Coupled feedback loops maintain synaptic long-term potentiation: A computational model of PKMzeta synthesis and AMPA receptor trafficking

In long-term potentiation (LTP), one of the most studied types of neural plasticity, synaptic strength is persistently increased in response to stimulation. Although a number of different proteins have been implicated in the sub-cellular molecular processes underlying induction and maintenance of LTP, the precise mechanisms remain unknown. A particular challenge is to demonstrate that a proposed molecular mechanism can provide the level of stability needed to maintain memories for months or longer, in spite of the fact that many of the participating molecules have much shorter life spans. Here we present a computational model that combines simulations of several biochemical reactions that have been suggested in the LTP literature and show that the resulting system does exhibit the required stability. At the core of the model are two interlinked feedback loops of molecular reactions, one involving the atypical protein kinase PKM{\zeta} and its messenger RNA, the other involving PKM{\zeta} and GluA2-containing AMPA receptors. We demonstrate that robust bistability - stable equilibria both in the synapse's potentiated and unpotentiated states - can arise from a set of simple molecular reactions. The model is able to account for a wide range of empirical results, including induction and maintenance of late-phase LTP, cellular memory reconsolidation and the effects of different pharmaceutical interventions

Cognitive Models as Simulators: The Case of Moral Decision-Making

To achieve desirable performance, current AI systems often require huge amounts of training data. This is especially problematic in domains where collecting data is both expensive and time-consuming, e.g., where AI systems require having numerous interactions with humans, collecting feedback from them. In this work, we substantiate the idea of $\textit{cognitive models as simulators}$, which is to have AI systems interact with, and collect feedback from, cognitive models instead of humans, thereby making their training process both less costly and faster. Here, we leverage this idea in the context of moral decision-making, by having reinforcement learning (RL) agents learn about fairness through interacting with a cognitive model of the Ultimatum Game (UG), a canonical task in behavioral and brain sciences for studying fairness. Interestingly, these RL agents learn to rationally adapt their behavior depending on the emotional state of their simulated UG responder. Our work suggests that using cognitive models as simulators of humans is an effective approach for training AI systems, presenting an important way for computational cognitive science to make contributions to AI

Na(V)1.5 sodium channel window currents contribute to spontaneous firing in olfactory sensory neurons

Olfactory sensory neurons (OSNs) fire spontaneously as well as in response to odor; both forms of firing are physiologically important. We studied voltage-gated Na+ channels in OSNs to assess their role in spontaneous activity. Whole cell patch-clamp recordings from OSNs demonstrated both tetrodotoxin-sensitive and tetrodotoxin-resistant components of Na+ current. RT-PCR showed mRNAs for five of the nine different Na+ channel α-subunits in olfactory tissue; only one was tetrodotoxin resistant, the so-called cardiac subtype NaV1.5. Immunohistochemical analysis indicated that NaV1.5 is present in the apical knob of OSN dendrites but not in the axon. The NaV1.5 channels in OSNs exhibited two important features: 1) a half-inactivation potential near −100 mV, well below the resting potential, and 2) a window current centered near the resting potential. The negative half-inactivation potential renders most NaV1.5 channels in OSNs inactivated at the resting potential, while the window current indicates that the minor fraction of noninactivated NaV1.5 channels have a small probability of opening spontaneously at the resting potential. When the tetrodotoxin-sensitive Na+ channels were blocked by nanomolar tetrodotoxin at the resting potential, spontaneous firing was suppressed as expected. Furthermore, selectively blocking NaV1.5 channels with Zn2+ in the absence of tetrodotoxin also suppressed spontaneous firing, indicating that NaV1.5 channels are required for spontaneous activity despite resting inactivation. We propose that window currents produced by noninactivated NaV1.5 channels are one source of the generator potentials that trigger spontaneous firing, while the upstroke and propagation of action potentials in OSNs are borne by the tetrodotoxin-sensitive Na+ channel subtypes.This work was aided by support from Boston University, the Rocky Mountain Taste and Smell Center Core for Cellular Visualization and Analysis [National Institute on Deafness and Other Communication Disorders (NIDCD) P30 DC-04657; D. Restrepo, principal investigator], and NIDCD Grants DC-04863 to V. Dionne and DC-006070 to D. Restrepo and T. E. Finger. (Boston University; P30 DC-04657 - Rocky Mountain Taste and Smell Center Core for Cellular Visualization and Analysis [National Institute on Deafness and Other Communication Disorders (NIDCD)]; DC-04863 - Rocky Mountain Taste and Smell Center Core for Cellular Visualization and Analysis [National Institute on Deafness and Other Communication Disorders (NIDCD)]; DC-006070 - Rocky Mountain Taste and Smell Center Core for Cellular Visualization and Analysis [National Institute on Deafness and Other Communication Disorders (NIDCD)])https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4122723/Accepted manuscrip