A new adaptive transmission protocol is introduced to improve the performance of slotted ALOHA. Nodes use known periodic schedules as base policies with which they collaboratively learn how to transmit periodically in different time slots so that packet collisions are minimized. The Adaptive Policy Tree (APT) algorithm is introduced for this purpose, which results in APT-ALOHA. APT-ALOHA does not require the presence of a central repeater and uses explicit acknowledgements to confirm the reception of packets. It is shown that nodes using APT-ALOHA quickly converge to transmission schedules that are virtually collision-free, and that the throughput of APT-ALOHA resembles that of TDMA, where slots are pre-allocated to nodes. In particular, APT-ALOHA attains a successful utilization of time slots -over 70% on saturation mode.

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Ketamine has shown antidepressant effects in patients with major depressive disorder (MDD) resistant to first‐line treatments and approved for use in this patient population. Ketamine induces several forms of synaptic plasticity, which are proposed to underlie its antidepressant effects. However, the molecular mechanism of action directly responsible for ketamine's antidepressant effects remains under active investigation. It was recently demonstrated that the effectors of the mammalian target of rapamycin complex 1 (mTORC1) signalling pathway, namely, eukaryotic initiation factor 4E (eIF4E) binding proteins 1 and 2 (4E‐BP1 and 4E‐BP2), are central in mediating ketamine‐induced synaptic plasticity and behavioural antidepressant‐like effect. 4E‐BPs are a family of messenger ribonucleic acid (mRNA) translation repressors inactivated by mTORC1. We observed that their expression in inhibitory interneurons mediates ketamine's effects in the forced swim and novelty suppressed feeding tests and the long‐lasting inhibition of GABAergic neurotransmission in the hippocampus. In addition, another effector pathway that regulates translation elongation downstream of mTORC1, the eukaryotic elongation factor 2 kinase (eEF2K), has been implicated in ketamine's behavioural effects. We will discuss how ketamine's rapid antidepressant effect depends on the activation of neuronal mRNA translation through 4E‐BP1/2 and eEF2K. Furthermore, given that these pathways also regulate cognitive functions, we will discuss the evidence of ketamine's effect on cognitive function in MDD. Overall, the data accrued from pre‐clinical research have implicated the mRNA translation pathways in treating mood symptoms of MDD. However, it is yet unclear whether the pro‐cognitive potential of subanesthetic ketamine in rodents also engages these pathways and whether such an effect is consistently observed in the treatment‐resistant MDD population.

Slotted ALOHA is known to have poor channel utilization (a maximum of 37% when average offered load is one packet per time slot). Reinforcement learning has recently been proposed as a technique that allows nodes to learn to coordinate their transmissions in order to attain much higher network utilization. All reinforcementlearning schemes proposed to date assume immediate feedback on the outcome of a packet transmission. We introduce ALOHA-dQT, a reinforcement-learning protocol that achieves high utilization by having nodes broadcast short summaries of the channel history as known to them along with their packets. Our simulation results show that ALOHA-dQT leads to network utilization above 75%, with fair bandwidth allocation among nodes. ALOHA-dQT is the first reinforcement-learning approach applied to slotted ALOHA suitable for ad-hoc networks without centralized repeaters. CCS CONCEPTS• Networks → Network protocols; Network performance modeling; • Theory of computation → Reinforcement learning.

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