Biomedical subjects

Alexander G Dimitrov

Publications and source records attributed to Alexander G Dimitrov.

4 recordsLinked to original sources

PubMed2006

Effects of stimulus transformations on estimates of sensory neuron selectivity.

Stimulus selectivity of sensory systems is often characterized by analyzing response-conditioned stimulus ensembles. However, in many cases these response-triggered stimulus sets have structure that is more complex than assumed. If not taken into account, when present it will bias the estimates of many simple statistics, and distort the estimated stimulus selectivity of a neural sensory system. We present an approach that mitigates these problems by modeling some of the response-conditioned stimulus structure as being generated by a set of transformations acting on a simple stimulus distribution. This approach corrects the estimates of key statistics and counters biases introduced by the transformations. In cases involving temporal spike jitter or spatial jitter of images, the main observed effects of transformations are blurring of the conditional mean and introduction of artefacts in the spectral decomposition of the conditional covariance matrix. We illustrate this approach by analyzing and correcting a set of model stimuli perturbed by temporal and spatial jitter. We apply the approach to neurophysiological data from the cricket cercal sensory system to correct the effects of temporal jitter.

Action Potentials↗

PubMed2005

Internodal sodium channels ensure active processes under myelin manifesting in depolarizing afterpotentials.

The current opinion about processes in myelinated axon is that action potential saltatorially propagates between nodes of Ranvier and passively charges internodal axolemma thus causing depolarizing afterpotentials (DAP). Demyelination blocks the conduction that gives additional argument in favor of hypothesis that internode is not able to be activated by the existing internodal sodium channels. The results of our modeling study shows that, when periaxonal space is sufficiently narrow, saltatorial action potential is able to activate internodes. Low density of internodal sodium channels is sufficient to generate active internodal waves that slowly propagate from nodes towards corresponding midinternodes where they collide. The periaxonal width that stops internodal wave propagation (about 400 nm) is significantly larger than the highest value of the physiological range for this parameter (30 nm). Internodal activation is directly manifested as transmembrane internodal potential or as a full-sized action potential in periaxonal space where it can hardly be detected, and only as a small deflection in intracellular space. However, changes in the periaxonal potential cause transmyelin currents that lead to significant DAP. The shape and amplitude of DAP depends on myelin parameters and densities of internodal channels. Several technical parameters affect the results of calculations. Internodal spatial segmentation has to be sufficiently fine (at most 20 microm) for the model to be able to simulate internodal activation. We employ 338 internodal segments as compared with up to 21 used in previous models. Ionic accumulation together with related diffusive and electrical processes alter the calculated DAP amplitude. Inclusion of these processes in calculations demands such increase in the total number of segments that the numerical methods used up to now become unapplicable. To overcome the problem, an iterative implicit approach is proposed. It reduces a matrix of general type in multi-cable models to tridiagonal one and accelerates calculations considerably.

Action Potentials↗

PubMed2005

Dejittered spike-conditioned stimulus waveforms yield improved estimates of neuronal feature selectivity and spike-timing precision of sensory interneurons.

What is the meaning associated with a single action potential in a neural spike train? The answer depends on the way the question is formulated. One general approach toward formulating this question involves estimating the average stimulus waveform preceding spikes in a spike train. Many different algorithms have been used to obtain such estimates, ranging from spike-triggered averaging of stimuli to correlation-based extraction of "stimulus-reconstruction" kernels or spatiotemporal receptive fields. We demonstrate that all of these approaches miscalculate the stimulus feature selectivity of a neuron. Their errors arise from the manner in which the stimulus waveforms are aligned to one another during the calculations. Specifically, the waveform segments are locked to the precise time of spike occurrence, ignoring the intrinsic "jitter" in the stimulus-to-spike latency. We present an algorithm that takes this jitter into account. "Dejittered" estimates of the feature selectivity of a neuron are more accurate (i.e., provide a better estimate of the mean waveform eliciting a spike) and more precise (i.e., have smaller variance around that waveform) than estimates obtained using standard techniques. Moreover, this approach yields an explicit measure of spike-timing precision. We applied this technique to study feature selectivity and spike-timing precision in two types of sensory interneurons in the cricket cercal system. The dejittered estimates of the mean stimulus waveforms preceding spikes were up to three times larger than estimates based on the standard techniques used in previous studies and had power that extended into higher-frequency ranges. Spike timing precision was approximately 5 ms.

Action Potentials↗

PubMed2003

Analysis of neural coding through quantization with an information-based distortion measure.

We discuss an analytical approach through which the neural symbols and corresponding stimulus space of a neuron or neural ensemble can be discovered simultaneously and quantitatively, making few assumptions about the nature of the code or relevant features. The basis for this approach is to conceptualize a neural coding scheme as a collection of stimulus-response classes akin to a dictionary or 'codebook', with each class corresponding to a spike pattern 'codeword' and its corresponding stimulus feature in the codebook. The neural codebook is derived by quantizing the neural responses into a small reproduction set, and optimizing the quantization to minimize an information-based distortion function. We apply this approach to the analysis of coding in sensory interneurons of a simple invertebrate sensory system. For a simple sensory characteristic (tuning curve), we demonstrate a case for which the classical definition of tuning does not describe adequately the performance of the cell studied. Considering a more involved sensory operation (sensory discrimination), we also show that, for some cells in this system, a significant amount of information is encoded in patterns of spikes that would not be discovered through analyses based on linear stimulus-response measures.

Air↗