Abstract
Spontaneous cortical population activity exhibits a multitude of oscillatory patterns, which often display synchrony during slow-wave sleep or under certain anesthetics and stay asynchronous during quiet wakefulness. The mechanisms behind these cortical states and transitions among them are not completely understood. Here we study spontaneous population activity patterns in random networks of spiking neurons of mixed types modeled by Izhikevich equations. Neurons are coupled by conductance-based synapses subject to synaptic noise. We localize the population activity patterns on the parameter diagram spanned by the relative inhibitory synaptic strength and the magnitude of synaptic noise. In absence of noise, networks display transient activity patterns, either oscillatory or at constant level. The effect of noise is to turn transient patterns into persistent ones: for weak noise, all activity patterns are asynchronous non-oscillatory independently of synaptic strengths; for stronger noise, patterns have oscillatory and synchrony characteristics that depend on the relative inhibitory synaptic strength. In the region of parameter space where inhibitory synaptic strength exceeds the excitatory synaptic strength and for moderate noise magnitudes networks feature intermittent switches between oscillatory and quiescent states with characteristics similar to those of synchronous and asynchronous cortical states, respectively. We explain these oscillatory and quiescent patterns by combining a phenomenological global description of the network state with local descriptions of individual neurons in their partial phase spaces. Our results point to a bridge from events at the molecular scale of synapses to the cellular scale of individual neurons to the collective scale of neuronal populations.
Highlights
Simultaneous recordings from large neuronal populations disclose complex spatio-temporal firing patterns characterized by rhythmic oscillations with variable degrees of synchrony (Buzsaki and Draguhn 2004; Bonifazi et al 2009; Uhlhaas et al 2009; Colgin 2011)
In the context of networks of leaky integrate-and-fire (LIF) neurons, the balance between average excitatory and inhibitory synaptic inputs is known to result in quantitative characteristics of network activity that resemble those of asynchronous cortical states (Brunel 2000; Mattia and Del Giudice 2002; Cessac and Vieville 2008; Vogels and Abbott 2005a; Kumar et al 2008; Wang et al 2011; Litwin-Kumar and Doiron 2012; Kriener et al 2014; Ostojic 2014; Potjans and Diesmann 2014)
To single out the effects caused by the introduction of synaptic noise, we first characterize the system in the nonperturbed state, i.e. in the absence of noise
Summary
Simultaneous recordings from large neuronal populations disclose complex spatio-temporal firing patterns characterized by rhythmic oscillations with variable degrees of synchrony (Buzsaki and Draguhn 2004; Bonifazi et al 2009; Uhlhaas et al 2009; Colgin 2011). There is a widespread assumption that prevalence of synchrony or asynchrony in the network activity depends on the relative strength of excitatory and inhibitory synaptic inputs (van Vreeswijk et al 1996; Amit and Brunel 1997; Renart et al 2010; Landau et al 2016). In the context of networks of leaky integrate-and-fire (LIF) neurons, the balance between average excitatory and inhibitory synaptic inputs is known to result in quantitative characteristics of network activity that resemble those of asynchronous cortical states (Brunel 2000; Mattia and Del Giudice 2002; Cessac and Vieville 2008; Vogels and Abbott 2005a; Kumar et al 2008; Wang et al 2011; Litwin-Kumar and Doiron 2012; Kriener et al 2014; Ostojic 2014; Potjans and Diesmann 2014). In the absence of such balance, the network displays behaviors akin to synchronous cortical states (Vogels et al 2005b)
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