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The experimental study of neural networks requires simultaneous measurements of a massive number of neurons, while monitoring properties of the connectivity, synaptic strengths and delays. Current technological barriers make such a mission unachievable. In addition, as a result of the enormous number of required measurements, the estimated network parameters would differ from the original ones. Here we present a versatile experimental technique, which enables the study of recurrent neural networks activity while being capable of dictating the network connectivity and synaptic strengths. This method is based on the observation that the response of neurons depends solely on their recent stimulations, a short-term memory. It allows a long-term scheme of stimulation and recording of a single neuron, to mimic simultaneous activity measurements of neurons in a recurrent network. Utilization of this technique demonstrates the spontaneous emergence of cooperative synchronous oscillations, in particular the coexistence of fast gamma and slow delta oscillations, and opens the horizon for the experimental study of other cooperative phenomena within large-scale neural networks.

Broadband spontaneous macroscopic neural oscillations are rhythmic cortical firing which was extensively examined during the last century, however, their possible origination is still controversial. In this work we show how macroscopic oscillations emerge in solely excitatory random networks and without topological constraints. We experimentally and theoretically show that these oscillations stem from the counterintuitive underlying mechanism - the intrinsic stochastic neuronal response failures. These neuronal response failures, which are characterized by short-term memory, lead to cooperation among neurons, resulting in sub- or several- Hertz macroscopic oscillations which coexist with high frequency gamma oscillations. A quantitative interplay between the statistical network properties and the emerging oscillations is supported by simulations of large networks based on single-neuron in-vitro experiments and a Langevin equation describing the network dynamics. Results call for the examination of these oscillations in the presence of inhibition and external drives.

Realizations of low firing rates in neural networks usually require globally balanced distributions among excitatory and inhibitory links, while feasibility of temporal coding is limited by neuronal millisecond precision. We show that cooperation, governing global network features, emerges through nodal properties, as opposed to link distributions. Using in vitro and in vivo experiments we demonstrate microsecond precision of neuronal response timings under low stimulation frequencies, whereas moderate frequencies result in a chaotic neuronal phase characterized by degraded precision. Above a critical stimulation frequency, which varies among neurons, response failures were found to emerge stochastically such that the neuron functions as a low pass filter, saturating the average inter-spike-interval. This intrinsic neuronal response impedance mechanism leads to cooperation on a network level, such that firing rates are suppressed towards the lowest neuronal critical frequency simultaneously with neuronal microsecond precision. Our findings open up opportunities of controlling global features of network dynamics through few nodes with extreme properties.

One of the richest and most powerful tools in social communication is the face. Facial expressions are a prominent way to convey high-dimensional, dynamic information, such as emotion, motivation, and intentions. Previous research has linked mimicry of facial expressions to positive human interaction (e.g., mutual agreement). In this study, we investigated in a real-world setting whether the mimicry pattern of multiple affective facial expressions depends on the interpersonal attitudinal agreement between interlocutors. We analyzed video clips of Democratic or Republican American politicians being interviewed by either a political ally or an opponent ( N total = 150 videos). The interviews showed either agreement between two Republicans or two Democrats, or disagreement between members of each affiliation. Using image processing tools, we extracted the intensity of the facial action units for each timepoint. In contrast to the prevalent notion that positive social interaction, such as agreement, fosters mimicry, we found mimicry of all facial expressions in both agreement and disagreement. Moreover, the pattern of the facial expressions mimicry depended on the agreement condition such that an artificial classifier could successfully discriminate between the agreement conditions. Our results suggest that not only positive interpersonal communication is characterized by mimicry but also negative one. This implies that in real-life interactions, mimicry may be a tool to understand others and thus successfully communicate, regardless of the positivity of the social interaction. Whereas the existence of mimicry may be indispensable for social communication, the specific pattern of facial expressions mimicry depends on the social context. (PsycInfo Database Record (c) 2024 APA, all rights reserved).