For decades, electrode–tissue interfaces are pursued to establish electrical stimulation as a reliable means to control neuronal cells behavior. However, spreading of electrical currents in tissues limits its spatial precision. Thus, optical cues, such as near‐infrared (NIR) light, are explored as alternatives. Presently, NIR stimulation requires higher energy input than electrical methods despite introduction of light absorbers, e.g., gold nanoparticles. As potential solution, NIR and electrical costimulation are proposed but with limited interfaces capable of sustaining this stimulation technique. Here, a novel electroactive nanocomposite with photoactive properties in the NIR range is constructed by N‐(3‐dimethylaminopropyl)‐N′‐ethylcarbodiimide hydrochloride/N‐hydroxysulfosuccinimide sodium (EDC)/NHS conjugation of liquid crystal graphene oxide (LCGO) to protein‐coated gold nanorods (AuNR). The liquid crystal graphene oxide–gold nanorod nanocomposite (LCGO–AuNR) is fabricated into a hydrophilic electrode‐coating via drop‐casting, making it appropriate for versatile electrode–tissue interface fabrication. UV–vis spectrophotometry results demonstrate that LCGO–AuNR presents an absorbance peak at 798 nm (NIR range). Cyclic voltammetry measurements further confirm its electroactive capacitive properties. Furthermore, LCGO–AuNR coating supports cell adhesion, proliferation, and differentiation of NG108‐15 neuronal cells. This biocompatible interface is anticipated, with ideal electrical and optical properties for NIR and electrical costimulation, to enable further development of the technique for energy‐efficient and precise neuronal cell modulation.

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Amongst the characteristics about information processing in the brain, observations of dynamic near-critical states have generated significant interest. However, theoretical and experimental limitations have precluded a definite answer about when and why neural criticality arises. To explore this topic, we used an in vitro neural network of cortical neurons that was trained to play a simplified game of `Pong'. We demonstrate that critical dynamics emerge when neural networks receive task-related structured sensory input, reorganizing the system to a near-critical state. Additionally, better task performance correlated with proximity to critical dynamics. However, criticality alone is insufficient for a neuronal network to demonstrate learning in the absence of additional information regarding the consequences of previous actions. These findings have compelling implications for the role of neural criticality.

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