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Neuromorphic photonics is a promising research field due to its potential to tackle the limitations arising from the bottleneck of the von-Neumann computation architecture. Inspired by the characteristics and behavior of the biological brain, photonic neural networks are touted as a solution for solving complex problems that require GHz operation at low latency and low power consumption. An essential building block of such a neural network is a low-complexity multiply-accumulate operation, for which efficient functional implementations in the optical domain are sought for. Towards this direction, we present a synaptic receptor that functionally integrates weighting and signal detection. This optical multiply-accumulate operation is accomplished through a monolithic integrated semiconductor optical amplifier and reflective electro-absorption modulator, which together serve as a colorless frequency demodulator and detector of frequency-coded signals. Moreover, we show that two spike trains can be simultaneously processed with alternating signs and detected as a weighted sum. The performance of the proposed synaptic receptors is further validated through a low bit error ratio for signal rates of up to 10 Gb/s.

Learning to recognize and respond to potential threats is crucial for survival. Pavlovian threat conditioning represents a key paradigm for investigating the neurobiological mechanisms of fear learning. In this review, we address the role of specific neuropharmacological adjuvants that act on neurochemical synaptic transmission, as well as on brain plasticity processes implicated in fear memory. We focus on novel neuropharmacological manipulations targeting glutamatergic, noradrenergic, and endocannabinoid systems, and address how the modulation of these neurobiological systems affects fear extinction learning in humans. We show that the administration of N-methyl-D-aspartate (NMDA) agonists and modulation of the endocannabinoid system by fatty acid amide hydrolase (FAAH) inhibition can boost extinction learning through the stabilization and regulation of the receptor concentration. On the other hand, elevated noradrenaline levels dynamically modulate fear learning, hindering long-term extinction processes. These pharmacological interventions could provide novel targeted treatments and prevention strategies for fear-based and anxiety-related disorders.

GABAA receptors are ligand-gated chloride channels and ionotropic receptors of GABA, the main inhibitory neurotransmitter in vertebrates. In this review, we discuss the major and diverse roles GABAA receptors play in the regulation of neuronal communication and the functioning of the brain. GABAA receptors have complex electrophysiological properties that enable them to mediate different types of currents such as phasic and tonic inhibitory currents. Their activity is finely regulated by membrane voltage, phosphorylation and several ions. GABAA receptors are pentameric and are assembled from a diverse set of subunits. They are subdivided into numerous subtypes, which differ widely in expression patterns, distribution and electrical activity. Substantial variations in macroscopic neural behavior can emerge from minor differences in structure and molecular activity between subtypes. Therefore, the diversity of GABAA receptors widens the neuronal repertoire of responses to external signals and contributes to shaping the electrical activity of neurons and other cell types.

Synaptic neurotransmission is generally considered as a function of membrane-embedded receptors and ion channels in response to the neurotransmitter (NT) release and binding. This perspective aims to widen the protein-centric view by including another vital component—the synaptic membrane—in the discussion. A vast set of atomistic molecular dynamics simulations and biophysical experiments indicate that NTs are divided into membrane-binding and membrane-nonbinding categories. The binary choice takes place at the water-membrane interface and follows closely the positioning of the receptors’ binding sites in relation to the membrane. Accordingly, when a lipophilic NT is on route to a membrane-buried binding site, it adheres on the membrane and, then, travels along its plane towards the receptor. In contrast, lipophobic NTs, which are destined to bind into receptors with extracellular binding sites, prefer the water phase. This membrane-based sorting splits the neurotransmission into membrane-independent and membrane-dependent mechanisms and should make the NT binding into the receptors more efficient than random diffusion would allow. The potential implications and notable exceptions to the mechanisms are discussed here. Importantly, maintaining specific membrane lipid compositions (MLCs) at the synapses, especially regarding anionic lipids, affect the level of NT-membrane association. These effects provide a plausible link between the MLC imbalances and neurological diseases such as depression or Parkinson’s disease. Moreover, the membrane plays a vital role in other phases of the NT life cycle, including storage and release from the synaptic vesicles, transport from the synaptic cleft, as well as their synthesis and degradation.

International audience; In systems biology, systems of kinetic reactions are generally used to model and simulate various biochemical pathways. These reactions are translated into ordinary differential equations, which are computationally resolved by numerical algorithms. Computation performance, defined by how fast the algorithm converges to a numerical solution of the system of ordinary differential equations, critically depends on the choice of the appropriate algorithm. In this paper, we compared several algorithms used to solve ordinary differential equations applied to several kinetic models that describe the dynamic behavior of receptors and ion channels found in chemical synapses of the Central Nervous System; we provide a simplified method to determine the performances of these ordinary differential equation solvers, in order to provide a benchmark for algorithm selection. This method will facilitate the choice of the most efficient algorithm for a given kinetic model with a minimum number of tests. Our results provide a tool for identifying optimal solvers for any biological bilinear kinetic models under various experimental conditions. This comparison also underscored the complexity of biological kinetic models and illustrates how their input dependency could interfere with performance. Despite these challenges, our simplified method helps to select the best solvers for any synaptic receptors kinetic models described, with a bilinear system with minimal a priori information on the solver structure and the model.