Biomedical subjects

Douglas A Nitz

Publications and source records attributed to Douglas A Nitz.

6 recordsLinked to original sources

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↗

PubMed2004

Improvements in the signal-to-noise ratio of motor cortex cells distinguish early versus late phases of motor skill learning.

There are numerous experience-driven changes in cortical circuitry that correlate with improved performance. Improved motor performance on a reach-to-grasp task in rodents is associated with changes in long-term potentiation (LTP), synaptogenesis, and movement representations in primary motor cortex (M1) by training days 3, 7, and 10, respectively. We recorded single-cell activity patterns in M1 during reach-to-grasp training to test how neural-spiking properties change with respect to LTP, synaptogenesis, and motor map changes. We also tested how neural-spiking changes relate directly to improved performance by monitoring muscle activity patterns. We found that signal-to-noise ratios (SNRs) of M1 spiking were significantly improved with practice but only after 7-12 d. Three sources of noise were assessed: signal-dependent noise exemplified by the slope of the relationship between mean spike count and count variance per burst, signal-independent noise exemplified by the offset of this relationship, and background firing rates before and after bursts. Signal-independent noise and pre-burst firing rates were reduced with practice. Early performance gains (days 1-6) were dissociated from SNR improvements, whereas later performance gains (day 7-12) were related directly to the magnitude of improvement in both muscle recruitment reliability and success rates. With training, an increased number of cells exhibited firing rates that were correlated with muscle recruitment patterns, with lags suggesting a primary direction of influence from M1 to muscles. These results suggest a functional linkage from local synaptogenesis in M1 to improved spiking reliability of M1 cells to more reliable recruitment of muscles and finally to improved behavioral performance.

Action Potentials↗

PubMed2003

Early skill learning is expressed through selection and tuning of cortically represented muscle synergies.

Skill learning may be based on integrating and adapting movement building blocks organized in the CNS. We examined at what level integration and adaptation occur during early skill learning, the level of individual muscles, muscle synergies or combinations of synergies through time, and whether these operations are expressed through the primary motor cortex (M1). Forelimb muscle and M1 cell activity were recorded over the first day of training on a reach-to-grasp task in rodents. Independent components analysis was used to assess how well muscle activation patterns could be described as time-varying combinations of synergies. In 3 of 11 animals, prereach M1 activity predicted the activation of different combinations of independent components (ICs) to perform the task. With training, animals increasingly adopted postures and prereach patterns of M1 activity that supported activation of the more successful combination. With training, animals also adjusted the activation magnitude (6 of 11 animals) and weights (11 of 11) of specific ICs that constituted the selected combination. Weights represent how IC activation patterns were distributed to forelimb muscles; this distribution pattern was adapted with training. M1 cells (37 of 100) had task-related firing rates that were significantly correlated with IC activation patterns. Changes in M1 firing rates were associated with corresponding changes in either the activation magnitude or weights of the correlated IC. Our data suggest that early skill learning is expressed through selection and tuning of M1 firing rates, which specify time-varying patterns of synergistic muscle contractions in the limb.

Adaptation, Physiological↗

PubMed2003

Use of 'relative-phase' analysis to assess correlation between neuronal spike trains.

We propose a new method of studying the correlation between neuronal spike trains. This technique is based on the analysis of relative phase between two point processes. Relative phase here is defined as the relative timing difference between two spike trains normalized by the associated interspike interval of one cell. This phase measurement is intended to reveal the relative timing relationship between spike trains at different firing rates. We apply this method to a numerical example and an example from two cerebellar neuronal spike trains of a behaving rat. The results are compared with classical cross-correlation analysis. We show that the technique can avoid some of the limitations of cross-correlation methods, reveal certain statistical dependencies that cannot be shown by cross correlation, and provide information as to the direction of influence between two spike trains.

Action Potentials↗

PubMed2002

Electrophysiological correlates of rest and activity in Drosophila melanogaster.

Extended periods of rest in Drosophila melanogaster resemble mammalian sleep states in that they are characterized by heightened arousal thresholds and specific alterations in gene expression. Defined as inactivity periods spanning 5 or more min, amounts of this sleep-like state are, as in mammals, sensitive to prior amounts of waking activity, time of day, and pharmacological intervention. Clearly recognizable changes in the pattern and amount of brain electrical activity accompany changes in motor activity and arousal thresholds originally used to identify mammalian sleeping behavior. Electroencephalograms (EEGs) and/or local field potentials (LFPs) are now widely used to quantify sleep state amounts and define types of sleep. Thus, slow-wave sleep (SWS) is characterized by EEG spindles and large-amplitude delta-frequency (0-3.5 Hz) waves. Rapid-eye movement (REM) sleep is characterized by irregular gamma-frequency cortical EEG patterns and rhythmic theta-frequency (5-9 Hz) hippocampal EEG activity. It is unknown whether rest and activity in Drosophila are associated with distinct electrophysiological correlates. To address this issue, we monitored motor activity levels and recorded LFPs in the medial brain between the mushroom bodies, structures implicated in the modulation of locomotor activity, of Drosophila. The results indicate that LFPs can be reliably recorded from the brains of awake, moving fruit flies, that targeted genetic manipulations can be used to localize sources of LFP activity, and that brain electrical activity of Drosophila is reliably correlated with activity state.

Acclimatization↗