smaller more immediate rewards, are the shape and the steepness of the function that discounts rewards with time. Although research in artificial intelligence favors exponential discounting in uncertain environments, studies with humans and animals have consistently shown hyperbolic discounting. We investigated how humans perform in a reward decision task with temporal constraints, in which each choice affects the time remaining for later trials, and in which the delays vary at each trial. We demonstrated that most of our subjects adopted exponential discounting in this experiment. Further, we confirmed analytically that exponential discounting, with a decay rate comparable to that used by our subjects, maximized the total reward gain in our task. Our results suggest that the particular shape and steepness of temporal discounting is determined by the task that the subject is facing, and question the notion of hyperbolic reward discounting as a universal principle.

Adaptation, Physiological↗

PubMed2001

Exploration of signal transduction pathways in cerebellar long-term depression by kinetic simulation.

Because multiple molecular signal transduction pathways regulate cerebellar long-term depression (LTD), which is thought to be a possible molecular and cellular basis of cerebellar learning, the systematic relationship between cerebellar LTD and the currently known signal transduction pathways remains obscure. To address this issue, we built a new diagram of signal transduction pathways and developed a computational model of kinetic simulation for the phosphorylation of AMPA receptors, known as a key step for expressing cerebellar LTD. The phosphorylation of AMPA receptors in this model consists of an initial phase and an intermediate phase. We show that the initial phase is mediated by the activation of linear cascades of protein kinase C (PKC), whereas the intermediate phase is mediated by a mitogen-activated protein (MAP) kinase-dependent positive feedback loop pathway that is responsible for the transition from the transient phosphorylation of the AMPA receptors to the stable phosphorylation of the AMPA receptors. These phases are dually regulated by the PKC and protein phosphatase pathways. Both phases also require nitric oxide (NO), although NO per se does not show any ability to induce LTD; this is consistent with a permissive role as reported experimentally (Lev-Ram et al., 1997). Therefore, the kinetic simulation is a powerful tool for understanding and exploring the behaviors of complex signal transduction pathways involved in cerebellar LTD.

Cerebellum↗

PubMed2001

Unsupervised learning of granule cell sparse codes enhances cerebellar adaptive control.

Marr [J. Physiol. (1969) 202, 437-470] and Albus [Math. Biosci. (1971) 10, 25-61] hypothesized that cerebellar learning is facilitated by a granule cell sparse code, i.e. a neural code in which the fraction of active neurons is low at any one time. In this paper, we re-examine this hypothesis in light of recent experimental and theoretical findings. We argue that cerebellar motor learning is enhanced by a sparse code that simultaneously maximizes information transfer between mossy fibers and granule cells, minimizes redundancies between granule cell discharges, and re-codes the mossy fiber inputs with an adaptive resolution such that inputs corresponding to large errors are finely encoded. We then propose that a set of biologically plausible unsupervised learning rules can produce such a code. To maintain a low mean firing rate compatible with a sparse code, an activity-dependent homeostatic mechanism sets the cells' thresholds. Then, to maximize information transfer, the mossy fiber--granule cell synapses are adjusted by a Hebbian rule. Furthermore, to minimize redundancies between granule cell discharges, the inhibitory Golgi cell--granule cell synapses are tuned by an anti-Hebbian rule. Finally, to allow adaptive resolution, a performance-based neuromodulator-like signal gates these three plastic processes. We integrate these gated learning rules into a simplified model of the cerebellum for arm movement control, and show that unsupervised learning of granule cell sparse codes greatly improves cerebellar adaptive motor control in comparison to a "fixed" Marr--Albus-type model. Until recently, activity-dependent cerebellar plasticity was thought to be largely confined to the granule cell--Purkinje cell synapses. This static view of the cerebellum is, however, quickly being replaced by an extremely dynamic view in which plasticity is omnipresent. The present theoretical study shows how several forms of plasticity in the granular layer of the cerebellum can produce fast, accurate and stable cerebellar learning.

Algorithms↗

PubMed2000

Diffusion of nitric oxide can facilitate cerebellar learning: A simulation study.

The gaseous second messenger nitric oxide (NO), which readily diffuses in brain tissue, has been implicated in cerebellar long-term depression (LTD), a form of synaptic plasticity thought to be involved in cerebellar learning. Can NO diffusion facilitate cerebellar learning? The inferior olive (IO) cells, which provide the error signals necessary for modifying the granule cell-Purkinje cell (PC) synapses by LTD, fire at ultra-low firing rates in vivo, rarely more than 2-4 spikes within a second. In this paper, we show that NO diffusion can improve the transmission of sporadic IO error signals to PCs within cerebellar cortical functional units, or microzones. To relate NO diffusion to adaptive behavior, we add NO diffusion and a "volumic" LTD learning rule, i.e., a learning rule that depends both on the synaptic activity and on the NO concentration at the synapse, to a cerebellar model for arm movement control. Our results show that biologically plausible diffusion leads to an increase in information transfer of the error signals to the PCs when the IO firing rate is ultra-low. This, in turn, enhances cerebellar learning as shown by improved performance in an arm-reaching task.

Action Potentials↗

PubMed2000

Cerebellar learning of accurate predictive control for fast-reaching movements.

Long conduction delays in the nervous system prevent the accurate control of movements by feedback control alone. We present a new, biologically plausible cerebellar model to study how fast arm movements can be executed in spite of these delays. To provide a realistic test-bed of the cerebellar neural model, we embed the cerebellar network in a simulated biological motor system comprising a spinal cord model and a six-muscle two-dimensional arm model. We argue that if the trajectory errors are detected at the spinal cord level, memory traces in the cerebellum can solve the temporal mismatch problem between efferent motor commands and delayed error signals. Moreover, learning is made stable by the inclusion of the cerebello-nucleo-olivary loop in the model. It is shown that the cerebellar network implements a nonlinear predictive regulator by learning part of the inverse dynamics of the plant and spinal circuit. After learning, fast accurate reaching movements can be generated.

Arm↗

PubMed1999

Electrophysiological properties of inferior olive neurons: A compartmental model.

As a step in exploring the functions of the inferior olive, we constructed a biophysical model of the olivary neurons to examine their unique electrophysiological properties. The model consists of two compartments to represent the known distribution of ionic currents across the cell membrane, as well as the dendritic location of the gap junctions and synaptic inputs. The somatic compartment includes a low-threshold calcium current (I(Ca_l)), an anomalous inward rectifier current (I(h)), a sodium current (I(Na)), and a delayed rectifier potassium current (I(K_dr)). The dendritic compartment contains a high-threshold calcium current (I(Ca_h)), a calcium-dependent potassium current (I(K_Ca)), and a current flowing into other cells through electrical coupling (I(c)). First, kinetic parameters for these currents were set according to previously reported experimental data. Next, the remaining free parameters were determined to account for both static and spiking properties of single olivary neurons in vitro. We then performed a series of simulated pharmacological experiments using bifurcation analysis and extensive two-parameter searches. Consistent with previous studies, we quantitatively demonstrated the major role of I(Ca_l) in spiking excitability. In addition, I(h) had an important modulatory role in the spike generation and period of oscillations, as previously suggested by Bal and McCormick. Finally, we investigated the role of electrical coupling in two coupled spiking cells. Depending on the coupling strength, the hyperpolarization level, and the I(Ca_l) and I(h) modulation, the coupled cells had four different synchronization modes: the cells could be in-phase, phase-shifted, or anti-phase or could exhibit a complex desynchronized spiking mode. Hence these simulation results support the counterintuitive hypothesis that electrical coupling can desynchronize coupled inferior olive cells.

Calcium Channels↗

PubMed1998

A model of activity-dependent formation of cerebellar microzones.

According to modern views of the cerebellum in motor control, each cerebellar functional unit, or microzone, learns how to execute predictive and coordinative control, based on long-term depression of the granule cell-Purkinje cell synapses. In the present paper, in light of recent experimental and theoretical studies on synaptic elimination and cerebellar motor learning, a model of the formation of cerebellar microzones by climbing fiber synaptic elimination is proposed. It is shown that competition for an activity-dependent supply of neurotrophic factor can reproduce the spatio-temporal characteristics of climbing fiber synaptic elimination. It is further shown that when this elimination is accurate, motor coordination can be acquired in an arm reaching task. In view of the results of the present study, several predictions are proposed.

Adult↗

PubMed1998

Role of the cerebellum in reaching movements in humans. I. Distributed inverse dynamics control.

This study focuses on the role of the motor cortex, the spinal cord and the cerebellum in the dynamics stage of the control of arm movement. Currently, two classes of models have been proposed for the neural control of movements, namely the virtual trajectory control hypothesis and the acquisition of internal models of the motor apparatus hypothesis. In the present study, we expand the virtual trajectory model to whole arm reaching movements. This expanded model accurately reproduced slow movements, but faster reaching movements deviated significantly from the planned trajectories, indicating that for fast movements, this model was not sufficient. These results led us to propose a new distributed functional model consistent with behavioural, anatomical and neurophysiological data, which takes into account arm muscles, spinal cord, motor cortex and cerebellum and is consistent with the view that the central nervous system acquires a distributed inverse dynamics model of the arm. Previous studies indicated that the cerebellum compensates for the interaction forces that arise during reaching movements. We show here how the cerebellum may increase the accuracy of reaching movements by compensating for the interaction torques by learning a portion of an inverse dynamics model that refines a basic inverse model in the motor cortex and spinal cord.

Arm↗

PubMed1998

Role of the cerebellum in reaching movements in humans. II. A neural model of the intermediate cerebellum.

The cerebellum is essential for the control of multijoint movements; when the cerebellum is lesioned, the performance error is more than the summed errors produced by single joints. In the companion paper (Schweighofer et al., 1998), a functional anatomical model for visually guided arm movement was proposed. The model comprised a basic feedforward/feedback controller with realistic transmission delays and was connected to a two-link, six-muscle, planar arm. In the present study, we examined the role of the cerebellum in reaching movements by embedding a novel, detailed cerebellar neural network in this functional control model. We could derive realistic cerebellar inputs and the role of the cerebellum in learning to control the arm was assessed. This cerebellar network learned the part of the inverse dynamics of the arm not provided by the basic feedforward/feedback controller. Despite realistically low inferior olive firing rates and noisy mossy fibre inputs, the model could reduce the error between intended and planned movements. The responses of the different cell groups were comparable to those of biological cell groups. In particular, the modelled Purkinje cells exhibited directional tuning after learning and the parallel fibres, due to their length, provide Purkinje cells with the input required for this coordination task. The inferior olive responses contained two different components; the earlier response, locked to movement onset, was always present and the later response disappeared after learning. These results support the theory that the cerebellum is involved in motor learning.

Arm↗

PubMed1996

A model of the cerebellum in adaptive control of saccadic gain. I. The model and its biological substrate.

We review data showing that the cerebellum is required for adaptation of saccadic gain to repeated presentations of dual-step visual targets and thus, presumably, for providing adaptive corrections for the brainstem saccade generator in response to any error created by the open-loop saccadic system. We model the adaptability of the system in terms of plasticity of synapses from parallel fibers to Purkinje cells in cerebellar cortex, stressing the integration of cerebellar cortex and nuclei in microzones as the units for correction of motor pattern generators. We propose a model of the inferior olive as an error detector, and use a 'window of eligibility' to insure that error signals that elicit a corrective movement are used to adjust the original movement, not the secondary movement. In a companion paper we simulate this large, realistic network of neural-like units to study the complex spatiotemporal behavior of neuronal subpopulations implicated in the control and adaptation of saccades.

Adaptation, Physiological↗

PubMed1996

A model of the cerebellum in adaptive control of saccadic gain. II. Simulation results.

A large, realistic cerebellar neural network has been incorporated into a previously developed saccade model. Using this model, in the present paper, we simulate the complex spatiotemporal behavior of the neuronal subpopulations implicated in adaptive saccadic control. Our simulation results are in good agreement with neurophysiological and behavioral data. Furthermore, we suggest several new experiments to test the validity of our predictions on adaptive saccadic control.

Adaptation, Physiological↗

PubMedDate not supplied

A model of cerebellar metaplasticity.

The term "learning rule" in neural network theory usually refers to a rule for the plasticity of a given synapse, whereas metaplasticity involves a "metalearning algorithm" describing higher level control mechanisms for apportioning plasticity across a population of synapses. We propose here that the cerebellar cortex may use metaplasticity, and we demonstrate this by introducing the Cerebellar Adaptive Rate Learning (CARL) algorithm that concentrates learning on those Purkinje cell synapses whose adaptation is most relevant to learning an overall pattern. Our results show that this biologically plausible metalearning algorithm not only improves significantly the learning capability of the cerebellum but is very robust. Finally, we identify several putative neurochemicals that could be involved in a cascade of events leading to adaptive learning rates in Purkinje cell synapses.

Adaptation, Physiological↗