The responses of neocortical cells to sensory stimuli are variable and state dependent. It has been hypothesized that intrinsic cortical dynamics play an important role in trial-to-trial variability; the precise nature of this dependence, however, is poorly understood. We show here that in auditory cortex of urethane-anesthetized rats, population responses to click stimuli can be quantitatively predicted on a trial-by-trial basis by a simple dynamical system model estimated from spontaneous activity immediately preceding stimulus presentation. Changes in cortical state correspond consistently to changes in model dynamics, reflecting a nonlinear, self-exciting system in synchronized states and an approximately linear system in desynchronized states. We propose that the complex and state-dependent pattern of trial-to-trial variability can be explained by a simple principle: sensory responses are shaped by the same intrinsic dynamics that govern ongoing spontaneous activity.

Within optogenetics there is a need for compact light sources that are capable of delivering light with excellent spatial, temporal, and spectral resolution to deep brain structures. Here, we demonstrate a custom GaN-based LED probe for such applications and the electrical, optical, and thermal properties are analyzed. The output power density and emission spectrum were found to be suitable for stimulating channelrhodopsin-2, one of the most common light-sensitive proteins currently used in optogenetics. The LED device produced high light intensities, far in excess of those required to stimulate the light-sensitive proteins within the neurons. Thermal performance was also investigated, illustrating that a broad range of operating regimes in pulsed mode are accessible while keeping a minimum increase in temperature for the brain (0.5°C). This type of custom device represents a significant step forward for the optogenetics community, allowing multiple bright excitation sites along the length of a minimally invasive neural probe.

Controlling neural circuits is a powerful approach to uncover a causal link between neural activity and behaviour. Optogenetics has been widely adopted by the neuroscience community as it offers cell-type-specific perturbation with millisecond precision. However, these studies require light delivery in complex patterns with cellular-scale resolution, while covering a large volume of tissue at depth in vivo. Here we describe a novel high-density silicon-based microscale light-emitting diode (μLED) array, consisting of up to ninety-six 25 μm-diameter μLEDs emitting at a wavelength of 450 nm with a peak irradiance of 400 mW/mm². A width of 100 μm, tapering to a 1 μm point, and a 40 μm thickness help minimise tissue damage during insertion. Thermal properties permit a set of optogenetic operating regimes, with ~0.5 °C average temperature increase. We demonstrate depth-dependent activation of mouse neocortical neurons in vivo, offering an inexpensive novel tool for the precise manipulation of neural activity.

Significance: Neurons in the cerebral cortex emit action potentials in a seemingly random manner. One puzzling aspect of this neuronal “noise” is that it is correlated among neighboring neurons, something thought to reflect the tendency of neurons to fire together. Here, we recorded the activity from populations of cortical neurons in rats and found that correlations could be largely explained by the tendency of cortical neurons to stop firing together. A computational network model whose activity alternated between periods of activity and silence was able to reproduce the pattern of correlations found in the experiments. Our findings shed light on the mechanisms causing neuronal variability and may contribute to elucidate its role in a neural code.

The phase of low-frequency network activity in the auditory cortex captures changes in neural excitability, entrains to the temporal structure of natural sounds, and correlates with the perceptual performance in acoustic tasks. Although these observations suggest a causal link between network rhythms and perception, it remains unknown how precisely they affect the processes by which neural populations encode sounds. We addressed this question by analyzing neural responses in the auditory cortex of anesthetized rats using stimulus–response models. These models included a parametric dependence on the phase of local field potential rhythms in both stimulus-unrelated background activity and the stimulus–response transfer function. We found that phase-dependent models better reproduced the observed responses than static models, during both stimulation with a series of natural sounds and epochs of silence. This was attributable to two factors: (1) phase-dependent variations in background firing (most prominent for delta; 1–4 Hz); and (2) modulations of response gain that rhythmically amplify and attenuate the responses at specific phases of the rhythm (prominent for frequencies between 2 and 12 Hz). These results provide a quantitative characterization of how slow auditory cortical rhythms shape sound encoding and suggest a differential contribution of network activity at different timescales. In addition, they highlight a putative mechanism that may implement the selective amplification of appropriately timed sound tokens relative to the phase of rhythmic auditory cortex activity.