Thomas Anastasio | University of Illinois at Urbana-Champaign (original) (raw)

Papers by Thomas Anastasio

Research paper thumbnail of Predicting the Potency of Anti-Alzheimer’s Drug Combinations Using Machine Learning

Processes

Clinical trials of single drugs intended to slow the progression of Alzheimer’s Disease (AD) have... more Clinical trials of single drugs intended to slow the progression of Alzheimer’s Disease (AD) have been notoriously unsuccessful. Combinations of repurposed drugs could provide effective treatments for AD. The challenge is to identify potentially effective combinations. To meet this challenge, machine learning (ML) was used to extract the knowledge from two leading AD databases, and then “the machine” predicted which combinations of the drugs in common between the two databases would be the most effective as treatments for AD. Specifically, three-layered artificial neural networks (ANNs) with compound, gated units in their internal layer were trained using ML to predict the cognitive scores of participants, separately in either database, given other data fields including age, demographic variables, comorbidities, and drugs taken. The predictions from the separately trained ANNs were statistically highly significantly correlated. The best drug combinations, jointly determined from bot...

Research paper thumbnail of Predicting the potency of anti-Alzheimer drug combinations using machine learning

ABSTRACTBACKGROUNDClinical trials of single drugs for the treatment of Alzheimer Disease (AD) hav... more ABSTRACTBACKGROUNDClinical trials of single drugs for the treatment of Alzheimer Disease (AD) have been notoriously unsuccessful. Combinations of repurposed drugs could provide effective treatments for AD. The challenge is to identify potentially potent combinations.OBJECTIVETo use machine learning (ML) to extract the knowledge from two leading AD databases, and then use the machine to predict which combinations of the drugs in common between the two databases would be the most effective as treatments for AD.METHODSThree-layered neural networks (NNs) having compound, gated units in their internal layer were trained using ML to predict the cognitive scores of participants in either database, given the other data fields including age, demographic variables, comorbidities, and drugs taken.RESULTSThe predictions from the separately trained NNs were strongly correlated. The best drug combinations, jointed determined from both sets of predictions, were high in NSAID, anticoagulant, lipid-...

Research paper thumbnail of Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant

European Neuropsychopharmacology

Research paper thumbnail of Computational Analysis of Therapeutic Neuroadaptation to Chronic Antidepressant in a Model of the Monoaminergic Neurotransmitter and Stress Hormone Systems

Frontiers in Pharmacology

The clinical practice of selective serotonin reuptake inhibitor (SSRI) augmentation relies heavil... more The clinical practice of selective serotonin reuptake inhibitor (SSRI) augmentation relies heavily on trial-and-error. Unfortunately, the drug combinations prescribed today fail to provide relief for many depressed patients. In order to identify potentially more effective treatments, we developed a computational model of the monoaminergic neurotransmitter and stress-steroid systems that neuroadapts to chronic administration of combinations of antidepressant drugs and hormones by adjusting the strengths of its transmitter-system components (TSCs). We used the model to screen 60 chronically administered drug/ hormone pairs and triples, and identified as potentially therapeutic those combinations that raised the monoamines (serotonin, norepinephrine, and dopamine) but lowered cortisol following neuroadaptation in the model. We also evaluated the contributions of individual and pairs of TSCs to therapeutic neuroadaptation with chronic SSRI using sensitivity, correlation, and linear temporal-logic analyses. All three approaches revealed that therapeutic neuroadaptation to chronic SSRI is an overdetermined process that depends on multiple TSCs, providing a potential explanation for the clinical finding that no single antidepressant regimen alleviates depressive symptoms in all patients.

Research paper thumbnail of Computational Model of Antidepressant Response Heterogeneity as Multi-pathway Neuroadaptation

Frontiers in Pharmacology

Current hypotheses cannot fully explain the clinically observed heterogeneity in antidepressant r... more Current hypotheses cannot fully explain the clinically observed heterogeneity in antidepressant response. The therapeutic latency of antidepressants suggests that therapeutic outcomes are achieved not by the acute effects of the drugs, but rather by the homeostatic changes that occur as the brain adapts to their chronic administration. We present a computational model that represents the known interactions between the monoaminergic neurotransmitter-producing brain regions and associated non-monoaminergic neurotransmitter systems, and use the model to explore the possible ways in which the brain can homeostatically adjust to chronic antidepressant administration. The model also represents the neuron-specific neurotransmitter receptors that are known to adjust their strengths (expressions or sensitivities) in response to chronic antidepressant administration, and neuroadaptation in the model occurs through sequential adjustments in these receptor strengths. The main result is that the model can reach similar levels of adaptation to chronic administration of the same antidepressant drug or combination along many different pathways, arriving correspondingly at many different receptor strength configurations, but not all of those adapted configurations are also associated with therapeutic elevations in monoamine levels. When expressed as the percentage of adapted configurations that are also associated with elevations in one or more of the monoamines, our modeling results largely agree with the percentage efficacy rates of antidepressants and antidepressant combinations observed in clinical trials. Our neuroadaptation model provides an explanation for the clinical reports of heterogeneous outcomes among patients chronically administered the same antidepressant drug regimen.

Research paper thumbnail of A geometric method for eigenvalue problems with low-rank perturbations

Royal Society Open Science

We consider the problem of finding the spectrum of an operator taking the form of a low-rank (ran... more We consider the problem of finding the spectrum of an operator taking the form of a low-rank (rank one or two) non-normal perturbation of a well-understood operator, motivated by a number of problems of applied interest which take this form. We use the fact that the system is a low-rank perturbation of a solved problem, together with a simple idea of classical differential geometry (the envelope of a family of curves) to completely analyse the spectrum. We use these techniques to analyse three problems of this form: a model of the oculomotor integrator due to Anastasio & Gad (2007 J. Comput. Neurosci. 22 , 239–254. ( doi:10.1007/s10827-006-0010-x )), a continuum integrator model, and a non-local model of phase separation due to Rubinstein & Sternberg (1992 IMA J. Appl. Math. 48 , 249–264. ( doi:10.1093/imamat/48.3.249 )).

Research paper thumbnail of Exploring the Correlation between the Cognitive Benefits of Drug Combinations in a Clinical Database and the Efficacies of the Same Drug Combinations Predicted from a Computational Model

Journal of Alzheimer's Disease

Identification of drug combinations that could be effective in Alzheimer's disease treatment is m... more Identification of drug combinations that could be effective in Alzheimer's disease treatment is made difficult by the sheer number of possible combinations. This analysis identifies as potentially therapeutic those drug combinations that rank highest when their efficacy is determined jointly from two independent data sources. Estimates of the efficacy of the same drug combinations were derived from a clinical dataset on cognitively impaired elderly participants and from pre-clinical data, in the form of a computational model of neuroinflammation. Linear regression was used to show that the two sets of estimates were correlated, and to rule out confounds. The ten highest ranking, jointly determined drug combinations most frequently consisted of COX2 inhibitors and aspirin, along with various antihypertensive medications. Ten combinations of from five to nine drugs, and the three-drug combination of a COX2 inhibitor, aspirin, and a calcium-channel blocker, are discussed as candidates for consideration in future pre-clinical and clinical studies.

Research paper thumbnail of Computational Analysis of Therapeutic Neuroadaptation to Chronic Antidepressant in a Model of the Monoaminergic Neurotransmitter, Stress Hormone, and Male Sex Hormone Systems

Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of... more Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of chronic administration of a selective serotonin reuptake inhibitor (SSRI), which is the first-line depression treatment. Unfortunately, many depressed patients still fail to respond even after months to years of searching to find an effective combination. To aid in the identification of potentially affective antidepressant combinations, we created a computational model of the monoaminergic neurotransmitter (serotonin, norepinephrine, and dopamine), stress-hormone (cortisol), and male sex-hormone (testosterone) systems. The model was trained via machine learning to represent a broad set of empirical observations. Neuroadaptation to chronic drug administration was simulated through incremental adjustments in model parameters that corresponded to key regulatory components of the neurotransmitter and neurohormone systems. Analysis revealed that neuroadaptation in the model depended on all o...

Research paper thumbnail of Computational Analysis of Therapeutic Neuroadaptation to Chronic Antidepressant in a Model of the Monoaminergic Neurotransmitter and Stress Hormone Systems

The clinical practice of selective serotonin reuptake inhibitor (SSRI) augmentation relies heavil... more The clinical practice of selective serotonin reuptake inhibitor (SSRI) augmentation relies heavily on clinical judgment and trial-and-error. Unfortunately, the drug combinations prescribed today fail to provide relief for all treatment-resistant depressed patients. In order to identify potentially more effective treatments, we developed a computational model of the monoaminergic neurotransmitter and stress-steroid systems that neuroadapts to chronic administration of combinations of antidepressant drugs and hormones by adjusting the strengths of its transmitter-system components (TSCs). We used the model to screen 60 chronically administered drug/hormone pairs and triples, and identified as potentially therapeutic those combinations that raised the monoamines (serotonin, norepinephrine, and dopamine) but lowered cortisol following neuroadaptation in the model. We also evaluated the contributions of individual and pairs of TSCs to therapeutic neuroadaptation with chronic SSRI using s...

Research paper thumbnail of Knowledge-driven computational modeling in Alzheimer's research: Current state and future trends

Research paper thumbnail of Instabilities in Eye Movement Control: A Model of Periodic Alternating Nystagmus

Research paper thumbnail of Distributed Parallel Processing in the Vertical Vestibulo-Ocular Reflex: Learning Networks Compared to Tensor Theory

Biological Cybernetics, Feb 1, 1990

The vestibulo-ocular reflex (VOR) is capable of producing compensatory eye movements in three dim... more 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.

Research paper thumbnail of A Hybrid NN-Bayesian Architecture for Information Fusion

Proceedings 1998 International Conference on Image Processing Icip98, Oct 4, 1998

Abstract This paper discusses a novel technique for information fusion. Specifically, a formula i... more Abstract This paper discusses a novel technique for information fusion. Specifically, a formula is derived for estimation of the joint probabilities in the maximum entropy sense. In addition, neural networks are used to estimate conditional probabilities required in the ...

Research paper thumbnail of The fractional-order dynamics of brainstem vestibulo-oculomotor neurons

Biological Cybernetics, Feb 1, 1994

The vestibulo-ocular reflex (VOR) and other oculomotor subsystems such as pursuit and saccades ar... more 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 fractionalorder differentiation (dkx/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.

Research paper thumbnail of Learning in the Vestibular System: Simulations of Vestibular Compensation Using Recurrent Back-Propagation

Research paper thumbnail of Computer Modeling in Neuroscience: From Imperative to Declarative Programming

Lecture Notes in Computer Science, 2015

Research paper thumbnail of A Recurrent Neural Network Model of Velocity Storage in the Vestibulo-Ocular Reflex

Research paper thumbnail of Method for determination of spatial target probability using a model of multisensory processing by the brain

Research paper thumbnail of Neutral network models of velocity storage in the horizontal vestibulo-ocular reflex

Biological Cybernetics, 1991

Research paper thumbnail of Instabilities in Eye Movement Control: A Model of Periodic Alternating Nystagmus

Neural Information Processing Systems, 1997

Nystagmus is a pattern of eye movement characterized by smooth rotations of the eye in one direct... more Nystagmus is a pattern of eye movement characterized by smooth rotations of the eye in one direction and rapid rotations in the opposite direction that reset eye position. Periodic alternating nystagmus (PAN) is a form of uncontrollable nystagmus that has been described as an unstable but amplitude-limited oscillation. PAN has been observed previously only in subjects with vestibulo-cerebellar damage. We describe results in which PAN can be produced in normal subjects by prolonged rotation in darkness. We propose a new model in which the neural circuits that control eye movement are inherently unstable, but this instability is kept in check under normal circumstances by the cerebellum. Circumstances which alter this cerebellar restraint, such as vestibulocerebellar damage or plasticity due to rotation in darkness, can lead to PAN.

Research paper thumbnail of Predicting the Potency of Anti-Alzheimer’s Drug Combinations Using Machine Learning

Processes

Clinical trials of single drugs intended to slow the progression of Alzheimer’s Disease (AD) have... more Clinical trials of single drugs intended to slow the progression of Alzheimer’s Disease (AD) have been notoriously unsuccessful. Combinations of repurposed drugs could provide effective treatments for AD. The challenge is to identify potentially effective combinations. To meet this challenge, machine learning (ML) was used to extract the knowledge from two leading AD databases, and then “the machine” predicted which combinations of the drugs in common between the two databases would be the most effective as treatments for AD. Specifically, three-layered artificial neural networks (ANNs) with compound, gated units in their internal layer were trained using ML to predict the cognitive scores of participants, separately in either database, given other data fields including age, demographic variables, comorbidities, and drugs taken. The predictions from the separately trained ANNs were statistically highly significantly correlated. The best drug combinations, jointly determined from bot...

Research paper thumbnail of Predicting the potency of anti-Alzheimer drug combinations using machine learning

ABSTRACTBACKGROUNDClinical trials of single drugs for the treatment of Alzheimer Disease (AD) hav... more ABSTRACTBACKGROUNDClinical trials of single drugs for the treatment of Alzheimer Disease (AD) have been notoriously unsuccessful. Combinations of repurposed drugs could provide effective treatments for AD. The challenge is to identify potentially potent combinations.OBJECTIVETo use machine learning (ML) to extract the knowledge from two leading AD databases, and then use the machine to predict which combinations of the drugs in common between the two databases would be the most effective as treatments for AD.METHODSThree-layered neural networks (NNs) having compound, gated units in their internal layer were trained using ML to predict the cognitive scores of participants in either database, given the other data fields including age, demographic variables, comorbidities, and drugs taken.RESULTSThe predictions from the separately trained NNs were strongly correlated. The best drug combinations, jointed determined from both sets of predictions, were high in NSAID, anticoagulant, lipid-...

Research paper thumbnail of Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant

European Neuropsychopharmacology

Research paper thumbnail of Computational Analysis of Therapeutic Neuroadaptation to Chronic Antidepressant in a Model of the Monoaminergic Neurotransmitter and Stress Hormone Systems

Frontiers in Pharmacology

The clinical practice of selective serotonin reuptake inhibitor (SSRI) augmentation relies heavil... more The clinical practice of selective serotonin reuptake inhibitor (SSRI) augmentation relies heavily on trial-and-error. Unfortunately, the drug combinations prescribed today fail to provide relief for many depressed patients. In order to identify potentially more effective treatments, we developed a computational model of the monoaminergic neurotransmitter and stress-steroid systems that neuroadapts to chronic administration of combinations of antidepressant drugs and hormones by adjusting the strengths of its transmitter-system components (TSCs). We used the model to screen 60 chronically administered drug/ hormone pairs and triples, and identified as potentially therapeutic those combinations that raised the monoamines (serotonin, norepinephrine, and dopamine) but lowered cortisol following neuroadaptation in the model. We also evaluated the contributions of individual and pairs of TSCs to therapeutic neuroadaptation with chronic SSRI using sensitivity, correlation, and linear temporal-logic analyses. All three approaches revealed that therapeutic neuroadaptation to chronic SSRI is an overdetermined process that depends on multiple TSCs, providing a potential explanation for the clinical finding that no single antidepressant regimen alleviates depressive symptoms in all patients.

Research paper thumbnail of Computational Model of Antidepressant Response Heterogeneity as Multi-pathway Neuroadaptation

Frontiers in Pharmacology

Current hypotheses cannot fully explain the clinically observed heterogeneity in antidepressant r... more Current hypotheses cannot fully explain the clinically observed heterogeneity in antidepressant response. The therapeutic latency of antidepressants suggests that therapeutic outcomes are achieved not by the acute effects of the drugs, but rather by the homeostatic changes that occur as the brain adapts to their chronic administration. We present a computational model that represents the known interactions between the monoaminergic neurotransmitter-producing brain regions and associated non-monoaminergic neurotransmitter systems, and use the model to explore the possible ways in which the brain can homeostatically adjust to chronic antidepressant administration. The model also represents the neuron-specific neurotransmitter receptors that are known to adjust their strengths (expressions or sensitivities) in response to chronic antidepressant administration, and neuroadaptation in the model occurs through sequential adjustments in these receptor strengths. The main result is that the model can reach similar levels of adaptation to chronic administration of the same antidepressant drug or combination along many different pathways, arriving correspondingly at many different receptor strength configurations, but not all of those adapted configurations are also associated with therapeutic elevations in monoamine levels. When expressed as the percentage of adapted configurations that are also associated with elevations in one or more of the monoamines, our modeling results largely agree with the percentage efficacy rates of antidepressants and antidepressant combinations observed in clinical trials. Our neuroadaptation model provides an explanation for the clinical reports of heterogeneous outcomes among patients chronically administered the same antidepressant drug regimen.

Research paper thumbnail of A geometric method for eigenvalue problems with low-rank perturbations

Royal Society Open Science

We consider the problem of finding the spectrum of an operator taking the form of a low-rank (ran... more We consider the problem of finding the spectrum of an operator taking the form of a low-rank (rank one or two) non-normal perturbation of a well-understood operator, motivated by a number of problems of applied interest which take this form. We use the fact that the system is a low-rank perturbation of a solved problem, together with a simple idea of classical differential geometry (the envelope of a family of curves) to completely analyse the spectrum. We use these techniques to analyse three problems of this form: a model of the oculomotor integrator due to Anastasio & Gad (2007 J. Comput. Neurosci. 22 , 239–254. ( doi:10.1007/s10827-006-0010-x )), a continuum integrator model, and a non-local model of phase separation due to Rubinstein & Sternberg (1992 IMA J. Appl. Math. 48 , 249–264. ( doi:10.1093/imamat/48.3.249 )).

Research paper thumbnail of Exploring the Correlation between the Cognitive Benefits of Drug Combinations in a Clinical Database and the Efficacies of the Same Drug Combinations Predicted from a Computational Model

Journal of Alzheimer's Disease

Identification of drug combinations that could be effective in Alzheimer's disease treatment is m... more Identification of drug combinations that could be effective in Alzheimer's disease treatment is made difficult by the sheer number of possible combinations. This analysis identifies as potentially therapeutic those drug combinations that rank highest when their efficacy is determined jointly from two independent data sources. Estimates of the efficacy of the same drug combinations were derived from a clinical dataset on cognitively impaired elderly participants and from pre-clinical data, in the form of a computational model of neuroinflammation. Linear regression was used to show that the two sets of estimates were correlated, and to rule out confounds. The ten highest ranking, jointly determined drug combinations most frequently consisted of COX2 inhibitors and aspirin, along with various antihypertensive medications. Ten combinations of from five to nine drugs, and the three-drug combination of a COX2 inhibitor, aspirin, and a calcium-channel blocker, are discussed as candidates for consideration in future pre-clinical and clinical studies.

Research paper thumbnail of Computational Analysis of Therapeutic Neuroadaptation to Chronic Antidepressant in a Model of the Monoaminergic Neurotransmitter, Stress Hormone, and Male Sex Hormone Systems

Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of... more Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of chronic administration of a selective serotonin reuptake inhibitor (SSRI), which is the first-line depression treatment. Unfortunately, many depressed patients still fail to respond even after months to years of searching to find an effective combination. To aid in the identification of potentially affective antidepressant combinations, we created a computational model of the monoaminergic neurotransmitter (serotonin, norepinephrine, and dopamine), stress-hormone (cortisol), and male sex-hormone (testosterone) systems. The model was trained via machine learning to represent a broad set of empirical observations. Neuroadaptation to chronic drug administration was simulated through incremental adjustments in model parameters that corresponded to key regulatory components of the neurotransmitter and neurohormone systems. Analysis revealed that neuroadaptation in the model depended on all o...

Research paper thumbnail of Computational Analysis of Therapeutic Neuroadaptation to Chronic Antidepressant in a Model of the Monoaminergic Neurotransmitter and Stress Hormone Systems

The clinical practice of selective serotonin reuptake inhibitor (SSRI) augmentation relies heavil... more The clinical practice of selective serotonin reuptake inhibitor (SSRI) augmentation relies heavily on clinical judgment and trial-and-error. Unfortunately, the drug combinations prescribed today fail to provide relief for all treatment-resistant depressed patients. In order to identify potentially more effective treatments, we developed a computational model of the monoaminergic neurotransmitter and stress-steroid systems that neuroadapts to chronic administration of combinations of antidepressant drugs and hormones by adjusting the strengths of its transmitter-system components (TSCs). We used the model to screen 60 chronically administered drug/hormone pairs and triples, and identified as potentially therapeutic those combinations that raised the monoamines (serotonin, norepinephrine, and dopamine) but lowered cortisol following neuroadaptation in the model. We also evaluated the contributions of individual and pairs of TSCs to therapeutic neuroadaptation with chronic SSRI using s...

Research paper thumbnail of Knowledge-driven computational modeling in Alzheimer's research: Current state and future trends

Research paper thumbnail of Instabilities in Eye Movement Control: A Model of Periodic Alternating Nystagmus

Research paper thumbnail of Distributed Parallel Processing in the Vertical Vestibulo-Ocular Reflex: Learning Networks Compared to Tensor Theory

Biological Cybernetics, Feb 1, 1990

The vestibulo-ocular reflex (VOR) is capable of producing compensatory eye movements in three dim... more 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.

Research paper thumbnail of A Hybrid NN-Bayesian Architecture for Information Fusion

Proceedings 1998 International Conference on Image Processing Icip98, Oct 4, 1998

Abstract This paper discusses a novel technique for information fusion. Specifically, a formula i... more Abstract This paper discusses a novel technique for information fusion. Specifically, a formula is derived for estimation of the joint probabilities in the maximum entropy sense. In addition, neural networks are used to estimate conditional probabilities required in the ...

Research paper thumbnail of The fractional-order dynamics of brainstem vestibulo-oculomotor neurons

Biological Cybernetics, Feb 1, 1994

The vestibulo-ocular reflex (VOR) and other oculomotor subsystems such as pursuit and saccades ar... more 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 fractionalorder differentiation (dkx/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.

Research paper thumbnail of Learning in the Vestibular System: Simulations of Vestibular Compensation Using Recurrent Back-Propagation

Research paper thumbnail of Computer Modeling in Neuroscience: From Imperative to Declarative Programming

Lecture Notes in Computer Science, 2015

Research paper thumbnail of A Recurrent Neural Network Model of Velocity Storage in the Vestibulo-Ocular Reflex

Research paper thumbnail of Method for determination of spatial target probability using a model of multisensory processing by the brain

Research paper thumbnail of Neutral network models of velocity storage in the horizontal vestibulo-ocular reflex

Biological Cybernetics, 1991

Research paper thumbnail of Instabilities in Eye Movement Control: A Model of Periodic Alternating Nystagmus

Neural Information Processing Systems, 1997

Nystagmus is a pattern of eye movement characterized by smooth rotations of the eye in one direct... more Nystagmus is a pattern of eye movement characterized by smooth rotations of the eye in one direction and rapid rotations in the opposite direction that reset eye position. Periodic alternating nystagmus (PAN) is a form of uncontrollable nystagmus that has been described as an unstable but amplitude-limited oscillation. PAN has been observed previously only in subjects with vestibulo-cerebellar damage. We describe results in which PAN can be produced in normal subjects by prolonged rotation in darkness. We propose a new model in which the neural circuits that control eye movement are inherently unstable, but this instability is kept in check under normal circumstances by the cerebellum. Circumstances which alter this cerebellar restraint, such as vestibulocerebellar damage or plasticity due to rotation in darkness, can lead to PAN.