Oscillator models-whose steady-state behavior is periodic rather than constant-are fundamental to rhythmic modeling, and they appear in many areas of engineering, physics, chemistry, and biology [1]-[6]. Many oscillators are, by nature, open dynamical systems in that they interact with their environment [7]. Whether functioning as clocks, information transmitters, or rhythm generators, these oscillators have the robust ability to respond to a particular input (entrainment) and to behave collectively in a network (synchronization or clustering).

Significance: Biases and fallacies can nudge humans in one direction or another as they make decisions. During gambling, bias is often generated by internal factors, including individual preferences, past experience, or emotions, and can move a person toward or away from risky behavior. The neural mechanisms responsible for generating internal bias are largely unknown, limiting the treatment of patients with neurological diseases that impair decision-making. We applied mathematical modeling techniques and high-resolution intracerebral recordings to uncover how a hidden internal bias builds up from past experiences to influence decisions and where this internal bias is encoded in the brain. Our findings suggest that biology exploits a distributed lateralized push–pull neural system to govern counterintuitive and highly variable decision-making in humans.

Spontaneous pain, a major complaint of patients with neuropathic pain, has eluded study because there is no reliable marker in either preclinical models or clinical studies. Here, we performed a comprehensive electroencephalogram/electromyogram analysis of sleep in several mouse models of chronic pain: neuropathic (spared nerve injury and chronic constriction injury), inflammatory (Freund’s complete adjuvant and carrageenan, plantar incision) and chemical pain (capsaicin). We find that peripheral axonal injury drives fragmentation of sleep by increasing brief arousals from non–rapid eye movement sleep (NREMS) without changing total sleep amount. In contrast to neuropathic pain, inflammatory or chemical pain did not increase brief arousals. NREMS fragmentation was reduced by the analgesics gabapentin and carbamazepine, and it resolved when pain sensitivity returned to normal in a transient neuropathic pain model (sciatic nerve crush). Genetic silencing of peripheral sensory neurons or ablation of CGRP ⁺ neurons in the parabrachial nucleus prevented sleep fragmentation, whereas pharmacological blockade of skin sensory fibers was ineffective, indicating that the neural activity driving the arousals originates ectopically in primary nociceptor neurons and is relayed through the lateral parabrachial nucleus. These findings identify NREMS fragmentation by brief arousals as an effective proxy to measure spontaneous neuropathic pain in mice.

The paper provides an introductory discussion about two fundamental models of oscillator synchronization: the (continuous-time) diffusive model, that dominates the mathematical literature on synchronization, and the (hybrid) kick model, that accounts for most popular examples of synchronization, but for which only few theoretical results exist. The paper stresses fundamental differences between the two models, such as the different contraction measures underlying the analysis, as well as important analogies that can be drawn in the limit of weak coupling.

Sensorimotor control studies have predominantly focused on how motor regions of the brain relay basic movement-related information such as position and velocity. However, motor control is often complex, involving the integration of sensory information, planning, visuomotor tracking, spatial mapping, retrieval and storage of memories, and may even be emotionally driven. This suggests that many more regions in the brain are involved beyond premotor and motor cortices. In this study, we exploited an experimental setup wherein activity from over 87 non-motor structures of the brain were recorded in eight human subjects executing a center-out motor task. The subjects were implanted with depth electrodes for clinical purposes. Using training data, we constructed subject-specific models that related spectral power of neural activity in six different frequency bands as well as a combined model containing the aggregation of multiple frequency bands to movement speed. We then tested the models by evaluating their ability to decode movement speed from neural activity in the test data set. The best models achieved a correlation of 0.38 ± 0.03 (mean ± standard deviation). Further, the decoded speeds matched the categorical representation of the test trials as correct or incorrect with an accuracy of 70 ± 2.75% across subjects. These models included features from regions such as the right hippocampus, left and right middle temporal gyrus, intraparietal sulcus, and left fusiform gyrus across multiple frequency bands. Perhaps more interestingly, we observed that the non-dominant hemisphere (ipsilateral to dominant hand) was most influential in decoding movement speed.

It is well established that emotions influence our decisions, yet the neural basis of this biasing effect is not well understood. Here we directly recorded local field potentials from the OrbitoFrontal Cortex (OFC) in five human subjects performing a financial decision-making task. We observed a striking increase in gamma-band (36–50 Hz) oscillatory activity that reflected subjects’ decisions to make riskier choices. Additionally, these gamma rhythms were linked back to mismatched expectations or “luck” occurring in past trials. Specifically, when a subject expected to win but lost, the trial was defined as “unlucky” and when the subject expected to lose but won, the trial was defined as “lucky”. Finally, a fading memory model of luck correlated to an objective measure of emotion, heart rate variability. Our findings suggest OFC may play a pivotal role in processing a subject’s internal (emotional) state during financial decision-making, a particularly interesting result in light of the more recent “cognitive map” theory of OFC function.