For a slowly varying stimulus, the simplest relationship between a neuron's input and output is a rate code, in which the spike rate is a unique function of the stimulus at that instant. In the case of spike-rate adaptation, there is no unique relationship between input and output, because the spike rate at any time depends both on the instantaneous stimulus and on prior spiking (the "history"). To improve the decoding of spike trains produced by neurons that show spike-rate adaptation, we developed a simple scheme that incorporates "history" into a rate code. We utilized this rate-history code successfully to decode spike trains produced by 1) mathematical models of a neuron in which the mechanism for adaptation (IAHP) is specified, and 2) the gastropyloric receptor (GPR2), a stretch-sensitive neuron in the stomatogastric nervous system of the crab Cancer borealis, that exhibits long-lasting adaptation of unknown origin. Moreover, when we modified the spike rate either mathematically in a model system or by applying neuromodulatory agents to the experimental system, we found that changes in the rate-history code could be related to the biophysical mechanisms responsible for altering the spiking.
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http://dx.doi.org/10.1152/jn.00993.2015 | DOI Listing |
bioRxiv
December 2024
Center for Neural Science, New York University, New York, 10003, NY, United States.
Can the transcriptomic profile of a neuron predict its physiological properties? Using a Patch-seq dataset of the primary visual cortex, we addressed this question by focusing on spike rate adaptation (SRA), a well-known phenomenon that depends on small conductance calcium (Ca)-dependent potassium (SK) channels. We first show that in parvalbumin-expressing (PV) and somatostatin-expressing (SST) interneurons (INs), expression levels of genes encoding the ion channels underlying action potential generation are correlated with the half-width (HW) of spikes. Surprisingly, the SK encoding gene is not correlated with the degree of SRA (dAdap).
View Article and Find Full Text PDFCell Rep
November 2024
Allen Institute for Brain Science, Seattle, WA 98109, USA; Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address:
The distinctive physiology of striatal medium spiny neurons (MSNs) underlies their ability to integrate sensory and motor input. In rodents, MSNs have a hyperpolarized resting potential and low input resistance. When activated, they have a delayed onset of spiking and regular spike rate.
View Article and Find Full Text PDFACS Appl Mater Interfaces
October 2024
Department of Semiconductor Engineering, Gyeongsang National University, Jinju, Gyeongnam 52828, Republic of Korea.
iScience
August 2024
Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea.
The rise of neuromorphic systems has addressed the shortcomings of current computing architectures, especially regarding energy efficiency and scalability. These systems use cutting-edge technologies such as Pt/SnO/TiN memristors, which efficiently mimic synaptic behavior and provide potential solutions to modern computing challenges. Moreover, their unipolar resistive switching ability enables precise modulation of the synaptic weights, facilitating energy-efficient parallel processing that is similar to biological synapses.
View Article and Find Full Text PDFHear Res
June 2024
Department of Otorhinolaryngology, Leiden University Medical Center, Albinusdreef 2, Leiden, Netherlands; Leiden Institute for Brain and Cognition, Wassenaarseweg 52, Leiden, Netherlands. Electronic address:
This study introduces and evaluates the PHAST+ model, part of a computational framework designed to simulate the behavior of auditory nerve fibers in response to the electrical stimulation from a cochlear implant. PHAST+ incorporates a highly efficient method for calculating accommodation and adaptation, making it particularly suited for simulations over extended stimulus durations. The proposed method uses a leaky integrator inspired by classic biophysical nerve models.
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