Biology subjects

Gribkova, E. D.

Publications and source records attributed to Gribkova, E. D..

3 recordsLinked to original sources

bioRxiv2024

Octopus Hypnosis: An Alternative to Global Anesthesia

Effective methods of anesthesia for octopuses are essential to physiological studies as well as for their welfare in scientific research. However, commonly used forms of general anesthesia using ethanol, magnesium chloride, and similar agents have certain drawbacks. While these methods effectively induce still states in the octopus, they also affect the peripheral body and nervous system and are therefore less than optimal for studying local behavior in octopus arms and suckers. Further, stupefying effects outlast the anesthetized state. We explore an old, rarely used method of octopus "hypnosis" in which tonic immobility is induced as a complementary and sometime alternative method to general anesthesia, as well as being particularly suited to studies of local arm-sucker coordination. We modify the procedure for better handling, unimpeded respiration, and isolation of arm peripheral nervous system from the central nervous system (CNS). In the still state, an arm can be neurophysiologically isolated from the CNS by local Mg2+ injection, removing need for isolation by amputation. Exemplary studies of arm-sucker coordination and electrode placements are presented. Additionally, an intriguing phenomenon is observed where the induction of tonic immobility is notably diminished in cases of senescence. This modified procedure offers new convenience and directions for octopus neurobehavioral research.

animal behavior and cognition↗

bioRxiv2024

LoCS-Net: Localizing Convolutional Spiking Neural Network for Fast Visual Place Recognition

Visual place recognition (VPR) is the ability to recognize locations in a physical environment based only on visual inputs. It is a challenging task due to perceptual aliasing, viewpoint and appearance variations and complexity of dynamic scenes. Despite promising demonstrations, many state-of-the-art VPR approaches based on artificial neural networks (ANNs) suffer from computational inefficiency. Spiking neural networks (SNNs), on the other hand, implemented on neuromorphic hardware, are reported to have remarkable potential towards more efficient solutions computationally, compared to ANNs. However, the training of the state-of-the-art (SOTA) SNNs for the VPR task is often intractable on large and diverse datasets. To address this, we develop an end-to-end convolutional SNN model for VPR, that leverages back-propagation for tractable training. Rate-based approximations of leaky integrate-and-fire (LIF) neurons are employed during training to enable back-propagation, and the approximation units are replaced with spiking LIF neurons during inference. The proposed method outperforms the SOTA ANNs and SNNs by achieving 78.2% precision at 100% recall on the challenging Nordland dataset, compared with 53% SOTA performance, and exhibits competitive performance on the Oxford RobotCar dataset while being easier to train and faster in both training and inference when compared to other ANN and SNN-based methods.

neuroscience↗

bioRxiv2018

A novel mutual information estimator to measure spike train correlations in a model thalamocortical network

The impact of thalamic state on information transmission to the cortex remains poorly understood. This limitation exists due to the rich dynamics displayed by thalamocortical networks and because of inadequate tools to characterize those dynamics. Here, we introduce a novel estimator of mutual information and use it to determine the impact of a computational model of thalamic state on information transmission. Using several criteria, this novel estimator, which uses an adaptive partition, is shown to be superior to other mutual information estimators with uniform partitions when used to analyze simulated spike train data with different mean spike rates, as well as electrophysiological data from simultaneously recorded neurons. When applied to a thalamocortical model, the estimator revealed that thalamocortical cell T-type calcium current conductance influences mutual information between the input and output from this network. In particular, a T-type calcium current conductance of about 40 nS appears to produce maximal mutual information between the input to this network (conceptualized as afferent input to the thalamocortical cell) and the output of the network at the level of a layer 4 cortical neuron. Furthermore, at particular combinations of inputs to thalamocortical and thalamic reticular nucleus cells, thalamic cell bursting correlated strongly with recovery of mutual information between thalamic afferents and layer 4 neurons. These studies suggest that the novel mutual information estimator has advantages over previous estimators, and that thalamic reticular nucleus activity can enhance mutual information between thalamic afferents and thalamorecipient cells in the cortex.

neuroscience↗