Neural network models of velocity storage in the horizontal vestibulo-ocular reflex

The vestibulo-ocular reflex (VOR) produces compensatory eye movements by utilizing head rotational velocity signals from the semicircular canals to control contractions of the extraocular muscles. In mammals, the time course of horizontal VOR is longer than that of the canal signals driving it, revealing the presence of a central integrator known as velocity storage. Although the neurons mediating VOR have been described neurophysiologically, their properties, and the mechanism of velocity storage itself, remain unexplained. Recent models of integration in VOR are based on systems of linear elements, interconnected in arbitrary ways. The present study extends this work by modeling horizontal VOR as a learning network composed of nonlinear model neurons. Network architectures are based on the VOR arc (canal afferents, vestibular nucleus (VN) neurons and extraocular motoneurons) and have both forward and lateral connections. The networks learn to produce velocity storage integration by forming lateral (commissural) inhibitory feedback loops between VN neurons. These loops overlap and interact in a complex way, forming both fast and slow VN pathways. The networks exhibit some of the nonlinear properties of the actual VOR, such as dependency of decay rate and phase lag upon input magnitude, and skewing of the response to higher magnitude sinusoidal inputs. Model VN neurons resemble their real counterparts. Both have increased time constant and gain, and decreased spontaneous rate as compared to canal afferents. Also, both model and real VN neurons exhibit rectification and skew. The results suggest that lateral inhibitory interactions produce velocity storage and also determine the properties of neurons mediating VOR. The neural network models demonstrate how commissural inhibition may be organized along the VOR pathway.     

Testable predictions from realistic neural network simulations of vestibular compensation: integrating the behavioural and physiological data

Neural network simulations have been used previously in the investigation of the horizontal vestibulo-ocular reflex (HVOR) and vestibular compensation. The simulations involved in the present research were based on known anatomy and physiology of the vestibular pathway. This enabled the straightforward comparison of the network response, both in terms of behavioural (eye movement) and physiological (neural activity) data to empirical data obtained from guinea pig. The network simulations matched the empirical data closely both in terms of the static symptoms (spontaneous nystagmus) of unilateral vestibular deafferentation (UVD) as well as in terms of the dynamic symptoms (decrease in VOR gain). The use of multiple versions of the basic network, trained to simulate individual guinea pigs, highlighted the importance of the particular connections: the vestibular ganglion to the type I medial vestibular nucleus (MVN) cells on the contralesional side. It also indicated the significance of the relative firing rate in type I MVN cells which make excitatory connections with abducens cells as contributors to the variability seen in the level of compensated response following UVD. There was an absence of any difference (both in terms of behavioural and neural response) between labyrinthectomised and neurectomised simulations. The fact that a dynamic VOR gain asymmetry remained following the elimination of the spontaneous nystagmus in the network suggested that the amelioration of both the static and dynamic symptoms of UVD may be mediated by a single network. The networks were trained on high acceleration impulse stimuli but displayed the ability to generalise to low frequency, low acceleration sinusoids and closely approximated the behavioural responses to those stimuli. Received: 12 October 1998 / Accepted in revised form: 11 February 1999     

Analysis and Neural Network Modeling of the Nonlinear Correlates of Habituation in the Vestibulo-ocular Reflex

Through the process of habituation, continued exposure to low-frequency (0.01 Hz) rotation in the dark produced suppression of the low-frequency response of the vestibulo-ocular reflex (VOR) in goldfish. The response did not decay gradually, as might be expected from an error-driven learning process, but displayed several nonlinear and nonstationary features. They included asymmetrical response suppression, magnitude-dependent suppression for lower- but not higher-magnitude head rotations, and abrupt-onset suppressions suggestive of a switching mechanism. Microinjection of lidocaine into the vestibulocerebellum of habituated goldfish resulted in a temporary dishabituation. This suggests that the vestibulocerebellum mediates habituation, presumably through Purkinje cell inhibition of vestibular nuclei neurons. The habituated VOR data were simulated with a feed-forward, nonlinear neural network model of the VOR in which only Purkinje cell inhibition of vestibular nuclei neurons was varied. The model suggests that Purkinje cell inhibition may switch in to introduce nonstationarities, and cause asymmetry and magnitude-dependency in the VOR to emerge from the essential nonlinearity of vestibular nuclei neurons.     

Distributed parallel processing in the vertical vestibulo-ocular reflex: Learning networks compared to tensor theory

The vestibulo-ocular reflex (VOR) is capable of producing compensatory eye movements in three dimensions. It utilizes the head rotational velocity signals from the semicircular canals to control the contractions of the extraocular muscles. Since canal and muscle coordinate frames are not orthogonal and differ from one another, a sensorimotor transformation must be produced by the VOR neural network. Tensor theory has been used to construct a linear transformation that can model the three-dimensional behavior of the VOR. But tensor theory does not take the distributed, redundant nature of the VOR neural network into account. It suggests that the neurons subserving the VOR, such as vestibular nucleus neurons, should have specific sensitivity-vectors. Actual data, however, are not in accord. Data from the cat show that the sensitivity-vectors of vestibular nucleus neurons, rather than aligning with any specific vectors, are dispersed widely. As an alternative to tensor theory, we modeled the vertical VOR as a three-layered neural network programmed using the back-propagation learning algorithm. Units in mature networks had divergent sensitivity-vectors which resembled those of actual vestibular nucleus neurons in the cat. This similarity suggests that the VOR sensorimotor transformation may be represented redundantly rather than uniquely. The results demonstrate how vestibular nucleus neurons can encode the VOR sensorimotor transformation in a distributed manner.     

Molecular mechanisms of Brainstem plasticity

Vestibular compensation is the process of behavioral recovery that occurs following unilateral deafferentation of the vestibular nerve fibers (unilateral labyrinthectomy, UL). Since UL results in a permanent loss of vestibular input from the ipsilateral vestibular (VIIIth) nerve, vestibular compensation is attributed to CNS plasticity and has been used as a general model of lesion-induced CNS plasticity. Behavioral recovery from the ocular motor and postural symptoms of UL is correlated with a partial return of resting activity to neurons in the vestibular nucleus (VN) on the deafferented side (the "deafferented VN"), and lesions to the deafferented VN prevent compensation; therefore, the regeneration of resting activity within the deafferented VN is believed to have a causal role in vestibular compensation. The biochemical mechanisms responsible for the adaptive neuronal changes within the deafferented VN are poorly understood. Neuropeptide hormone fragments, such as adrenocorticotrophic hormone (ACTH)-4-10, have been shown to accelerate vestibular compensation and can act directly on some VN neurons in vitro. Antagonists for the N-methyl-D-aspartate (NMDA) receptor have been shown to inhibit vestibular compensation if administered early in the compensation process. Biochemical studies in frog indicate marked alterations in the phosphorylation patterns of several proteins during compensation, and the in vitro phosphorylation of some of these proteins is modulated by ACTH-(1-24), calcium (Ca2+), and calmodulin or protein kinase C. It is therefore possible that ACTH fragments and NMDA antagonists (via their effects on NMDA receptor-mediated Ca2+ channels) modulate vestibular compensation through their action on Ca(2+)-dependent pathways within VN neurons. Recent studies have shown that some Ca2+ channel antagonists and the Ca(2+)-dependent enzyme inhibitor calmidazolium chloride facilitate vestibular compensation. How the regulation of Ca2+ may be related to the neuronal changes responsible for vestibular compensation is unclear at present.     

A matrix analysis for a conjugate vestibulo-ocular reflex

The technique of matrix analysis is used to compare the connectivity between vestibular neurons and oculomotor neurons of the two eyes that would generate a conjugate vestibulo-ocular reflex (VOR). The technique shows that the connectivity is normally anatomically symmetric. The technique is also used to determine the types and loci of adaptation within the VOR that will maintain conjugacy. Adaptation is divided into1) that evoked by changes in visual feedback, which requires VOR or system-specific changes and2) that produced by changes in the canals or muscles, which requires deficit-specific adaptation. In the former case, the adaptation could best be achieved by an additive alteration of the vestibularmotoneuron projections. In the latter case, the appropriate adaptations would be serial, multiplicative changes, applied at the level of the vestibular neurons when the canals are at fault or at the level of the motoneurons of the eye whose muscles are impaired. The analysis thus suggests multiple loci of plasticity within the VOR, specialized for adapting to different deficits.     

The vestibulo-ocular reflex and velocity storage in spinocerebellar ataxia 8

The autosomal dominant spinocerebellar ataxias (SCAs) are a group of neurodegenerative diseases characterized by progressive instability of posture and gait, incoordination, ocular motor dysfunction, and dysarthria due to degeneration of cerebellar and brainstem neurons. Among the more than 20 genetically distinct subtypes, SCA8 is one of several wherein clinical observations indicate that cerebellar dysfunction is primary, and there is little evidence for other CNS involvement. The aim of the present work was to study the decay of the horizontal vestibulo-ocular reflex (VOR) after a short period of constant acceleration to understand the pathophysiology of the VOR due to cerebellar Purkinje cell degeneration in SCA8. The VOR was recorded in patients with genetically defined SCA8 during rotation in the dark. Moderate to severely affected patients had a qualitatively intact VOR, but there were quantitative differences in the gain and dynamics compared to normal controls. During angular velocity ramp rotations, there was a reversal in the direction of the VOR that was more pronounced in SCA8 compared to controls. Modeling studies indicate that there are significant changes in the velocity storage network, including abnormal feedback of an eye position signal into the network that contributes to this reversal. These and other results will help to identify features that are diagnostic for SCA subtypes and provide new information about selective vulnerability of neurons controlling vestibular reflexes.     

The fractional-order dynamics of brainstem vestibulo-oculomotor neurons

The vestibulo-ocular reflex (VOR) and other oculomotor subsystems such as pursuit and saccades are ultimately mediated in the brainstem by premotor neurons in the vestibular and prepositus nuclei that relay eye movement commands to extraocular motoneurons. The premotor neurons receive vestibular signals from canal afferents. Canal afferent frequency responses have a component that can be characterized as a fractional-order differentiation (d ^k x/dt _k where k is a nonnegative real number). This article extends the use of fractional calculus to describe the dynamics of motor and premotor neurons. It suggests that the oculomotor integrator, which converts eye velocity into eye position commands, may be of fractional order. This order is less than one, and the velocity commands have order one or greater, so the resulting net output of motor and premotor neurons can be described as fractional differentiation relative to eye position. The fractional derivative dynamics of motor and premotor neurons may serve to compensate fractional integral dynamics of the eye. Fractional differentiation can be used to account for the constant phase shift across frequencies, and the apparent decrease in time constant as VOR and pursuit frequency increases, that are observed for motor and premotor neurons. Fractional integration can reproduce the time course of motor and premotor neuron saccade-related activity, and the complex dynamics of the eye. Insight into the nature of fractional dynamics can be gained through simulations in which fractional-order differentiators and integrators are approximated by sums of integer-order high-pass and low-pass filters, respectively. Fractional dynamics may be applicable not only to the oculomotor system, but to motor control systems in general.     

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

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.     

The distributed representation of vestibulo-oculomotor signals by brain-stem neurons

The vestibuloocular reflex and other oculomotor functions are subserved by populations of neurons operating in parallel. This distributed aspect of the system's organization has been largely ignored in previous block diagram models. Neurons that transmit oculomotor signals, such as those in the vestibular nucleus (VN), actually combine the different types of signals in a diverse, seemingly random way that could not be predicted from a block diagram. We used the backpropagation learning algorithm to program distributed neural-network models of the vestibulo-oculomotor system. Networks were trained to combine vestibular, pursuit and saccadic eye velocity command signals. The model neurons in these neural networks have diverse combinations of vestibulo-oculomotor signals that are qualitatively similar to those reported for actual VN neurons in the monkey. This similarity implicates a learning mechanism as an organizing influence on the vestibulo-oculomotor system and demonstrates how VN neurons can encode vestibulo-oculomotor signals in a diverse, distributed manner.     

The high number of neurons contributes to the robustness of the locust flight-CPG against parameter variation

 Real pattern-generating networks often consist of more neurons than necessary for the production of a certain rhythm. We investigated the question of whether these neurons contribute to the robustness of a pattern-generating system of using the central pattern generator (CPG) for flight of the locust, generating the deafferented activity pattern of wing elevator and wing depressor motoneurons, as an example of a rhythm-generating system. The neuronal network was reconstructed, based on the known connectivity of the interneurons in the flight CPG, using a biologically orientated network simulator (BioSim 3.0). This simulator allows a physiologically realistic simulation of particular neurons as well as the synaptic connections between them. The flight CPG consists of at least five cyclic loops. The simulation shows that each of them is in principle able to produce a rhythm comparable to the rhythm produced by the whole network, i.e. the ‘deafferented’ flight pattern of elevator and depressor motoneurons. Varying the parameter ‘synaptic strength’ in each of these loops and in the complete system shows that this parameter can be changed within certain ranges without loosing the ability to produce oscillations. These ranges are much smaller in each of the subloops than in the whole network. This result demonstrates that the robustness of the system is increased by supranumerary neurons and connections. Changing the active properties of the simulated neurons so that they are able to produce plateau potentials has no effect on the robustness of the simulated network. Received: 13 April 1994/Accepted in revised form: 15 September 1994     

Neuronal responses to turtle head rotation in vitro

Extracellular recordings were made during vestibular stimulation from an in vitro turtle brain stem in which the temporal bones remained attached. Under visual control, microelectrodes were slowly advanced into the vestibular nucleus (VN) while we rotated the brain and searched for a single isolated unit whose spike activity was modulated by the lateral semicircular canals. In some experiments, responses were shown to be due to stimulation of the lateral canals, either by positioning the brains in forward or backward pitch during horizontal rotation or by plugging the vertical canals with wax. VN neurons usually had low spontaneous activity and rectified sinusoidal responses to sinusoidal stimulation. Spike response histograms were averaged from many stimulus cycles and were then fit to a sine function. The fitted phase and amplitude parameters were plotted relative to stimulus frequency and amplitude. The sample of VN cells were quite heterogeneous. Using stimuli at 1 Hz, however, each cell's response phase was weakly correlated with the slope of the plots of response amplitude versus frequency so that a cell could be categorized as sensitive to velocity or acceleration and as sensitive to ipsiversive or contraversive rotation, depending on whether its phase was near −180°, −90°, 0°, or 90°, and whether the gain exceeded 0.4 spikes/s per °/s. The properties of these VN cells suggest that there is substantial complexity in the vestibular responses at this first site of central vestibular processing. These data are compared to that of other species where such vestibular signals play an important role in oculomotor and spinal reflexes. © 1997 John Wiley & Sons, Inc. J Neurobiol 33: 99–177, 1997     

Primary neurotransmitters and regulatory substances onto vestibular nucleus neurons.

This review article focused on the primary neurotransmitters involved in transmission from the otolith to the vestibular nucleus (VN), especially in relation to the neurotransmission to the VN neurons (gravity-sensitive neurons) activated by tilt stimulation. The medial vestibular nucleus (MVN) neurons were classified in 8 types (alpha-theta) according to the patterns in response to the clockwise and counterclockwise tilt-stimulations. The tilt-induced firing was inhibited by GDEE (a non-selective glutamate receptor antagonist) and/or atropine (a muscarinic receptor antagonist). Thus, glutamate and/or acetylcholine may serve as the primary neurotransmitters. This conclusion is supported by the previous findings that glutamate exists in the vestibular nerve and is released from the nerve besides the presence of glutamate receptor subtypes in the VN. In addition, acetylcholine induced atropine-reversible firing of MVN neurons, and the enzymes involved in acetylcholine synthesis/metabolism are also found in the VN. Furthermore, serotonin was found to inhibit the MVN neuronal activities via the 5-HT1A receptors. As such, the 5-HT1A agonist, tandospirone, may be effective in preventing and/or treating motion sickness and/or space sickness.     

Learning neural connectivity from firing activity: efficient algorithms with provable guarantees on topology

The connectivity of a neuronal network has a major effect on its functionality and role. It is generally believed that the complex network structure of the brain provides a physiological basis for information processing. Therefore, identifying the network’s topology has received a lot of attentions in neuroscience and has been the center of many research initiatives such as Human Connectome Project. Nevertheless, direct and invasive approaches that slice and observe the neural tissue have proven to be time consuming, complex and costly. As a result, the inverse methods that utilize firing activity of neurons in order to identify the (functional) connections have gained momentum recently, especially in light of rapid advances in recording technologies; It will soon be possible to simultaneously monitor the activities of tens of thousands of neurons in real time. While there are a number of excellent approaches that aim to identify the functional connections from firing activities, the scalability of the proposed techniques plays a major challenge in applying them on large-scale datasets of recorded firing activities. In exceptional cases where scalability has not been an issue, the theoretical performance guarantees are usually limited to a specific family of neurons or the type of firing activities. In this paper, we formulate the neural network reconstruction as an instance of a graph learning problem, where we observe the behavior of nodes/neurons (i.e., firing activities) and aim to find the links/connections. We develop a scalable learning mechanism and derive the conditions under which the estimated graph for a network of Leaky Integrate and Fire (LIf) neurons matches the true underlying synaptic connections. We then validate the performance of the algorithm using artificially generated data (for benchmarking) and real data recorded from multiple hippocampal areas in rats.     

A burst-feedback model of fast-phase burst generation during nystagmus

 Vestibular and optokinetic nystagmus are characterized by alternating slow-phase eye rotations that stabilize the retinal image, and fast-phase eye rotations that reset eye position. Nystagmus is coordinated in the brainstem by burst neurons that fire an intense, temporally circumscribed burst that terminates the slow phase and drives the fast phase. This paper demonstrates that such a burst can be generated during nystagmus using a simple neural network model containing only known brainstem neurons and their interconnections. These include the feedback connections of the burst neuron (burst feedback). The burst neuron excites itself directly, and disinhibits itself by inhibiting the pause neuron (positive feedback). It also inhibits itself by inhibiting the vestibular neuron (negative feedback). The burst neuron begins to fire after its inhibitory bias is overcome by excitation from the vestibular neuron, and burst neuron positive feedback then produces an intense burst with an abrupt onset. The burst causes the vestibular and pause neurons to pause. The benefit of the pause neuron loop is that it contributes to burst neuron positive feedback when it is needed at burst onset, but that contribution is eliminated when the pause neuron pauses and opens the loop. The burst can then terminate, with an offset duration proportional to burst amplitude, under the control of burst neuron self-excitation and inhibitory bias. Model neuron behavior is comparable to that of real brainstem neurons. Synchronized bursts can be produced over the population of burst neurons in a distributed version of the network. Variability in connection weights in the distributed network results in variability in prelude activity among burst neurons that is similar to the spread in lead observed for real burst neurons during nystagmus. Received: 11 April 1996 / Accepted in revised form: 6 August 1996     

A learning network model of the neural integrator of the oculomotor system

Certain premotor neurons of the oculomotor system fire at a rate proportional to desired eye velocity. Their output is integrated by a network of neurons to supply an eye positon command to the motoneurons of the extraocular muscles. This network, known as the neural integrator, is calibrated during infancy and then maintained through development and trauma with remarkable precision. We have modeled this system with a self-organizing neural network that learns to integrate vestibular velocity commands to generate appropriate eye movements. It learns by using current eye movement on any given trial to calculate the amount of retinal image slip and this is used as the error signal. The synaptic weights are then changed using a straightforward algorithm that is independent of the network configuration and does not necessitate backwards propagation of information. Minimization of the error in this fashion causes the network to develop multiple positive feedback loops that enable it to integrate a push-pull signal without integrating the background rate on which it rides. The network is also capable of recovering from various lesions and of generating more complicated signals to simulate induced postsaccadic drift and compensation for eye muscle mechanics.     

Efficient fitting of conductance-based model neurons from somatic current clamp

Estimating biologically realistic model neurons from electrophysiological data is a key issue in neuroscience that is central to understanding neuronal function and network behavior. However, directly fitting detailed Hodgkin?CHuxley type model neurons to somatic membrane potential data is a notoriously difficult optimization problem that can require hours/days of supercomputing time. Here we extend an efficient technique that indirectly matches neuronal currents derived from somatic membrane potential data to two-compartment model neurons with passive dendrites. In consequence, this approach can fit semi-realistic detailed model neurons in a few minutes. For validation, fits are obtained to model-derived data for various thalamo-cortical neuron types, including fast/regular spiking and bursting neurons. A key aspect of the validation is sensitivity testing to perturbations arising in experimental data, including sampling rates, inadequately estimated membrane dynamics/channel kinetics and intrinsic noise. We find that maximal conductance estimates and the resulting membrane potential fits diverge smoothly and monotonically from near-perfect matches when unperturbed. Curiously, some perturbations have little effect on the error because they are compensated by the fitted maximal conductances. Therefore, the extended current-based technique applies well under moderately inaccurate model assumptions, as required for application to experimental data. Furthermore, the accompanying perturbation analysis gives insights into neuronal homeostasis, whereby tuning intrinsic neuronal properties can compensate changes from development or neurodegeneration.     

Nonuniformity in the linear network model of the oculomotor integrator produces approximately fractional-order dynamics and more realistic neuron behavior

The oculomotor integrator is a network that is composed of neurons in the medial vestibular nuclei and nuclei prepositus hypoglossi in the brainstem. Those neurons act approximately as fractional integrators of various orders, converting eye velocity commands into signals that are intermediate between velocity and position. The oculomotor integrator has been modeled as a network of linear neural elements, the time constants of which are lengthened by positive feedback through reciprocal inhibition. In this model, in which each neuron reciprocally inhibits its neighbors with the same Gaussian profile, all model neurons behave as identical, first-order, low-pass filters with dynamics that do not match the variable, approximately fractional-order dynamics of the neurons that compose the actual oculomotor integrator. Fractional-order integrators can be approximated by weighted sums of first-order, low-pass filters with diverse, broadly distributed time constants. Dynamic systems analysis reveals that the model integrator indeed has many broadly distributed time constants. However, only one time constant is expressed in the model due to the uniformity of its network connections. If the model network is made nonuniform by removing the reciprocal connections to and from a small number of neurons, then many more time constants are expressed. The dynamics of the neurons in the nonuniform network model are variable, approximately fractional-order, and resemble those of the neurons that compose the actual oculomotor integrator. Completely removing the connections to and from a neuron is equivalent to eliminating it, an operation done previously to demonstrate the robustness of the integrator network model. Ironically, the resulting nonuniform network model, previously supposed to represent a pathological integrator, may in fact represent a healthy integrator containing neurons with realistically variable, approximately fractional-order dynamics. Received: 8 August 1997 / Accepted in revised form: 18 June 1998     

Recovery of the vertical vestibulo-ocular reflex gain in rabbits submitted to bilateral and unilateral visual deprivation from birth

Rabbits were raised in complete darkness from birth to the age of 3 months. At this age, the animals were submitted to dynamic vestibular stimulation consisting of lateral sinusoidal oscillations of different frequencies and fixed amplitude. The vertical VOR, elicited in complete darkness, was then recorded. While the phase of the response was perfectly adequate to ensure head movements compensation, the gain values recorded were clearly reduced with respect to the values obtained in a normally raised control group of the same age. After exposure to light, the visually deprived animals showed a complete recovery of normal VOR gain values in a relatively short period of time. Another group of animals was submitted to monocular prolongation of light deprivation during the fourth month of life. After 2 weeks these rabbits displayed a clear unbalance of the VOR between the two eyes: the eye in which vision was allowed showed a complete recovery of VOR gain values, while the gain of the occluded eye remained unchanged. The present results confirm that visual experience in early life is necessary for a correct development of the VOR. If visual deprivation is limited to the first few months of life, the impairment of the reflex characteristics is completely reversible. Finally, data on monocular deprivation suggest that, in the rabbit, the neural structures which preside to the development of the vertical VOR compensatory properties are lateralized.     

Excitatory amino acid receptors in normal and abnormal vestibular function

Although excitatory amino acid (EAA) receptors have been investigated extensively in the limbic system and neocortex, less is known of the function of EAA receptors in the brainstem. A number of biochemical and electrophysiological studies suggest that the synapse between the ipsilateral vestibular (VIIIth) nerve and the brainstem vestibular nucleus (VN) is mediated by an EAA acting predominantly on kainate or alpha-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid (AMPA) receptors. In addition, there is electrophysiological evidence that input from the contralateral vestibular nerve via the contralateral VN is partially mediated by N-methyl-D-aspartate (NMDA) receptors. Input to the VN from the spinal cord may also be partially mediated by NMDA receptors. All of the electrophysiological studies conducted so far have used in vitro preparations, and it is possible that denervation of the VN during the preparation of an explant or slice causes changes in EAA receptor function. Nonetheless, these results suggest that EAA receptors may be important in many different parts of the vestibular reflex pathways. Studies of the peripheral vestibular system have also shown that EAAs are involved in transmission between the receptor hair cells and the vestibular nerve fibers. A number of recent studies in the area of vestibular plasticity have reported that antagonists for the NMDA receptor subtype disrupt the behavioral recovery that occurs following unilateral deafferentation of the vestibular nerve fibers (vestibular compensation). It has been suggested that vestibular compensation may be owing to an upregulation or increased affinity of NMDA receptors in the VN ipsilateral to the peripheral deafferentation; however; at present, there is no clear evidence to support this hypothesis.