scholarly journals Short Stimulus, Long Response: Sodium and Calcium Dynamics Explain Persistent Neuronal Firing

PLoS Biology ◽  
2015 ◽  
Vol 13 (12) ◽  
pp. e1002320
Author(s):  
Richard Robinson
Keyword(s):  
Neuronal Firing ◽  
Calcium Dynamics ◽  
Short Stimulus

Measurement of spontaneous neuronal membrane activity by time lapse confocal microscopy

1995 ◽  
Vol 53 ◽  
pp. 972-973
Author(s):  
R H. Selinfreund ◽  
A. H. Cornell-Bell
Keyword(s):  
Membrane Potential ◽  
Confocal Microscopy ◽  
Patch Clamp ◽  
Electrical Activity ◽  
Time Lapse ◽  
Neuronal Firing ◽  
Neuronal Membrane ◽  
Pituitary Cells ◽  
Patch Clamp Recording ◽  
Spontaneous Electrical Activity

Cellular electrophysiological properties are normally monitored by standard patch clamp techniques . The combination of membrane potential dyes with time-lapse laser confocal microscopy provides a more direct, least destructive rapid method for monitoring changes in neuronal electrical activity. Using membrane potential dyes we found that spontaneous action potential firing can be detected using time-lapse confocal microscopy. Initially, patch clamp recording techniques were used to verify spontaneous electrical activity in GH4\C1 pituitary cells. It was found that serum depleted cells had reduced spontaneous electrical activity. Brief exposure to the serum derived growth factor, IGF-1, reconstituted electrical activity. We have examined the possibility of developing a rapid fluorescent assay to measure neuronal activity using membrane potential dyes. This neuronal regeneration assay has been adapted to run on a confocal microscope. Quantitative fluorescence is then used to measure a compounds ability to regenerate neuronal firing.The membrane potential dye di-8-ANEPPS was selected for these experiments. Di-8- ANEPPS is internalized slowly, has a high signal to noise ratio (40:1), has a linear fluorescent response to change in voltage.

Extraction of Auditory Information by Modulation of Neuronal Ion Channels

2018 ◽  
pp. 272-300
Author(s):  
Leonard K. Kaczmarek
Keyword(s):  
Ion Channels ◽  
Neuronal Firing ◽  
Auditory Information ◽  
Auditory Neurons ◽  
Complex Sound ◽  
Medial Nucleus ◽  
Rapid Changes ◽  
Genes Encoding ◽  
Kinetics Of ◽  
Trapezoid Body

All neurons express a subset of over seventy genes encoding potassium channel subunits. These channels have been studied in auditory neurons, particularly in the medial nucleus of the trapezoid body. The amplitude and kinetics of various channels in these neurons can be modified by the auditory environment. It has been suggested that such modulation is an adaptation of neuronal firing patterns to specific patterns of auditory inputs. Alternatively, such modulation may allow a group of neurons, all expressing the same set of channels, to represent a variety of responses to the same pattern of incoming stimuli. Such diversity would ensure that a small number of genetically identical neurons could capture and encode many aspects of complex sound, including rapid changes in timing and amplitude. This review covers the modulation of ion channels in the medial nucleus of the trapezoid body and how it may maximize the extraction of auditory information.All neurons express a subset of over seventy genes encoding potassium channel subunits. These channels have been studied in auditory neurons, particularly in the medial nucleus of the trapezoid body. The amplitude and kinetics of various channels in these neurons can be modified by the auditory environment. It has been suggested that such modulation is an adaptation of neuronal firing patterns to specific patterns of auditory inputs. Alternatively, such modulation may allow a group of neurons, all expressing the same set of channels, to represent a variety of responses to the same pattern of incoming stimuli. Such diversity would ensure that a small number of genetically identical neurons could capture and encode many aspects of complex sound, including rapid changes in timing and amplitude. This review covers the modulation of ion channels in the medial nucleus of the trapezoid body and how it may maximize the extraction of auditory information.

scholarly journals A deep convolutional visual encoding model of neuronal responses in the LGN

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eslam Mounier ◽  
Bassem Abdullah ◽  
Hani Mahdi ◽  
Seif Eldawlatly
Keyword(s):  
Firing Rate ◽  
Visual Information ◽  
Visual Stimulation ◽  
Neuronal Population ◽  
Neuronal Firing ◽  
Electrode Arrays ◽  
Deep Convolutional Neural Networks ◽  
Firing Rates ◽  
Spatiotemporal Representation

AbstractThe Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.

Sign in / Sign up

Export Citation Format

Share Document