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Jeffrey L Krichmar

Publications and source records attributed to Jeffrey L Krichmar.

10 recordsLinked to original sources
PubMed2006

A cerebellar model for predictive motor control tested in a brain-based device.

The cerebellum is known to be critical for accurate adaptive control and motor learning. We propose here a mechanism by which the cerebellum may replace reflex control with predictive control. This mechanism is embedded in a learning rule (the delayed eligibility trace rule) in which synapses onto a Purkinje cell or onto a cell in the deep cerebellar nuclei become eligible for plasticity only after a fixed delay from the onset of suprathreshold presynaptic activity. To investigate the proposal that the cerebellum is a general-purpose predictive controller guided by a delayed eligibility trace rule, a computer model based on the anatomy and dynamics of the cerebellum was constructed. It contained components simulating cerebellar cortex and deep cerebellar nuclei, and it received input from a middle temporal visual area and the inferior olive. The model was incorporated in a real-world brain-based device (BBD) built on a Segway robotic platform that learned to traverse curved paths. The BBD learned which visual motion cues predicted impending collisions and used this experience to avoid path boundaries. During learning, the BBD adapted its velocity and turning rate to successfully traverse various curved paths. By examining neuronal activity and synaptic changes during this behavior, we found that the cerebellar circuit selectively responded to motion cues in specific receptive fields of simulated middle temporal visual areas. The system described here prompts several hypotheses about the relationship between perception and motor control and may be useful in the development of general-purpose motor learning systems for machines.

Cerebellum↗
PubMed2006

Effects of beta-catenin on dendritic morphology and simulated firing patterns in cultured hippocampal neurons.

Beta-catenin is an intracellular signaling molecule that has been shown to be important in activity-dependent dendritic morphogenesis. Here, we investigate the detailed morphological changes elicited in dendritic arbors of cultured hippocampal neurons by overexpression of beta-catenin, and we simulate the electrophysiological consequences of these changes. Compared to control neurons, cells overexpressing beta-catenin have dendritic arbors with significantly greater surface area and more branches, as well as different topological characteristics. To investigate possible effects of beta-catenin expression on the electrophysiological properties of neurons, we converted confocal images of neurons expressing beta-catenin into computational simulator formats using parameters that evenly distributed voltage-dependent channels across the cells' membranes. In simulated current clamp experiments, somata were injected with a normalized current such that the observed electrophysiological differences in the neurons would be due only to morphological differences. We found that the morphology of beta-catenin-expressing neurons contributes to significantly smaller action potential amplitude and greater sensitivity than seen in control neurons. As a consequence, beta-catenin-expressing neurons tended to exhibit higher spike rates and needed less excitation to induce firing. These findings show that beta-catenin, by modifying dendritic arborization, could have profound influences on the electrophysiological behavior of neurons.

Computer Simulation↗
PubMed2005

Characterizing functional hippocampal pathways in a brain-based device as it solves a spatial memory task.

Analyzing neural dynamics underlying complex behavior is a major challenge in systems neurobiology. To meet this challenge through computational neuroscience, we have constructed a brain-based device (Darwin X) that interacts with a real environment, and whose behavior is guided by a simulated nervous system incorporating detailed aspects of the anatomy and physiology of the hippocampus and its surrounding regions. Darwin X integrates cues from its environment to solve a spatial memory task. Place-specific units, similar to place cells in the rodent, emerged by integrating visual and self-movement cues during exploration without prior assumptions in the model about environmental inputs. Because synthetic neural modeling using brain-based devices allows recording from all elements of the simulated nervous system during behavior, we were able to identify different functional hippocampal pathways. We did this by tracing back from reference neuronal units in the CA1 region of the simulated hippocampus to all of the synaptically connected units that were coactive during a particular exploratory behavior. Our analysis identified a number of different functional pathways within the simulated hippocampus that incorporate either the perforant path or the trisynaptic loop. Place fields, which were activated by the trisynaptic circuit, tended to be more selective and informative. However, place units that were activated by the perforant path were prevalent in the model and were crucial for generating appropriate exploratory behavior. Thus, in the model, different functional pathways influence place field activity and, hence, behavior during navigation.

Animals↗
PubMed2005

Spatial navigation and causal analysis in a brain-based device modeling cortical-hippocampal interactions.

We describe Darwin X, a physical device that interacts with a real environment, whose behavior is guided by a simulated nervous system incorporating aspects of the detailed anatomy and physiology of the hippocampus and its surrounding regions. This brain-based device integrates cues from its environment and solves a spatial memory task. The responses of simulated neuronal units in the hippocampal areas during its exploratory behavior are comparable to place cells in the rodent hippocampus and emerged by associating sensory cues during exploration. To identify different functional hippocampal pathways and their influence on behavior, we employed a time series analysis that distinguishes causal interactions within and between simulated hippocampal and neocortical regions while the device is engaged in a spatial memory task. Our analysis identified different functional pathways within the neural simulation and prompts novel predictions about the influence of the perforant path, the trisynaptic loop and hippocampal-cortical interactions on place cell activity and behavior during navigation. Moreover, this causal time series analysis may be useful in analyzing networks in general.

Animals↗
PubMed2005

Oculomotor responses during partial and total sleep deprivation.

INTRODUCTION: Oculomotor responses related to the pupil light reflex (PLR) and saccadic velocity may be sensitive to the effects of sleepiness and therefore could be used to evaluate an individual's fitness for duty. METHODS: There were 12 normal subjects who completed an 8-d study. They were allowed 8 h in bed on the first three nights, 4 h in bed on the fourth night, and then were sleep deprived for the following 64 h. Approximately every 3 h, subjects performed a battery of tests which included a 45-s automated oculomotor test and a 40-min PC-based driving simulator task. Sleepiness was evaluated with a self-assessment instrument. Subjects were allowed 10 h of recovery sleep following sleep deprivation. RESULTS: Oculomotor results for nine subjects showed a significant increase in latency to pupil constriction and a significant decrease in saccadic velocity with total, but not partial, sleep deprivation. The most robust changes during sleep deprivation occurred for saccadic velocity. A night of recovery sleep reversed the effects of total sleep deprivation on latency to pupil constriction and saccadic velocity. Subjective sleepiness and off-road accidents were found to significantly increase over the sleep deprivation period. A significant positive correlation between increasing latency to pupil constriction and increasing sleepiness and driving accidents, and a significant negative correlation between decreasing saccadic velocity and increasing sleepiness and driving accidents during sleep deprivation were found. CONCLUSION: These findings suggest that oculomotor functions, particularly saccadic velocity, are feasible for assessing neurophysiological changes associated with and predictive of sleep deprivation-induced operational performance degradation.

Adult↗
PubMed2004

Visual binding through reentrant connectivity and dynamic synchronization in a brain-based device.

Effective visual object recognition requires mechanisms to bind object features (e.g. color, shape and motion) while distinguishing distinct objects. Synchronously active neuronal circuits among reentrantly connected cortical areas may provide a basis for visual binding. To assess the potential of this mechanism, we have constructed a mobile brain-based device, Darwin VIII, which is guided by simulated analogues of cortical and sub-cortical areas required for visual processing, decision-making, reward and motor responses. These simulated areas are reentrantly connected and each area contains neuronal units representing both the mean activity level and the relative timing of the activity of groups of neurons. Darwin VIII learns to discriminate among multiple objects with shared visual features and associates 'target' objects with innately preferred auditory cues. We observed the co-activation of globally distributed neuronal circuits that corresponded to distinct objects in Darwin VIII's visual field. These circuits, which are constrained by a reentrant neuroanatomy and modulated by behavior and synaptic plasticity, are necessary for successful discrimination. By situating Darwin VIII in a rich real-world environment involving continual changes in the size and location of visual stimuli due to self-generated movement, and by recording its behavioral and neuronal responses in detail, we were able to show that reentrant connectivity and dynamic synchronization provide an effective mechanism for binding the features of visual objects.

Acoustic Stimulation↗
PubMed2002

Effects of dendritic morphology on CA3 pyramidal cell electrophysiology: a simulation study.

We investigated the effect of morphological differences on neuronal firing behavior within the hippocampal CA3 pyramidal cell family by using three-dimensional reconstructions of dendritic morphology in computational simulations of electrophysiology. In this paper, we report for the first time that differences in dendritic structure within the same morphological class can have a dramatic influence on the firing rate and firing mode (spiking versus bursting and type of bursting). Our method consisted of converting morphological measurements from three-dimensional neuroanatomical data of CA3 pyramidal cells into a computational simulator format. In the simulation, active channels were distributed evenly across the cells so that the electrophysiological differences observed in the neurons would only be due to morphological differences. We found that differences in the size of the dendritic tree of CA3 pyramidal cells had a significant qualitative and quantitative effect on the electrophysiological response. Cells with larger dendritic trees: (1) had a lower burst rate, but a higher spike rate within a burst, (2) had higher thresholds for transitions from quiescent to bursting and from bursting to regular spiking and (3) tended to burst with a plateau. Dendritic tree size alone did not account for all the differences in electrophysiological responses. Differences in apical branching, such as the distribution of branch points and terminations per branch order, appear to effect the duration of a burst. These results highlight the importance of considering the contribution of morphology in electrophysiological and simulation studies.

Action Potentials↗
PubMed2002

Machine psychology: autonomous behavior, perceptual categorization and conditioning in a brain-based device.

In studying brain activity during the behavior of living animals, it is not possible simultaneously to analyze all levels of control from molecular events to motor responses. To provide insights into how levels of control interact, we have carried out synthetic neural modeling using a brain-based real-world device. We describe here the design and performance of such a device, designated Darwin VII, which is guided by computer-simulated analogues of cortical and subcortical structures. All levels of Darwin VII's neural architecture can be examined simultaneously as the device behaves in a real environment. Analysis of its neural activity during perceptual categorization and conditioned behavior suggests neural mechanisms for invariant object recognition, experience-dependent perceptual categorization, first-order and second-order conditioning, and the effects of different learning rates on responses to appetitive and aversive events. While individual Darwin VII exemplars developed similar categorical responses that depended on exploration of the environment and sensorimotor adaptation, each showed highly individual patterns of changes in synaptic strengths. By allowing exhaustive analysis and manipulation of neuroanatomy and large-scale neural dynamics, such brain-based devices provide valuable heuristics for understanding cortical interactions. These devices also provide the groundwork for the development of intelligent machines that follow neurobiological rather than computational principles in their construction.

Animals↗
PubMedDate not supplied

Brain-based devices for the study of nervous systems and the development of intelligent machines.

The simultaneous study of brain function at all levels of organization is difficult to undertake with current experimental tools. Present day electrophysiology only allows the recording of at most hundreds of neurons while an animal is performing a behavioral task. Because of this limitation and the sheer complexity of the nervous system, computational modeling has become essential in developing theories of brain function. Accordingly, our group has constructed a series of brain-based devices (BBDs), that is, physical devices with simulated nervous systems that guide behavior, to serve as a heuristic for testing theories of brain function. Unlike animal models, BBDs permit analysis of activity at all levels of the nervous system as the device behaves in its environment. Although the principal focus of developing BBDs has been to test theories of brain function, this type of modeling may also provide a basis for robotic design and practical applications.

Artificial Intelligence↗