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Local field potentials (LFPs) recorded in the barrel cortex in rats and mice are important to investigate somatosensory systems, the final aim being to start to understand mechanisms of brain representation of sensory stimuli in humans. Parameters extracted from LFP of particular interest include spike timing and transmembrane current flow. Recent improvements in microelectrodes technology have enabled neuroscientists to acquire a great amount of LFP signals during the same experimental session, calling for the development of algorithms for their quantitative automatic analysis. In the present work, an algorithm based on Phillips-Tikhonov regularization is presented to automatically detect the main features (in terms of amplitude and latency) of LFP waveforms recorded after whisker stimulation in rat. The accuracy of the algorithm is first assessed in a Monte Carlo simulation mimicking the acquisition of LFP in three different conditions of SNR. Then, the algorithm is tested by analyzing a set of 100 LFP recorded in the primary somatosensory (S1) cortex, i.e., the region involved in the cortical representation of touch in mammals.

The rat barrel cortex is a widely used model of information processing in the somatosensory area, thanks to its precise and easily recognizable organization. However, evoked Local Field Potentials (LFPs) generated in the barrel cortex by repetitive deflections of rat whiskers show large variability in shapes and timings. Moreover, anesthetics can deeply affect the profile of evoked responses. This paper presents preliminary report on the variability and the effect of commonly used anesthetics on these signals. We studied representative signal shape characteristics (e.g., latency and amplitude of events) extracted from evoked responses acquired by means of standard Ag/AgCl electrodes from different cortical layers. As an early result, we found significant difference in the latency of the first principal peak of the responses. Under Tiletamine-Xylazine anesthetic, the responses or events of the evoked LFPs occurred later than the ones recorded while urethane was administered. Furthermore, the distributions of the peak latencies in all cortical layers were narrower in case of Urethane. This behavior should be attributed to the different effects of these two anesthetics on specific synaptic receptors and thus on the processing of neural information and the encoding of sensory input along the cortical pathway.

Motor movement is controlled by the brain through transmitting electrochemical signals to the muscle fibers that cause the contraction of the muscles. A motoneuron carrying the impulse creates a synapse with the muscle fiber which is known as Neuromuscular Junction (NMJ). The muscle infolds taking part in the synapse contains large amount of sodium channels. The current that passes through the narrow synaptic cleft affects the adjacent membranes electrical properties in turn modifies the synaptic transmission process. Considering this phenomenon we have studied the effect of sodium channels at the NMJ to find out its effect in the generation of extracellular potentials at the synaptic cleft of the junction. Through simulation results we confirm that the conductivity of the sodium channels present at the postsynaptic muscle membrane and the junction height affect the generation of the extracellular potentials at the junction which modifies the synaptic properties of the NMJ.

The neuromuscular junction (NMJ) is the place where the axon terminal of motoneuron connects the 'endplate' of a muscle fiber. During this transduction a large depolarization (endplate potential) caused by the nerve impulse opens a large number of voltage-sensitive sodium channels at the post-junctional terminal. As a result, action potentials are generated and propagated along the muscle fiber causing contraction. This work shows simulated results of the voltage-dependent sodium channels' firing behavior at the NMJ using a mathematical model. It is found that the firing behavior of the sodium channels change basing on their activation and inactivation kinetics which are highly influenced by the self-gating behavior of the sodium conductances. The simulation results showed that self-gating of sodium channels increase conduction efficiency at the NMJ and decrease threshold for firing.

The sensory information processing in the rodents is mainly done by whisking, through which they explore the environment, perform object localization, texture and shape discrimination very precisely. During whisking, microcircuits in the corresponding barrel columns get activated to segregate and integrate the tactile information through the information processing pathway. To primarily understand the whisking mechanism angular preferentiality determination is very important. In this work we propose an automated method to determine different events present in the local field potentials (LFPs), calculate latencies and amplitudes related to those events and use them along with the stimulation angle information to determine the angular preferentiality. The method is extensively tested on LFPs recorded from S1 barrel cortex of anesthetized rats using EOSFET (Electrolyte-Oxide-Semiconductor Field Effect Transistor) based neuronal probes.

Whisking is the natural way by which rodents explore the environment. During whisking, microcircuits in the corresponding barrel columns get activated to segregate and integrate the tactile information through the information processing pathway. The local field potentials (LFPs) recorded from the barrel columns provide important information about this pathway. Different layers of the cortex get activated during this information processing, thus having precise information about the order of layer activation is desired. This work proposes an automated, computationally efficient and easy to implement method to determine the cortical layer activation for the signals recorded from barrel cortex of anesthetized rats upon mechanical whisker stimulation.

Advances in neuronal probe technology to record brain activity have posed a significant challenge in performing necessary processing and analysis of the recorded data. To be able to infer meaningful conclusions from the recorded signals through these probes, sophisticated signal processing and analysis tools are required. This paper presents a MATLAB-based novel tool, 'SigMate', capable of performing various processing and analysis incorporating the available standard tools and our in-house custom tools. The present features include, data display (2D and 3D), baseline correction, stimulus artifact removal, noise characterization, file operations (file splitter, file concatenator, and file column rearranger), latency estimation, determination of cortical layer activation order, spike detection, spike sorting, and are gradually growing. This tool has been tested extensively for the recordings using the standard micropipettes as well as implantable neural probes based on EOSFETs (Electrolyte-Oxide-Semiconductor Field Effect Transistors) and will be made available to the community shortly.