Ih-mediated depolarization enhances the temporal precision of neuronal integration

Feed-forward inhibition mediated by ionotropic GABAA receptors contributes to the temporal precision of neuronal signal integration. These receptors exert their inhibitory effect by shunting excitatory currents and by hyperpolarizing neurons. The relative roles of these mechanisms in neuronal computations are, however, incompletely understood. In this study, we show that by depolarizing the resting membrane potential relative to the reversal potential for GABAA receptors, the hyperpolarization-activated mixed cation current (Ih) maintains a voltage gradient for fast synaptic inhibition in hippocampal pyramidal cells. Pharmacological or genetic ablation of Ih broadens the depolarizing phase of afferent synaptic waveforms by hyperpolarizing the resting membrane potential. This increases the integration time window for action potential generation. These results indicate that the hyperpolarizing component of GABAA receptor-mediated inhibition has an important role in maintaining the temporal fidelity of coincidence detection and suggest a previously unrecognized mechanism by which Ih modulates information processing in the hippocampus

A biophysical model of endocannabinoid-mediated short term depression in hippocampal inhibition

Memories are believed to be represented in the synaptic pathways of vastly interconnected networks of neurons. The plasticity of synapses, that is, their strengthening and weakening depending on neuronal activity, is believed to be the basis of learning and establishing memories. An increasing number of studies indicate that endocannabinoids have a widespread action on brain function through modulation of synap–tic transmission and plasticity. Recent experimental studies have characterised the role of endocannabinoids in mediating both short- and long-term synaptic plasticity in various brain regions including the hippocampus, a brain region strongly associated with cognitive functions, such as learning and memory. Here, we present a biophysically plausible model of cannabinoid retrograde signalling at the synaptic level and investigate how this signalling mediates depolarisation induced suppression of inhibition (DSI), a prominent form of shortterm synaptic depression in inhibitory transmission in hippocampus. The model successfully captures many of the key characteristics of DSI in the hippocampus, as observed experimentally, with a minimal yet sufficient mathematical description of the major signalling molecules and cascades involved. More specifically, this model serves as a framework to test hypotheses on the factors determining the variability of DSI and investigate under which conditions it can be evoked. The model reveals the frequency and duration bands in which the post-synaptic cell can be sufficiently stimulated to elicit DSI. Moreover, the model provides key insights on how the state of the inhibitory cell modulates DSI according to its firing rate and relative timing to the post-synaptic activation. Thus, it provides concrete suggestions to further investigate experimentally how DSI modulates and is modulated by neuronal activity in the brain. Importantly, this model serves as a stepping stone for future deciphering of the role of endocannabinoids in synaptic transmission as a feedback mechanism both at synaptic and network level

Disinhibition Mediates a Form of Hippocampal Long-Term Potentiation in Area CA1

The hippocampus plays a central role in memory formation in the mammalian brain. Its ability to encode information is thought to depend on the plasticity of synaptic connections between neurons. In the pyramidal neurons constituting the primary hippocampal output to the cortex, located in area CA1, firing of presynaptic CA3 pyramidal neurons produces monosynaptic excitatory postsynaptic potentials (EPSPs) followed rapidly by feedforward (disynaptic) inhibitory postsynaptic potentials (IPSPs). Long-term potentiation (LTP) of the monosynaptic glutamatergic inputs has become the leading model of synaptic plasticity, in part due to its dependence on NMDA receptors (NMDARs), required for spatial and temporal learning in intact animals. Using whole-cell recording in hippocampal slices from adult rats, we find that the efficacy of synaptic transmission from CA3 to CA1 can be enhanced without the induction of classic LTP at the glutamatergic inputs. Taking care not to directly stimulate inhibitory fibers, we show that the induction of GABAergic plasticity at feedforward inhibitory inputs results in the reduced shunting of excitatory currents, producing a long-term increase in the amplitude of Schaffer collateral-mediated postsynaptic potentials. Like classic LTP, disinhibition-mediated LTP requires NMDAR activation, suggesting a role in types of learning and memory attributed primarily to the former and raising the possibility of a previously unrecognized target for therapeutic intervention in disorders linked to memory deficits, as well as a potentially overlooked site of LTP expression in other areas of the brain

A neuroscientist's guide to lipidomics

Nerve cells mould the lipid fabric of their membranes to ease vesicle fusion, regulate ion fluxes and create specialized microenvironments that contribute to cellular communication. The chemical diversity of membrane lipids controls protein traffic, facilitates recognition between cells and leads to the production of hundreds of molecules that carry information both within and across cells. With so many roles, it is no wonder that lipids make up half of the human brain in dry weight. The objective of neural lipidomics is to understand how these molecules work together; this difficult task will greatly benefit from technical advances that might enable the testing of emerging hypotheses

Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

A negotiation between agents is typically an incomplete information game, where the agents initially do not know their opponent’s preferences or strategy. This poses a challenge, as efficient and effective negotiation requires the bidding agent to take the other’s wishes and future behavior into account when deciding on a proposal. Therefore, in order to reach better and earlier agreements, an agent can apply learning techniques to construct a model of the opponent. There is a mature body of research in negotiation that focuses on modeling the opponent, but there exists no recent survey of commonly used opponent modeling techniques. This work aims to advance and integrate knowledge of the field by providing a comprehensive survey of currently existing opponent models in a bilateral negotiation setting. We discuss all possible ways opponent modeling has been used to benefit agents so far, and we introduce a taxonomy of currently existing opponent models based on their underlying learning techniques. We also present techniques to measure the success of opponent models and provide guidelines for deciding on the appropriate performance measures for every opponent model type in our taxonomy

Finding optimal feasible global plans for multiple teams of heterogeneous robots using hybrid reasoning: an application to cognitive factories

We consider cognitive factories with multiple teams of heterogenous robots, and address two key challenges of these domains, hybrid reasoning for each team and finding an optimal global plan (with minimum makespan) for multiple teams. For hybrid reasoning, we propose modeling each team’s workspace taking into account capabilities of heterogeneous robots, embedding continuous external computations into discrete symbolic representation and reasoning, not only optimizing the makespans of local plans but also minimizing the total cost of robotic actions. To find an optimal global plan, we propose a semi-distributed approach that does not require exchange of information between teams but yet achieves on an optimal coordination of teams that can help each other. We prove that the optimal coordination problem is NP-complete, and describe a solution using automated reasoners. We experimentally evaluate our methods, and show their applications on a cognitive factory with dynamic simulations and a physical implementation