Local field potentials indicate network state and account for neuronal response variability

Abstract

Multineuronal recordings have revealed that neurons in primary visual cortex (V1) exhibit coordinated fluctuations of spiking activity in the absence and in the presence of visual stimulation. From the perspective of understanding a single cell’s spiking activity relative to a behavior or stimulus, these network flutuations are typically considered to be noise. We show that these events are highly correlated with another commonly recorded signal, the local field potential (LFP), and are also likely related to global network state phenomena which have been observed in a number of neural systems. Moreover, we show that attributing a component of cell firing to these network fluctuations via explicit modeling of the LFP improves the recovery of cell properties. This suggests that the impact of network fluctuations may be estimated using the LFP, and that a portion of this network activity is unrelated to the stimulus and instead reflects ongoing cortical activity. Thus, the LFP acts as an easily accessible bridge between the network state and the spiking activity.

Natural Movies Evoke Spike Trains with Low Spike Time Variability in Cat Primary Visual Cortex

“…Striate neurons live within a heavily influential network (Arieli et al, 1996;Tsodyks et al, 1999), in which the overall "state" augments the receptive field properties (Wörgötter et al, 1998;Wörgötter and Eysel, 2000), and are not only influenced by anesthesia type and depth (Villeneuve and Casanova, 2003), but can also be modulated directly through cholinergic inputs (Herculano-Houzel et al, 1999;Rodriguez et al, 2004). These factors likely influence spiking behavior Gray, 1999, 2000) and should be incorporated into more sophisticated models (Kelly et al, 2010) Figure 14. Illustration of the difficulties encountered in trying to identity events.…”

Stimulus-dependent spiking relationships with the EEG

“…Spontaneous activity exemplifies one such state, in which each neuron tends to be highly correlated with its neighbors and the population as a whole is strongly modulated by global network fluctuations. In contrast, visual stimulation transitions the population into a state in which neurons operate more independently (Kelly et al 2010;Smith and Kohn 2008;Smith and Sommer 2013). Subtler computational states exist between these quite distinct modes of neural activity, and modulation in the degree of coordination between individual neurons and their surrounding populations has been reported in a variety of contexts, such as attention (Cohen and Maunsell 2009;Mitchell et al 2009), learning (Gu et al 2011;Jeanne et al 2013), and contextual modulation (Snyder et al 2014).…”

- Spike Trains as Event Sequences: Fundamental Implications

“…Precisely-timed synchrony is usually identified with a narrow peak in the CCH. Figure 2 shows a CCH taken from a pair of neurons in primary visual cortex [46]. Its appearance, with a peak near 0, is typical of many CCHs that are used to demonstrate synchronous firing.…”

“…In addition to spike history effects, generalized regression models may incorporate additional covariates that represent slow fluctuations in firing rate that occur across trials [73]. Furthermore, measured network effects related to neural inputs can be included in the generalized regression model and they, too, can be effectively removed from the analysis [46,43,42]. If, for example, each of two neurons were to have highly rhythmic spiking in conjunction with an oscillatory potential produced by some large network of cells, then the two neurons would both tend to spike near the times of the peaks of the oscillatory potential.…”

“…Here, observed synchrony is defined using time bins having 5 ms width. Spiking activity was recorded for 1 second from primary visual cortex in response to a drifting sinusoidal grating, as reported in [43] and [46].…”