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N Schmajuk

Publications and source records attributed to N Schmajuk.

5 recordsLinked to original sources
PubMed1992

Simulation of adaptive mechanisms in the vestibulo-ocular reflex.

The vestibulo-ocular reflex (VOR), which stabilizes the eyes in space during head movements, can undergo adaptive modification to maintain retinal stability in response to natural or experimental challenges. A number of models and neural sites have been proposed to account for this adaptation but these do not fully explain how the nervous system can detect and correct errors in both gain and phase of the VOR. This paper presents a general error correction algorithm based on the multiplicative combination of three signals (retinal slip velocity, head position, head velocity) directly relevant to processing of the VOR. The algorithm is highly specific, requiring the combination of particular sets of signals to achieve compensation. It is robust, with essentially perfect compensation observed for all gain (0.25X - 4.0X) and phase (-180 degrees - +180 degrees) errors tested. Output of the model closely resembles behavioral data from both gain and phase adaptation experiments in a variety of species. Imposing physiological constraints (no negative activation levels or changes in the sign of unit weights) does not alter the effectiveness of the algorithm. These results suggest that the mechanisms implemented in our model correspond to those implemented in the brain of the behaving organism. Predictions concerning the nature of the adaptive process are specific enough to permit experimental verification using electrophysiological techniques. In addition, the model provides a strategy for adaptive control of any first order mechanical system.

Adaptation, Physiological↗
PubMed1992

Vestibuloocular reflex arc analysis using an experimentally constrained neural network.

The primary function of the vestibuloocular reflex (VOR) is to maintain the stability of retinal images during head movements. This function is expressed through a complex array of dynamic and adaptive characteristics whose essential physiological basis is a disynaptic arc. We present a model of normal VOR function using a simple neural network architecture constrained by the physiological and anatomical characteristics of this disynaptic reflex arc. When tuned using a method of global optimization, this network is capable of exhibiting the broadband response characteristics observed in behavioral tests of VOR function. Examination of the internal units in the network show that this performance is achieved by rediscovering the solution to VOR processing first proposed by Skavenski and Robinson (1973). Type I units at the intermediate level of the network possess activation characteristics associated with either pure position or pure velocity. When the network is made more complex either through adding more pairs of internal units or an additional level of units, the characteristic division of unit activation properties into position and velocity types remains unchanged. Although simple in nature, the results of our simulations reinforce the validity of bottom-up approaches to modeling of neutral function. In addition, the architecture of the network is consistent with current ideas on the characteristics and site of adaptation of the reflex and should be compatible with current theories regarding learning rules for synaptic modification during VOR adaptation.

Computer Simulation↗
PubMedDate not supplied

Predictive regulation of associative learning in a neural network by reinforcement and attentive feedback.

At least four types of learning processes are relevant in the present paper: learning of conditioned reinforcement, incentive motivation, sensory expectancy, and motor command. These several types of learning processes, which operate on a slow time scale, regulate and are regulated by rapidly fluctuating limited capacity STM representations of sensory events. The theory suggest how nonlinear feedback interactions among these fast information processing mechanisms and slow learning mechanisms participate in different conditioning paradigms, and actively regulate learning and memory to generate predictive internal representations of external environmental contingencies.

Animals↗