Showing papers by "John Porrill published in 2010"

The cerebellar microcircuit as an adaptive filter: experimental and computational evidence

Abstract: Initial investigations of the cerebellar microcircuit inspired the Marr-Albus theoretical framework of cerebellar function. We review recent developments in the experimental understanding of cerebellar microcircuit characteristics and in the computational analysis of Marr-Albus models. We conclude that many Marr-Albus models are in effect adaptive filters, and that evidence for symmetrical long-term potentiation and long-term depression, interneuron plasticity, silent parallel fibre synapses and recurrent mossy fibre connectivity is strikingly congruent with predictions from adaptive-filter models of cerebellar function. This congruence suggests that insights from adaptive-filter theory might help to address outstanding issues of cerebellar function, including both microcircuit processing and extra-cerebellar connectivity.

Synaptic Plasticity in Medial Vestibular Nucleus Neurons: Comparison with Computational Requirements of VOR Adaptation

Abstract: Background: Vestibulo-ocular reflex (VOR) gain adaptation, a longstanding experimental model of cerebellar learning, utilizes sites of plasticity in both cerebellar cortex and brainstem. However, the mechanisms by which the activity of cortical Purkinje cells may guide synaptic plasticity in brainstem vestibular neurons are unclear. Theoretical analyses indicate that vestibular plasticity should depend upon the correlation between Purkinje cell and vestibular afferent inputs, so that, in gain-down learning for example, increased cortical activity should induce long-term depression (LTD) at vestibular synapses. Methodology/Principal Findings: Here we expressed this correlational learning rule in its simplest form, as an anti-Hebbian, heterosynaptic spike-timing dependent plasticity interaction between excitatory (vestibular) and inhibitory (floccular) inputs converging on medial vestibular nucleus (MVN) neurons (input-spike-timing dependent plasticity, iSTDP). To test this rule, we stimulated vestibular afferents to evoke EPSCs in rat MVN neurons in vitro. Control EPSC recordings were followed by an induction protocol where membrane hyperpolarizing pulses, mimicking IPSPs evoked by flocculus inputs, were paired with single vestibular nerve stimuli. A robust LTD developed at vestibular synapses when the afferent EPSPs coincided with membrane hyperpolarisation, while EPSPs occurring before or after the simulated IPSPs induced no lasting change. Furthermore, the iSTDP rule also successfully predicted the effects of a complex protocol using EPSP trains designed to mimic classical conditioning. Conclusions: These results, in strong support of theoretical predictions, suggest that the cerebellum alters the strength of vestibular synapses on MVN neurons through hetero-synaptic, anti-Hebbian iSTDP. Since the iSTDP rule does not depend on post-synaptic firing, it suggests a possible mechanism for VOR adaptation without compromising gaze-holding and VOR performance in vivo.

Sensory Prediction or Motor Control? Application of Marr–Albus Type Models of Cerebellar Function to Classical Conditioning

Abstract: Marr-Albus adaptive filter models of the cerebellum have been applied successfully to a range of sensory and motor control problems. Here we analyze their properties when applied to classical conditioning of the nictitating membrane response in rabbits. We consider a system-level model of eyeblink conditioning based on the anatomy of the eyeblink circuitry, comprising an adaptive filter model of the cerebellum, a comparator model of the inferior olive and a linear dynamic model of the nictitating membrane plant. To our knowledge, this is the first model that explicitly includes all these principal components, in particular the motor plant that is vital for shaping and timing the behavioral response. Model assumptions and parameters were systematically investigated to disambiguate basic computational capacities of the model from features requiring tuning of properties and parameter values. Without such tuning, the model robustly reproduced a range of behaviors related to sensory prediction, by displaying appropriate trial-level associative learning effects for both single and multiple stimuli, including blocking and conditioned inhibition. In contrast, successful reproduction of the real-time motor behavior depended on appropriate specification of the plant, cerebellum and comparator models. Although some of these properties appear consistent with the system biology, fundamental questions remain about how the biological parameters are chosen if the cerebellar microcircuit applies a common computation to many distinct behavioral tasks. It is possible that the response profiles in classical conditioning of the eyeblink depend upon operant contingencies that have previously prevailed, for example in naturally occurring avoidance movements.

The cerebellum as an adaptive filter: a general model?

Abstract: Many functional models of the cerebellar microcircuit are based on the adaptive-filter model first proposed by Fujita. The adaptive filter has powerful signal processing capacities that are suitable for both sensory and motor tasks, and uses a simple and intuitively plausible decorrelation learning rule that offers and account of the evolution of the inferior olive. Moreover, in those cases where the input-output transformations of cerebellar microzones have been sufficiently characterised, they appear to conform to those predicted by the adaptive-filter model. However, these cases are few in number, and comparing the model with the internal operations of the microcircuit itself has not proved straightforward. Whereas some microcircuit features appear compatible with adaptive-filter function, others such as simple granular-layer processing or Purkinje cell bistability, do not. How far these seeming incompatibilities indicate additional computational roles for the cerebellar microcircuit remains to be determined.

Adaptive Cancelation of Self-Generated Sensory Signals in a Whisking Robot

Abstract: Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the often-used tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme.

Nonlinear Dynamic Modeling of Isometric Force Production in Primate Eye Muscle

Abstract: Although the oculomotor plant is usually modeled as a linear system, recent studies of ocular motoneuron behavior have drawn attention to the presence of significant nonlinearities. One source of these is the development of muscle force in response to changes in motoneuron firing rate. Here, we attempt to simulate the production of isometric force by the primate lateral rectus muscle in response to electrical stimulation [A. Fuchs and E. Luschei, “Development of isometric tension in simian extraocular muscle,” J. Physiol., vol. 219, no. 1, pp. 155-166, 1971] by comparing four different modeling approaches. The data could be well fitted either by parameter estimation for physically based models of force production [J. Bobet, E. R. Gossen, and R. B. Stein, “A comparison of models of force production during stimulated isometric ankle dorsiflexion in humans,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 13, no. 4, pp. 444-451, Dec. 2005; E. Mavritsaki, N. Lepora, J. Porrill, C. H. Yeo, and P. Dean, “Response linearity determined by recruitment strategy in detailed model of nictitating membrane control,” Biol. Cybern., vol. 96, no. 1, pp. 39-57, 2007], or by the application of a generic method for nonlinear system identification (the nonlinear autoregressive with exogenous input (NARX) model). These results suggest that nonlinear system identification may be a useful method for modeling more general aspects of muscle function, and provide a basis for distributed models of motor units in extraocular muscle for understanding dynamic oculomotor control. The success of previous linear models points to the potential importance of motor unit recruitment in overcoming nonlinearities in the oculomotor plant.

Can cerebellar input calibrate collicular topographic maps

Abstract: There is substantial evidence that the repeating cerebellar microcircuit implements an adaptive filter algorithm suitable for fine tuning a wide range of sensorimotor skills [1]. Although it is known that the multimodal map layer of superior colliculus receives a massive cerebellar input which directly influences collicular output cells [2], there has been little speculation as to the function of this influence. We suggest here that these cerebellar inputs play a role in calibrating the accuracy of collicular topographic maps. The cerebellum is a plausible candidate for this role for a number of reasons. It is known to be crucial for accuracy of saccades generated by colliculus. It is also known that cerebellar lesions impair prism adaptation of eye movements. A recent imaging study [3] found direct evidence of cerebellar activation during prism adaptation and suggests that "the cerebellum is particularly involved in [establishing] a correct spatial mapping among visuomotor and sensorimotor coordinates systems". A modeling study [3] of collicular visuomotor mapping concludes that the transformation occurs in brainstem with the cerebellum adjusting it for accuracy, stating that "... the importance of the cerebellum has been neglected in previous modeling studies". Here we investigate whether the adaptive filter model of the cerebellar microcircuit which has been so successful in conventional sensorimotor contexts can be applied without change to the very different computational problem of calibrating a topographic map driving an orienting response. We propose a model in which (i) unimodal sensory topographic maps constitute a probabilistic representation of target position and that these maps are optimally combined to produce a multimodal map [4] whose peak activity drives the orienting response, (ii) cerebellar input can bias the position of peak map activity (e.g. by a process such as attentional gain modulation [5]), (iii) the cerebellum receives the sensory information that generates the topographic maps on its mossy fibre inputs, and (iv) information about orienting errors caused by miscalibration is made available to the cerebellum on its climbing fibre inputs and drives cerebellar learning. We demonstrate in simulation that this mechanism can successfully calibrate topographic maps and go on to investigate its computational properties. For example we investigate a fundamental calibration ambiguity in which sensory maps can be miscalibrated in such a way that their effects on the combined estimate cancel. We show that this ambiguity is resolved in a plausible way if error signals are gated whenever a sensor fails to observe the target; a similar gating process has been observed in some motor behaviors [6].We also demonstrate that optimality of the cerebellar learning rule [7] ensures that Purkinje cell synapses carrying cross-talk between sensors are driven to silence, greatly reducing the need to hard-wire the connectivity of parallel fiber inputs to the cerebellar microzones.