Biomedical subjects

Artur Luczak

Publications and source records attributed to Artur Luczak.

4 recordsLinked to original sources

PubMed2006

Sequential structure of neocortical spontaneous activity in vivo.

Even in the absence of sensory stimulation, the neocortex shows complex spontaneous activity patterns, often consisting of alternating "DOWN" states of generalized neural silence and "UP" states of massive, persistent network activity. To investigate how this spontaneous activity propagates through neuronal assemblies in vivo, we simultaneously recorded populations of 50-200 cortical neurons in layer V of anesthetized and awake rats. Each neuron displayed a virtually unique spike pattern during UP states, with diversity seen amongst both putative pyramidal cells and interneurons, reflecting a complex but stereotypically organized sequential spread of activation through local cortical networks. Spike timing was most precise during the first approximately 100 ms after UP state onset, and decayed as UP states progressed. A subset of UP states propagated as traveling waves, but waves passing a given point in either direction initiated similar local sequences, suggesting local networks as the substrate of sequential firing patterns. A search for repeating motifs indicated that their occurrence and structure was predictable from neurons' individual latencies to UP state onset. We suggest that these stereotyped patterns arise from the interplay of intrinsic cellular conductances and local circuit properties.

Animals↗

PubMed2006

Spatial embedding of neuronal trees modeled by diffusive growth.

The relative importance of the intrinsic and extrinsic factors determining the variety of geometric shapes exhibited by dendritic trees remains unclear. This question was addressed by developing a model of the growth of dendritic trees based on diffusion-limited aggregation process. The model reproduces diverse neuronal shapes (i.e., granule cells, Purkinje cells, the basal and apical dendrites of pyramidal cells, and the axonal trees of interneurons) by changing only the size of the growth area, the time span of pruning, and the spatial concentration of 'neurotrophic particles'. Moreover, the presented model shows how competition between neurons can affect the shape of the dendritic trees. The model reveals that the creation of complex (but reproducible) dendrite-like trees does not require precise guidance or an intrinsic plan of the dendrite geometry. Instead, basic environmental factors and the simple rules of diffusive growth adequately account for the spatial embedding of different types of dendrites observed in the cortex. An example demonstrating the broad applicability of the algorithm to model diverse types of tree structures is also presented.

Animals↗

PubMed2005

Spectral representation--analyzing single-unit activity in extracellularly recorded neuronal data without spike sorting.

One step in the conventional analysis of extracellularly recorded neuronal data is spike sorting, which separates electrical signal into action potentials from different neurons. Because spike sorting involves human judgment, it can be subjective and time intensive, particularly for large sets of neurons. Here we propose a simple, automated way to construct alternative representations of neuronal activity, called spectral representation (SR). In this approach, neuronal spikes are mapped to a discrete space of spike waveform features and time. Spectral representation enables us to find single-unit stimulus-related changes in neuronal activity without spike sorting. We tested the ability of this method to predict stimuli using both simulated data and experimental data from an auditory mapping study in anesthetized marmoset monkeys. We find that our approach produces more accurate classification of stimuli than spike-sorted data for both simulated and experimental conditions. Furthermore, this method lends itself to automated analysis of extracellularly recorded neuronal ensembles. Additionally, we suggest ways in which these representations can be readily extended to assist in spike sorting and the evaluation of single-neuron peri-stimulus time histograms.

Acoustic Stimulation↗

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↗