Information in a spike train is limited by variability in the spike timing. This variability is caused by noise from several sources including synapses and membrane channels; but how deleterious each noise source is and how they affect spike train coding is unknown. Combining physiology and a multicompartment model, we studied the effect of synaptic input noise and voltage-gated channel noise on spike train reliability for a mammalian ganglion cell. For tonic stimuli, the SD of the interspike intervals increased supralinearly with increasing interspike interval. When the cell was driven by current injection, voltage-gated channel noise and background synaptic noise caused fluctuations in the interspike interval of comparable amplitude. Spikes initiated on the dendrites could cause additional spike timing fluctuations. For transient stimuli, synaptic noise was dominant and spontaneous background activity strongly increased fluctuations in spike timing but decreased the latency of the first spike.
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http://dx.doi.org/10.1152/jn.01106.2002 | DOI Listing |
Sci Rep
January 2025
School of Biological Sciences, Georgia Institute of Technology, 315 Ferst Dr NW, Atlanta, 30332-0535, GA, USA.
Nat Commun
January 2025
Neurobiology Department, School of Biological Sciences, University of California, San Diego, CA, USA.
The hippocampal CA3 subregion is a densely connected recurrent circuit that supports memory by generating and storing sequential neuronal activity patterns that reflect recent experience. While theta phase precession is thought to be critical for generating sequential activity during memory encoding, the circuit mechanisms that support this computation across hippocampal subregions are unknown. By analyzing CA3 network activity in the absence of each of its theta-modulated external excitatory inputs, we show necessary and unique contributions of the dentate gyrus (DG) and the medial entorhinal cortex (MEC) to phase precession.
View Article and Find Full Text PDFeNeuro
January 2025
Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel. Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, significantly impacts patient quality of life. Current classification methods focus primarily on clinical observations and electroencephalography (EEG) analysis, often overlooking the underlying dynamics driving seizures. This study uses surface EEG data to identify seizure transitions using a dynamical systems-based framework-the taxonomy of seizure dynamotypes-previously examined only in invasive data.
View Article and Find Full Text PDFBMC Neurol
December 2024
Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, 02115, USA.
Parkinson's disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high energy and have complex architecture. Considering these limitations, a time-varying synaptic efficacy function based leaky-integrate and fire neuron model, called SEFRON is used for the detection of PD.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, 55905, USA.
Alcohol use disorder (AUD) is a chronic relapsing brain disorder characterized by an impaired ability to stop or control alcohol consumption despite adverse social, occupational, or health consequences. AUD affects nearly one-third of adults at some point during their lives, with an associated cost of approximately $249 billion annually in the U.S.
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