Entropy of Neuronal Spike Patterns.

Neuronal spike patterns are the fundamental units of neural communication in the brain, which is still not fully understood. Entropy measures offer a quantitative framework to assess the variability and information content of these spike patterns. By quantifying the uncertainty and informational content of neuronal patterns, entropy measures provide insights into neural coding strategies, synaptic plasticity, network dynamics, and cognitive processes.

Psilocybin reduces functional correlation and the encoding of spatial information by neurons in mouse retrosplenial cortex.

Psychedelic drugs have profound effects on perception, cognition and mood. How psychedelics affect neural signaling to produce these effects remains poorly understood. We investigated the effect of the classic psychedelic psilocybin on neural activity patterns and spatial encoding in the retrosplenial cortex of head-fixed mice navigating on a treadmill.

Associating sensory cues with incoming seizures: developing an animal model of auras.

For patients with epilepsy, one of the biggest problems is the unpredictability of the time when the next seizure will occur. Interestingly, some epileptic patients experience a sensory sensation preceding seizures, called aura, which helps them move to safety before a seizure. Here, we describe the development of the first animal model of auras, which could allow for a more detailed study of this phenomenon.

A bio-inspired reinforcement learning model that accounts for fast adaptation after punishment.

Humans and animals can quickly learn a new strategy when a previously-rewarding strategy is punished. It is difficult to model this with reinforcement learning methods, because they tend to perseverate on previously-learned strategies - a hallmark of impaired response to punishment. Past work has addressed this by augmenting conventional reinforcement learning equations with ad hoc parameters or parallel learning systems.

Correction: Data-driven analyses of motor impairments in animal models of neurological disorders.

[This corrects the article DOI: 10.1371/journal.pbio.

An orexigenic subnetwork within the human hippocampus.

Only recently have more specific circuit-probing techniques become available to inform previous reports implicating the rodent hippocampus in orexigenic appetitive processing. This function has been reported to be mediated at least in part by lateral hypothalamic inputs, including those involving orexigenic lateral hypothalamic neuropeptides, such as melanin-concentrating hormone. This circuit, however, remains elusive in humans.

Biologically-inspired neuronal adaptation improves learning in neural networks.

Since humans still outperform artificial neural networks on many tasks, drawing inspiration from the brain may help to improve current machine learning algorithms. Contrastive Hebbian learning (CHL) and equilibrium propagation (EP) are biologically plausible algorithms that update weights using only local information (without explicitly calculating gradients) and still achieve performance comparable to conventional backpropagation. In this study, we augmented CHL and EP with , inspired by the adaptation effect observed in neurons, in which a neuron's response to a given stimulus is adjusted after a short time.

Combining backpropagation with Equilibrium Propagation to improve an Actor-Critic reinforcement learning framework.

Backpropagation (BP) has been used to train neural networks for many years, allowing them to solve a wide variety of tasks like image classification, speech recognition, and reinforcement learning tasks. But the biological plausibility of BP as a mechanism of neural learning has been questioned. Equilibrium Propagation (EP) has been proposed as a more biologically plausible alternative and achieves comparable accuracy on the CIFAR-10 image classification task.

Neurons learn by predicting future activity.

Understanding how the brain learns may lead to machines with human-like intellectual capacities. It was previously proposed that the brain may operate on the principle of predictive coding. However, it is still not well understood how a predictive system could be implemented in the brain.

Sensory experience selectively reorganizes the late component of evoked responses.

In response to sensory stimulation, the cortex exhibits an early transient response followed by late and slower activation. Recent studies suggest that the early component represents features of the stimulus while the late component is associated with stimulus perception. Although very informative, these studies only focus on the amplitude of the evoked responses to study its relationship with sensory perception.

Epileptic seizures and link to memory processes.

Epileptogenesis is a complex and not well understood phenomenon. Here, we explore the hypothesis that epileptogenesis could be "hijacking" normal memory processes, and how this hypothesis may provide new directions for epilepsy treatment. First, we review similarities between the hypersynchronous circuits observed in epilepsy and memory consolidation processes involved in strengthening neuronal connections.

Predictive Neuronal Adaptation as a Basis for Consciousness.

Being able to correctly predict the future and to adjust own actions accordingly can offer a great survival advantage. In fact, this could be the main reason why brains evolved. Consciousness, the most mysterious feature of brain activity, also seems to be related to predicting the future and detecting surprise: a mismatch between actual and predicted situation.

Spatiotemporal structure of sensory-evoked and spontaneous activity revealed by mesoscale imaging in anesthetized and awake mice.

Stimuli-evoked and spontaneous brain activity propagates across the cortex in diverse spatiotemporal patterns. Despite extensive studies, the relationship between spontaneous and evoked activity is poorly understood. We investigate this relationship by comparing the amplitude, speed, direction, and complexity of propagation trajectories of spontaneous and evoked activity elicited with visual, auditory, and tactile stimuli using mesoscale wide-field imaging in mice.

A Neural Network Reveals Motoric Effects of Maternal Preconception Exposure to Nicotine on Rat Pup Behavior: A New Approach for Movement Disorders Diagnosis.

Neurodevelopmental disorders can stem from pharmacological, genetic, or environmental causes and early diagnosis is often a key to successful treatment. To improve early detection of neurological motor impairments, we developed a deep neural network for data-driven analyses. The network was applied to study the effect of maternal nicotine exposure prior to conception on 10-day-old rat pup motor behavior in an open field task.

Spatiotemporal patterns of neocortical activity around hippocampal sharp-wave ripples.

A prevalent model is that sharp-wave ripples (SWR) arise 'spontaneously' in CA3 and propagate recent memory traces outward to the neocortex to facilitate memory consolidation there. Using voltage and extracellular glutamate transient recording over widespread regions of mice dorsal neocortex in relation to CA1 multiunit activity (MUA) and SWR, we find that the largest SWR-related modulation occurs in retrosplenial cortex; however, contrary to the unidirectional hypothesis, neocortical activation exhibited a continuum of activation timings relative to SWRs, varying from leading to lagging. Thus, contrary to the model in which SWRs arise 'spontaneously' in the hippocampus, neocortical activation often precedes SWRs and may thus constitute a trigger event in which neocortical information seeds associative reactivation of hippocampal 'indices'.

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments.

Closed-loop neurophysiological systems use patterns of neuronal activity to trigger stimuli, which in turn affect brain activity. Such closed-loop systems are already found in clinical applications, and are important tools for basic brain research. A particularly interesting recent development is the integration of closed-loop approaches with optogenetics, such that specific patterns of neuronal activity can trigger optical stimulation of selected neuronal groups.

Data-driven analyses of motor impairments in animal models of neurological disorders.

Behavior provides important insights into neuronal processes. For example, analysis of reaching movements can give a reliable indication of the degree of impairment in neurological disorders such as stroke, Parkinson disease, or Huntington disease. The analysis of such movement abnormalities is notoriously difficult and requires a trained evaluator.

Anodal transcranial direct current stimulation with monopolar pulses improves limb use after stroke by enhancing inter-hemispheric coherence.

Post-stroke neurological deficits, such as sensorimotor impairments, are often permanent and a leading cause of disability. Stroke is also associated with changes in neuronal synchrony among different brain areas. Multiple studies demonstrated that non-invasive brain stimulation, such as transcranial direct current stimulation (tDCS), enhances the efficacy of existing rehabilitative therapies.

Effect of body position on relieve of foreign body from the airway.

Foreign body airway obstruction (FBAO), or commonly known as choking, is an extremely dangerous event. The European Resuscitation Council recommends that back blows and abdominal thrusts should be performed for relieving FBAO in conscious adults. Reviewed here evidence suggests that applying a prone or a head-down position increases effectiveness of the above standard approaches to relieve obstruction, due to help of gravity.

Phase of electroencephalography theta oscillation during stimulus encoding affects accuracy of memory recall.

Oscillatory activity is a ubiquitous property of brain signals, and yet relatively few studies have investigated how the phase of such ongoing oscillations affects our cognition. One of the main findings in this field is that the phase of electroencephalography (EEG) in the alpha band can affect perception of milliseconds-long stimuli. However, the importance of the phase of EEG for processing more naturalistic stimuli, which have a much longer duration, is still not clear.

Involvement of fast-spiking cells in ictal sequences during spontaneous seizures in rats with chronic temporal lobe epilepsy.

See Lenck-Santini (doi:10.1093/awx205) for a scientific commentary on this article. Epileptic seizures represent altered neuronal network dynamics, but the temporal evolution and cellular substrates of the neuronal activity patterns associated with spontaneous seizures are not fully understood.

UP-DOWN cortical dynamics reflect state transitions in a bistable network.

In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. Here we analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. We built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate.

Chronic mild stress exacerbates severity of experimental autoimmune encephalomyelitis in association with altered non-coding RNA and metabolic biomarkers.

The causal factors determining the onset and severity of multiple sclerosis (MS) are not well understood. Here, we investigated the influence of chronic stress on clinical symptoms, metabolic and epigenetic manifestations of experimental autoimmune encephalomyelitis (EAE), a common animal model of MS. Lewis rats were immunized for monophasic EAE with MBP and were exposed to chronic stress for 37days starting 7days prior to immunization.

Learning to Predict Consequences as a Method of Knowledge Transfer in Reinforcement Learning.

The reinforcement learning (RL) paradigm allows agents to solve tasks through trial-and-error learning. To be capable of efficient, long-term learning, RL agents should be able to apply knowledge gained in the past to new tasks they may encounter in the future. The ability to predict actions' consequences may facilitate such knowledge transfer.