Biomedical subjects

Mark Laubach

Publications and source records attributed to Mark Laubach.

7 recordsLinked to original sources

PubMed2006

Top-down control of motor cortex ensembles by dorsomedial prefrontal cortex.

Dorsomedial prefrontal cortex is critical for the temporal control of behavior. Dorsomedial prefrontal cortex might alter neuronal activity in areas such as motor cortex to inhibit temporally inappropriate responses. We tested this hypothesis by recording from neuronal ensembles in rodent dorsomedial prefrontal cortex during a delayed-response task. One-third of dorsomedial prefrontal neurons were significantly modulated during the delay period. The activity of many of these neurons was predictive of premature responding. We then reversibly inactivated dorsomedial prefrontal cortex while recording ensemble activity in motor cortex. Inactivation of dorsomedial prefrontal cortex reduced delay-related firing, but not response-related firing, in motor cortex. Finally, we made simultaneous recordings in dorsomedial prefrontal cortex and motor cortex and found strong delay-related temporal correlations between neurons in the two cortical areas. These data suggest that functional interactions between dorsomedial prefrontal cortex and motor cortex might serve as a top-down control signal that inhibits inappropriate responding.

Animals↗

PubMed2006

Reversible inactivations of rat medial prefrontal cortex impair the ability to wait for a stimulus.

In simple reaction time tasks, lesions of rat dorsomedial prefrontal cortex impair the ability to wait for trigger stimuli and result in increased premature responding. This effect could be due to impairments in attending to trigger stimuli, estimating the timing of trigger stimuli, or inhibitory control of the motor response. Here, we examined these issues by reversibly inactivating dorsomedial prefrontal cortex during simple reaction time tasks with variable or fixed foreperiods. There were three consistent effects of dorsomedial prefrontal cortex inactivation: 1) increased premature responding, 2) increased variability in the timing of premature responses, and 3) speeded response latencies, especially on trials with short foreperiods in tasks with variable foreperiods. We observed these effects independent of differences in foreperiod duration, foreperiod variability, and stimulus probabilities. Therefore, dorsomedial prefrontal cortex appears not to be involved in attending to the trigger stimulus or in time estimation. Instead, we suggest that dorsomedial prefrontal cortex is critical for inhibiting responses before the maximum foreperiod duration, i.e. the "deadline" [Ollman RT, Billington MJ (1972) The deadline model for simple reaction times. Cognit Psychol 3:311-336], after which the rat should respond even if the trigger stimulus has not occurred.

Animals↗

PubMed2005

Redundancy and synergy of neuronal ensembles in motor cortex.

We examined the ability of neuronal ensembles from rat motor cortex to predict behavioral performance during a reaction time task. We found that neurons that were the best individual predictors of task performance were not necessarily the neurons that contributed the most predictive information to an ensemble of neurons. To understand this result, we applied a framework for quantifying statistical relationships between neurons (Schneidman et al., 2003) to all possible combinations of neurons within our ensembles. We found that almost all neurons (96%) contributed redundant predictive information to the ensembles. This redundancy resulted in the maintenance of predictive information despite the removal of many neurons from each ensemble. Moreover, the balance of synergistic and redundant interactions depended on the number of neurons in the ensemble. Small ensembles could exhibit synergistic interactions (e.g., 23 +/- 9% of ensembles with two neurons were synergistic). In contrast, larger ensembles exhibited mostly redundant interactions (e.g., 99 +/- 0.1% of ensembles with eight neurons were redundant). We discuss these results with regard to constraints on interpreting neuronal ensemble data and with respect to motor cortex involvement in reaction time performance.

Action Potentials↗

PubMed2005

Who's on first? What's on second? The time course of learning in corticostriatal systems.

The prefrontal cortex and basal ganglia are known to be crucial for learning arbitrary sensorimotor associations (e.g. knowing to stop at red traffic lights). However, little is known about the timing of learning-related activity in these brain systems. Conventional wisdom suggests that the prefrontal cortex should drive learning-related changes in the basal ganglia. However, it is possible that the basal ganglia are instead responsible for the development of learning-related activity in prefrontal cortex. Indeed, recent work using methods for recording in the prefrontal cortex and basal ganglia simultaneously suggests that learning-related activity emerges first in the basal ganglia. Here, these studies are reviewed and integrated with the known anatomy of corticostriatal connections. Testable hypotheses regarding corticostriatal interactions during learning are proposed.

Animals↗

PubMed2004

Layer-specific somatosensory cortical activation during active tactile discrimination.

Ensemble neuronal activity was recorded in each layer of the whisker area of the primary somatosensory cortex (SI) while rats performed a whisker-dependent tactile discrimination task. Comparison of this activity with SI activity evoked by similar passive whisker stimulation revealed fundamental differences in tactile signal processing during active and passive stimulation. Moreover, significant layer-specific functional differences in SI activity were observed during active discrimination. These differences could not be explained solely by variations in ascending thalamocortical input to SI. Instead, these results suggest that top-down influences during active discrimination may alter the overall functional nature of SI as well as layer-specific mechanisms of tactile processing.

Afferent Pathways↗

PubMed2004

Multivariate receptive field mapping in marmoset auditory cortex.

We describe a novel method for estimation of multivariate neuronal receptive fields that is based on least-squares (LS) regression. The method is shown to account for the relationship between the spike train of a given neuron, the activity of other neurons that are recorded simultaneously, and a variety of time-varying features of acoustic stimuli, e.g. spectral content, amplitude, and sound source direction. Vocalization-evoked neuronal responses from the marmoset auditory cortex are used to illustrate the method. Optimal predictions of single-unit activity were obtained by using the recent-time history of the target neuron and the concurrent activity of other simultaneously recorded neurons (R: 0.82 +/- 0.01, approximately 67% of variance). Predictions based on ensemble activity alone (R: 0.63 +/- 0.18) were equivalent to those based on the combination of ensemble activity and spectral features of the vocal calls (R: 0.61 +/- 0.24). This result suggests that all information derived from the spectrogram is embodied in ensemble activity and that there is a high level of redundancy in the marmoset auditory cortex. We also illustrate that the method allows for quantification of relative and shared contributions of each variable (spike train, spectral feature) to predictions of neuronal activity and describe a novel "neurolet" transform that arises from the method and that may serve as a tool for computationally efficient processing of natural sounds.

Acoustic Stimulation↗

PubMed2004

Wavelet-based processing of neuronal spike trains prior to discriminant analysis.

Investigations of neural coding in many brain systems have focused on the role of spike rate and timing as two means of encoding information within a spike train. Recently, statistical pattern recognition methods, such as linear discriminant analysis (LDA), have emerged as a standard approach for examining neural codes. These methods work well when data sets are over-determined (i.e., there are more observations than predictor variables). But this is not always the case in many experimental data sets. One way to reduce the number of predictor variables is to preprocess data prior to classification. Here, a wavelet-based method is described for preprocessing spike trains. The method is based on the discriminant pursuit (DP) algorithm of Buckheit and Donoho [Proc. SPIE 2569 (1995) 540-51]. DP extracts a reduced set of features that are well localized in the time and frequency domains and that can be subsequently analyzed with statistical classifiers. DP is illustrated using neuronal spike trains recorded in the motor cortex of an awake, behaving rat [Laubach et al. Nature 405 (2000) 567-71]. In addition, simulated spike trains that differed only in the timing of spikes are used to show that DP outperforms another method for preprocessing spike trains, principal component analysis (PCA) [Richmond and Optican J. Neurophysiol. 57 (1987) 147-61].

Action Potentials↗