Implications of vestibular nucleus neuron rectification for signal processing in the horizontal vestibuloocular reflex.
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Animals↗
Biomedical subjects
T J Anastasio
Publications and source records attributed to T J Anastasio.
Simulating vestibular compensation using recurrent back-propagation.
Vestibular compensation is simulated as learning in a dynamic neural network model of the horizontal vestibulo-ocular reflex (VOR). The bilateral, three-layered VOR model consists of nonlinear units representing horizontal canal afferents, vestibular nuclei (VN) neurons and eye muscle motoneurons. Dynamic processing takes place via commissural connections that link the VN bilaterally. The intact network is trained, using recurrent back-propagation, to produce the VOR with velocity storage integration. Compensation is simulated by removing vestibular afferent input from one side and retraining the network. The time course of simulated compensation matches that observed experimentally. The behavior of model VN neurons in the compensated network also matches real data, but only if connections at the motoneurons, as well as at the VN, are allowed to be plastic. The dynamic properties of real VN neurons in compensated and normal animals are found to differ when tested with sinusoidal but not with step stimuli. The model reproduces these conflicting data, and suggests that the disagreement may be due to VN neuron nonlinearity.
Algorithms↗
Failure of the oculomotor neural integrator from a discrete midline lesion between the abducens nuclei in the monkey.
Recent anatomical studies indicate that axons of neurons in the vestibular nuclei, projecting to the contralateral abducens nuclei, cross the midline at the abducens level. These axons then give off collaterals to the contralateral vestibular and prepositus nuclei that may be important for the neural integrator that converts eye-velocity to eye-position signals. We disrupted a subset of these commissural projections by making a small midline lesion between the abducens nuclei in a monkey. The vestibulo-ocular reflex and saccades were still present post-lesion, indicating that premotor drive was intact, but the lesion produced severe post-saccadic drift, indicating failure of the neural integrator. We conclude that commissural projections crossing at the abducens level may be important for oculomotor integration.
Abducens Nerve↗
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.
Eye Movements↗
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.
Animals↗
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.
Algorithms↗
A frequency and time domain study of the horizontal and vertical vestibuloocular reflex in the pigeon.
1. The horizontal and vertical vestibuloocular reflex (HVOR and VVOR, respectively) was studied in four chronically instrumented pigeons. Eye movements were measured using the magnetic search-coil technique and were produced by rotation in the dark. During the rotation paradigms, the pigeons were either pharmacologically aroused (using amphetamine) or drug free (normal). The pigeon HVOR and VVOR were tested using step and sinusoidal rotational stimulation. The range of frequencies (0.03-6.0 Hz) and the magnitude of the sinusoidal stimuli were chosen to match those used in a previous study of the responses of semicircular canal primary afferents (SCPAs) in unanesthetized (i.e., normal) pigeons. 2. The gain of the HVOR and VVOR in both normal and aroused pigeons was independent of stimulus magnitude (6-30 degrees/s) over the frequency range tested. In aroused pigeons, the frequency independent gain (G) of the HVOR (G = 0.6) and VVOR (G = 0.9) was roughly twice that for normal pigeons. Pigeon VOR phase under all combinations of orientation and arousal level was independent of stimulus magnitude except at the lowest frequency tested (0.03 Hz). At this frequency, phase lead decreased as stimulus magnitude increased for the HVOR and VVOR in both normal and aroused pigeons. 3. The step and sinusoidal gains were greater for the VVOR than for the HVOR under the same level of arousal. Neither the gain nor the dominant time constant of the VOR (tau vor) differed for rotation direction (clockwise or counterclockwise) for the HVOR or VVOR in normal or aroused pigeons. 4. The mean value of tau vor was 4.0 +/- 0.5 (SE) s as estimated from frequency response data and 4.3 +/- 0.4 s as estimated from step response data for the HVOR and VVOR in both normal and aroused pigeons. In comparison, the mean value of the dominant or cupular time constant (tau c) of normal pigeon SCPAs was approximately 10 s as estimated from frequency response data. These results indicate that tau vor is shorter than tau c in the pigeon. 5. At higher frequencies, the pigeon HVOR and VVOR exhibit an increasing phase lag unaccompanied by a gain change--characteristics produced by a pure time delay. The value of this time delay was about 7 ms for both the HVOR and VVOR in both normal and aroused pigeons. The HVOR and VVOR in normal and aroused pigeons lacked the higher frequency lead characteristics present in the frequency responses of most pigeon SCPAs.
Animals↗
Spontaneous and driven responses of semicircular canal primary afferents in the unanesthetized pigeon.
Spontaneous activity from 120 vestibular semicircular canal (SC) primary afferents and driven responses from 20 SC primary afferents to 2.3-decade bandwidth (0.029-6.152 Hz) sum of sinusoidal angular rotations were characterized in 10 unanesthetized pigeons. The results were compared with those previously gathered from barbiturate anesthetized pigeons. The average spontaneous mean firing rate (MFR) of SC primary afferents in unanesthetized pigeons was found to be 168 impulses per second (I/s), n = 120, and is approximately 80% higher than that for SC primary afferents in anesthetized pigeons [93 I/s, n = 149 (Ref. 9) and 92 I/s, n = 124 (Ref. 22)]. The spontaneous discharge of SC primary afferents from unanesthetized pigeons was classified according to the coefficient of variation (CV) of 512-1,024 interspike intervals (ISIs) into regular (CV less than 0.1), intermediate (0.1 less than or equal to CV less 0.4), and irregular (0.4 less than or equal to CV) categories. The percentages of SC primary afferents falling within each CV category were similar for unanesthetized and anesthetized pigeons (22). The relation between the spontaneous mean ISI and its standard deviation (SD) for SC primary afferents in the unanesthetized pigeon is best described by a power function model, which provides a significantly better fit than does a linear model. The parameters of this power function model are similar for SC primary afferents in unanesthetized and anesthetized pigeons (10). The spontaneous ISIs of individual SC primary afferents in the unanesthetized pigeon were found to be distributed either normally, log-normally, or according to the first passage time of the Wiener-Levy (WL) process. The gain of anterior SC primary afferents at 0.25 Hz is similar for anesthetized (2.93 I X s-1 X deg-1 X s-1, n = 14) (11) and for unanesthetized (3.01 I X s-1 X deg-1 X s-1, n = 14) pigeons. Bode plots constructed from the responses to sum sinusoidal angular accelerations reveal that SC primary afferents in unanesthetized pigeons display a phase lead and gain enhancement at frequencies between 0.6 and 6 Hz.(ABSTRACT TRUNCATED AT 400 WORDS)
Action Potentials↗