Origin of intrinsic irregular firing in cortical interneurons

Abstract

Cortical spike trains are highly irregular both during ongoing, spontaneous activity and when driven at high firing rates. There is uncertainty about the source of this irregularity, ranging from intrinsic noise sources in neurons to collective effects in large-scale cortical networks. Cortical interneurons display highly irregular spike times (coefficient of variation of the interspike intervals >1) in response to dc-current injection in vitro. This is in marked contrast to cortical pyramidal cells, which spike highly irregularly in vivo, but regularly in vitro. We show with in vitro recordings and computational models that this is due to the fast activation kinetics of interneuronal K + currents. This explanation holds over a wide parameter range and with Gaussian white, power-law, and OrnsteinUhlenbeck noise. The intrinsically irregular spiking of interneurons could contribute to the irregularity of the cortical network.inhibitory interneuron | bistability | neural noise | fluctuations | cortex W hen spike trains of cortical pyramidal neurons are recorded in vivo from the cortices of awake or sleeping cats (1-3) or awake monkeys (4), the interspike intervals (ISIs) are highly variable, with coefficients of variation (CV ISI = ISI SD/ISI mean) >1. However, when the main cortical excitatory (pyramidal) neurons are depolarized in vitro by injection of constant current to above firing threshold, their spike trains are substantially more regular (CV ISI <0.5). The irregularity of pyramidal neuron firing in vivo arises from the intense, ongoing, temporally correlated synaptic activity that bombards cortical neurons (1, 2, 5-7). The lower in vitro irregularity can be raised to in vivo levels by using fluctuating current injection (8), hyperosmolar solution (9), and a neuromodulatory mixture (10, 11).Computational models of irregular neuronal firing typically include network synaptic activity represented by stochastic aggregate processes. Fluctuating inputs have been represented by Brownian motion (12) or an Ornstein-Uhlenbeck process (8) in both single-and multicompartmental neuronal models (5). These are externally induced fluctuations and do not mechanistically explain the variety of irregular discharge patterns observed in cortical interneurons. In this study we combined in vitro recordings and computational models of cortical interneurons to show how neuronal conductances interact with the stochastic input to produce the variety of observed neuronal phenotypes. ResultsRecordings from Cortical Inhibitory Interneurons. Cortical GABAergic interneurons have a range of functions including the gating (13) and entrainment (14) of neuronal firing, dendritic integration (15), synaptic plasticity (16), and the generation of network oscillations (10, 11). There are several morphologically and physiologically distinct classes of interneurons (17).In contrast to pyramidal neurons, a constant current injected into mouse visual cortex layer 2/3 interneurons in vitro frequently produced a spike train with a CV ISI that substant...

Characterizing spiking in noisy type II neurons

“…2 on the logplots of ISI distribution. Evidence for the large impact of potassium currents can be found in Stiefel et al (2012) for irregularly spiking cortical interneurons but also in the computational neuronal network study (Ozer et al, 2009) where the topology of the network that produces maximal firing rate depends more significantly on potassium than on sodium channel densities. However, the numerical simulations presented in the Appendix A agree with a common belief that in the large noise regime the contrary is true, i.e.…”

Note on the coefficient of variations of neuronal spike trains

“…The question how to achieve large CV values has been studied in computer simulations (Softky and Koch 1993;Troyer and Miller 1997;Christodoulou and Bugmann 2000;Stiefel et al 2013;Lengler et al 2013;Moreno-Bote 2014), in random walk models (Shadlen and Newsome 1998;Salinas and Sejnowski 2000), in Ornstein-Uhlenback models (Feng and Brown 1999;Fusi and Mattia 1999;Moreno-Bote 2014), and in other models (Terman et al 2013). In contrast to most other approaches, our model allows exact mathematical solutions.…”

“…In the literature, several explanations for large CV values have been brought forward, including closely balanced excitation and inhibition (Shadlen and Newsome 1998;Feng andBrown 1998, 1999;Christodoulou and Bugmann 2000), an excess of inhibition (Fusi and Mattia 1999), correlated input (see Christodoulou and Bugmann 2001 for a brief review), random threshold or reset potentials (Bugmann et al 1997;Gabbiani and Koch 1998;Gutkin and Ermentrout 1998), heterogeneous neurons (Lengler et al 2013), probabilistic synapses (Lengler et al 2013; Moreno-Bote 2014), short afterhyperpolarization time constants (Stiefel et al 2013), or inhibitory coupling (Terman et al 2013). In this paper, we supplement some of the hypotheses (most notably Troyer and Miller 1997;Kara et al 2000; MorenoBote 2014) by exact formulas for the CV value.…”

Functional Diversity of Neurotrophin Actions on the Oculomotor System

“…In primary sensory neurons, NGF acting through p75 NTR [191,194] or TrkA [195,196,197] induces pain sensitization due to changes of expression and kinematics of different receptors and ion channels that contribute to the immediate hyperexcitability of the nociceptor after inflammation (reviewed in [198]). Since irregularity of firing in neurons can arise from the external stimuli the cell receives [199,200,201,202] or from intrinsic changes leading to alterations in the kinetics of ion channels [203,204], further studies are needed to define the mechanism by which NGF alters firing variability of abducens motoneurons.…”