Multiple Time Scales of Temporal Response in Pyramidal and Fast Spiking Cortical Neurons
Overview
Physiology
Affiliations
Neural dynamic processes correlated over several time scales are found in vivo, in stimulus-evoked as well as spontaneous activity, and are thought to affect the way sensory stimulation is processed. Despite their potential computational consequences, a systematic description of the presence of multiple time scales in single cortical neurons is lacking. In this study, we injected fast spiking and pyramidal (PYR) neurons in vitro with long-lasting episodes of step-like and noisy, in-vivo-like current. Several processes shaped the time course of the instantaneous spike frequency, which could be reduced to a small number (1-4) of phenomenological mechanisms, either reducing (adapting) or increasing (facilitating) the neuron's firing rate over time. The different adaptation/facilitation processes cover a wide range of time scales, ranging from initial adaptation (<10 ms, PYR neurons only), to fast adaptation (<300 ms), early facilitation (0.5-1 s, PYR only), and slow (or late) adaptation (order of seconds). These processes are characterized by broad distributions of their magnitudes and time constants across cells, showing that multiple time scales are at play in cortical neurons, even in response to stationary stimuli and in the presence of input fluctuations. These processes might be part of a cascade of processes responsible for the power-law behavior of adaptation observed in several preparations, and may have far-reaching computational consequences that have been recently described.
A neuronal least-action principle for real-time learning in cortical circuits.
Senn W, Dold D, Kungl A, Ellenberger B, Jordan J, Bengio Y Elife. 2024; 12.
PMID: 39704647 PMC: 11661794. DOI: 10.7554/eLife.89674.
Iigaya K, Larsen T, Fong T, ODoherty J J Neurosci. 2024; 45(1).
PMID: 39557579 PMC: 11694394. DOI: 10.1523/JNEUROSCI.0080-24.2024.
Unsupervised learning of perceptual feature combinations.
Tamosiunaite M, Tetzlaff C, Worgotter F PLoS Comput Biol. 2024; 20(3):e1011926.
PMID: 38442095 PMC: 10942261. DOI: 10.1371/journal.pcbi.1011926.
Kern F, Chao Z PLoS Comput Biol. 2023; 19(10):e1011554.
PMID: 37831721 PMC: 10599548. DOI: 10.1371/journal.pcbi.1011554.
Mechanisms of human dynamic object recognition revealed by sequential deep neural networks.
Sorensen L, Bohte S, de Jong D, Slagter H, Scholte H PLoS Comput Biol. 2023; 19(6):e1011169.
PMID: 37294830 PMC: 10306191. DOI: 10.1371/journal.pcbi.1011169.