Abstract
Gamma rhythms in many brain regions, including the primary visual cortex (V1), are thought to play a role in information processing. Here, we report a surprising finding of 3 narrowband gamma rhythms in V1 that processed distinct spatial frequency (SF) signals and had different neural origins. The low gamma (LG; 25 to 40 Hz) rhythm was generated at the V1 superficial layer and preferred a higher SF compared with spike activity, whereas both the medium gamma (MG; 40 to 65 Hz), generated at the cortical level, and the high gamma HG; (65 to 85 Hz), originated precortically, preferred lower SF information. Furthermore, compared with the rates of spike activity, the powers of the 3 gammas had better performance in discriminating the edge and surface of simple objects. These findings suggest that gamma rhythms reflect the neural dynamics of neural circuitries that process different SF information in the visual system, which may be crucial for multiplexing SF information and synchronizing different features of an object.
Highlights
The gamma rhythm (30 to 100 Hz) is an oscillatory pattern of neural activity
We assumed that the LFP power spectrum could be modeled by the summation of a baseline and 3 bell-shaped components peaking at different temporal frequency (TF) ranges: low gamma (LG) (25 to 45 Hz), medium gamma (MG) (45 to 65 Hz), and high gamma (HG) (65 to 100 Hz)
We found that LG emerged when the site’s receptive fields (RFs) was on the edge, while MG and HG emerged when the site’s RF was on the surface (Fig 3A, middle and right panels)
Summary
The gamma rhythm (30 to 100 Hz) is an oscillatory pattern of neural activity. It is commonly found in many brain regions [1,2,3,4,5,6] and is thought to play an important role in cognitive functions such as learning [7], memory [8,9], and attention [10,11,12,13]. The cortical surface was considered to be located 0.050 mm above the uppermost channel (Cha2) with significant visually driven spiking responses (signal-to-noise ratio [SNR] > 3 and the 3 consecutive channels below Cha were expected to meet the criterion of SNR > 3) and was assigned a ReD value of 0. We evaluated spike-field coherence (SFC) by calculating the coherency Cxy between different sites (x and y) as the cross-spectra between signals in x and y (Sxy), normalized by the geometric mean of their autospectra (Sxx and Syy) [8] This was estimated using the multitaper method [44] (time-bandwidth product, 3; tapers, 5; Chronux toolbox (http://chronux.org/)) and implemented using custom software written in MATLAB. The sites with a high goodness of fit (larger than 0.8) were analyzed: Goodness of fit 1⁄4 1 À
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