Visual awareness and the cerebellum: possible role of decorrelation control

The Integrated Information Theory of consciousness: A case of mistaken identity

Giulio Tononi's Integrated Information Theory (IIT) proposes explaining consciousness by directly identifying it with integrated information. We examine the construct validity of IIT's measure of consciousness, phi (Φ), by analyzing its formal properties, its relation to key aspects of consciousness, and its co-variation with relevant empirical circumstances. Our analysis shows that IIT's identification of consciousness with the causal efficacy with which differentiated networks accomplish global information transfer (which is what Φ in fact measures) is mistaken. This misidentification has the consequence of requiring the attribution of consciousness to a range of natural systems and artifacts that include, but are not limited to, large-scale electrical power grids, gene-regulation networks, some electronic circuit boards, and social networks. Instead of treating this consequence of the theory as a disconfirmation, IIT embraces it. By regarding these systems as bearers of consciousness ex hypothesi, IIT is led towards the orbit of panpsychist ideation. This departure from science as we know it can be avoided by recognizing the functional misattribution at the heart of IIT's identity claim. We show, for example, what function is actually performed, at least in the human case, by the cortical combination of differentiation with integration that IIT identifies with consciousness. Finally, we examine what lessons may be drawn from IIT's failure to provide a credible account of consciousness for progress in the very active field of research concerned with exploring the phenomenon from formal and neural points of view.

Is Purkinje Neuron Hyperpolarisation Important for Cerebellar Synaptic Plasticity? A Retrospective and Prospective Analysis

Two recent studies have demonstrated that the dendritic Ca²⁺ signal associated with a climbing fibre (CF) input to the cerebellar Purkinje neuron (PN) depends on the membrane potential (V_m). Specifically, when the cell is hyperpolarised, this signal is mediated by T-type voltage-gated Ca²⁺ channels; in contrast, when the cell is firing, the CF-PN signal is mediated by P/Q-type voltage-gated Ca²⁺ channels. When the CF input is paired with parallel fibre (PF) activity, the signal is locally amplified at the sites of PF-activated synapses according to the V_m at the time of the CF input, suggesting that the standing V_m is a critical parameter for the induction of PF synaptic plasticity. In this review, I analyse how the V_m can potentially play a role in cerebellar learning focussing, in particular, on the hyperpolarised state that appears to occur episodically, since PNs are mostly firing under physiological conditions. By revisiting the recent literature reporting in vivo recordings and synaptic plasticity studies, I speculate on how a putative role of the PN V_m can provide an interpretation for the results of these studies.

Seeking a unified framework for cerebellar function and dysfunction: from circuit operations to cognition

Following the fundamental recognition of its involvement in sensory-motor coordination and learning, the cerebellum is now also believed to take part in the processing of cognition and emotion. This hypothesis is recurrent in numerous papers reporting anatomical and functional observations, and it requires an explanation. We argue that a similar circuit structure in all cerebellar areas may carry out various operations using a common computational scheme. On the basis of a broad review of anatomical data, it is conceivable that the different roles of the cerebellum lie in the specific connectivity of the cerebellar modules, with motor, cognitive, and emotional functions (at least partially) segregated into different cerebro-cerebellar loops. We here develop a conceptual and operational framework based on multiple interconnected levels (a meta-levels hypothesis): from cellular/molecular to network mechanisms leading to generation of computational primitives, thence to high-level cognitive/emotional processing, and finally to the sphere of mental function and dysfunction. The main concept explored is that of intimate interplay between timing and learning (reminiscent of the “timing and learning machine” capabilities long attributed to the cerebellum), which reverberates from cellular to circuit mechanisms. Subsequently, integration within large-scale brain loops could generate the disparate cognitive/emotional and mental functions in which the cerebellum has been implicated. We propose, therefore, that the cerebellum operates as a general-purpose co-processor, whose effects depend on the specific brain centers to which individual modules are connected. Abnormal functioning in these loops could eventually contribute to the pathogenesis of major brain pathologies including not just ataxia but also dyslexia, autism, schizophrenia, and depression.

Adaptive-filter models of the cerebellum: computational analysis

Many current models of the cerebellar cortical microcircuit are equivalent to an adaptive filter using the covariance learning rule. The adaptive filter is a development of the original Marr-Albus framework that deals naturally with continuous time-varying signals, thus addressing the issue of 'timing' in cerebellar function, and it can be connected in a variety of ways to other parts of the system, consistent with the microzonal organization of cerebellar cortex. However, its computational capacities are not well understood. Here we summarise the results of recent work that has focused on two of its intrinsic properties. First, an adaptive filter seeks to decorrelate its (mossy fibre) inputs from a (climbing fibre) teaching signal. This procedure can be used both for sensory processing, e.g. removal of interference from sensory signals, and for learning accurate motor commands, by decorrelating an efference copy of those commands from a sensory signal of inaccuracy. As a model of the cerebellum the adaptive filter thus forms a natural link between events at the cellular level, such as forms of synaptic plasticity and the learning rules they embody, and intelligent behaviour at the system level. Secondly, it has been shown that the covariance learning rule enables the filter to handle input and intrinsic noise optimally. Such optimality may underlie the recently described role of the cerebellum in producing accurate smooth pursuit eye movements in the face of sensory noise. Moreover, it has the consequence of driving most input weights to very small values, consistent with experimental data that many parallel-fibre synapses are normally silent. The effectiveness of silent synapses can only be altered by LTP, so learning tasks depending on a reduction of Purkinje cell firing require the synapses to be embedded in a second, inhibitory pathway from parallel fibre to Purkinje cell. This pathway and the appropriate climbing-fibre related plasticity have been described experimentally, and its presence has implications for asymmetries and hysteresis in behavioural learning rates that are also consistent with experimental observations. These computational properties of the adaptive filter suggest that it is both powerful and realistic enough to be a suitable candidate model of the cerebellar cortical microcircuit.

Recurrent cerebellar loops simplify adaptive control of redundant and nonlinear motor systems

We have described elsewhere an adaptive filter model of cerebellar learning in which the cerebellar microcircuit acts to decorrelate motor commands from their sensory consequences (Dean, Porrill, & Stone, 2002). Learning stability required the cerebellar microcircuit to be embedded in a recurrent loop, and this has been shown to lead to a simple and modular adaptive control architecture when applied to the linearized 3D vestibular ocular reflex (Porrill, Dean, & Stone, 2004). Here we investigate the properties of recurrent loop connectivity in the case of redundant and nonlinear motor systems and illustrate them using the example of kinematic control of a simulated two-joint robot arm. We demonstrate that (1) the learning rule does not require unavailable motor error signals or complex neural reference structures to estimate such signals (i.e., it solves the motor error problem) and (2) control of redundant systems is not subject to the nonconvexity problem in which incorrect average motor commands are learned for end-effector positions that can be accessed in more than one arm configuration. These properties suggest a central functional role for the closed cerebellar loops, which have been shown to be ubiquitous in motor systems (e.g., Kelly & Strick, 2003).

Long Time-Constant Behavior of the Oculomotor Plant in Barbiturate-Anesthetized Primate

The mechanics of the extraocular muscles and orbital tissue (“oculomotor plant”) can be approximated by a small number of viscoelastic (Voigt) elements in series. Recent analysis of the eye's return from displacement in lightly anesthetized rhesus monkeys has suggested a four-element plant model with time constants (TCs) of ∼0.01, 0.1, 1, and 10 s. To demonstrate directly the presence of long (1,10 s) TC elements and to assess their contribution quantitatively, horizontal eye displacement was induced in Cynomolgus monkeys under deep barbiturate anesthesia that prevented interference from spontaneous eye movements. The displacement was maintained for either a prolonged (30 s) or brief (0.2 s) period before release. Return to resting position took 20–30 s after prolonged displacement but only 1–2 s after brief displacement, consistent with the presence of long TC elements that would only be substantially stretched in the former condition. Quantitative fitting of the release curves after prolonged displacement indicated that the two long TC elements contribute a substantial proportion (∼30%) of the total plant compliance. A model based on the estimated compliance values is shown to account quantitatively both for our release data and for Goldstein and Robinson's data on hysteresis of ocular motoneuron firing rates measured after centripetal saccades following prolonged eccentric fixation. Long time-constant elements in the plant thus make a substantial contribution to some types of eye movement, and their inclusion in plant models can help interpret the firing patterns of single units in the oculomotor system.

Animal models of focal dystonia

Animal models indicate that the abnormal movements of focal dystonia result from disordered sensorimotor integration. Sensorimotor integration involves a comparison of sensory information resulting from a movement with the sensory information expected from the movement. Unanticipated sensory signals identified by sensorimotor processing serve as signals to modify the ongoing movement or the planning for subsequent movements. Normally, this process is an effective mechanism to modify neural commands for ongoing movement or for movement planning. Animal models of the focal dystonias spasmodic torticollis, writer's cramp, and benign essential blepharospasm reveal different dysfunctions of sensorimotor integration through which dystonia can arise. Animal models of spasmodic torticollis demonstrate that modifications in a variety of regions are capable of creating abnormal head postures. These data indicate that disruption of neural signals in one structure may mutate the activity pattern of other elements of the neural circuits for movement. The animal model of writer's cramp demonstrates the importance of abnormal sensory processing in generating dystonic movements. Animal models of blepharospasm illustrate how disrupting motor adaptation can produce dystonia. Together, these models show mechanisms by which disruptions in sensorimotor integration can create dystonic movements.

Evidence for wide range of time scales in oculomotor plant dynamics: implications for models of eye-movement control

Oculomotor-plant dynamics are not well characterised, despite their importance for modelling eye-movement control. We analysed the time course of the globe's return after horizontal displacements in three rhesus monkeys lightly anaesthetised with ketamine. The eye-position traces were well fitted by a sum of four exponentials (time constants 0.012, 0.099, 0.46, 7.8 s). The two long time-constant terms accounted for 25% of plant compliance, and led to a model that accounted for hitherto unexplained features of ocular motoneuron firing such as (i) hysteresis, and (ii) the inability of a 2 time-constant model to fit data for both fast and slow eye-movements.

Anatomical and physiological foundations of cerebellar information processing

A coordinated movement is easy to recognize, but we know little about how it is achieved. In search of the neural basis of coordination, we present a model of spinocerebellar interactions in which the structure-functional organizing principle is a division of the cerebellum into discrete microcomplexes. Each microcomplex is the recipient of a specific motor error signal - that is, a signal that conveys information about an inappropriate movement. These signals are encoded by spinal reflex circuits and conveyed to the cerebellar cortex through climbing fibre afferents. This organization reveals salient features of cerebellar information processing, but also highlights the importance of systems level analysis for a fuller understanding of the neural mechanisms that underlie behaviour.