An Interneuron Circuit Reproducing Essential Spectral Features of Field Potentials

Abstract

Recent advances in engineering and signal processing have renewed the interest in invasive and surface brain recordings, yet many features of cortical field potentials remain incompletely understood.In the present computational study, we show that a model circuit of interneurons, coupled via both GABA A receptor synapses and electrical synapses, reproduces many essential features of the power spectrum of local field potential (LFP) recordings, such as 1/f power scaling at low (< 15 Hz) frequencies, power accumulation in the γ-frequency band (30-100 Hz), and a robust α rhythm in the absence of stimulation. The low-frequency 1/f power scaling depends on strong reciprocal inhibition, whereas the α rhythm is generated by electrical coupling of intrinsically active neurons. As in previous studies, the γ power arises through the amplification of single-neuron spectral properties, owing to the refractory period, by parameters that favour neuronal synchrony, such as delayed inhibition. The present study also confirms that both synaptic and voltage-gated membrane currents substantially contribute to the LFP, and that high-frequency signals such as action potentials quickly taper off with distance. Given the ubiquity of electrically coupled interneuron circuits in the mammalian brain, they may be major determinants of the recorded potentials.

The Impact of Frequency Scale on the Response Sensitivity and Reliability of Cortical Neurons to 1/fβ Input Signals

“…The power spectrum of various natural signals typically exhibits the power law 1/f β in the frequency domain, with β close to one (Voss and Clarke, 1978; Gilden et al, 1995; Musha and Yamamoto, 1997; De Coensel et al, 2003). Moreover, this 1/f property within a specific frequency range is widely observed in neural activities at all levels, as evidenced in recordings of the membrane potential and current (Diba, 2004; Jacobson et al, 2005; Bédard et al, 2006; Yaron-Jakoubovitch, 2008; El Boustani et al, 2009), EEG (Novikov et al, 1997; Bhattacharya and Petsche, 2001; Bédard et al, 2006; Dehghani et al, 2010; Voytek et al, 2015), MEG (Novikov et al, 1997; Dehghani et al, 2010), LFPs (Bédard and Destexhe, 2009; Bedard et al, 2017; Maex, 2018), and fMRI signals (Bullmore et al, 2001; He, 2011; Ciuciu et al, 2014). In practice, the white noise (the power law 1/f 0 ) with generally low cutoff frequencies is widely used to detect neuronal input-and-output functions (Sakai, 1992; Fairhall et al, 2001; Cook et al, 2007; Vilela and Lindner, 2009).…”