Probabilistic models for neural populations that naturally capture global coupling and criticality

Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system’s state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality

Investigating the adsorption and transport of water in MFI zeolite pores for water desalination

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 71-74).The permeability of reverse osmosis membranes is limited by the diffusive transport of water across a non-porous polyamide active layer. Alternatively, fabricating a microporous active layer capable of rejecting salt ions while allowing for water transport would increase the permeability while maintaining high salt rejection. Zeolites provide a model porous network which is capable of performing this type of molecular sieve separation. However, a lack of understanding of the mechanisms that govern transport within the zeolite pore network as well as an insufficient control of membrane synthesis has limited the performance of past zeolite-based membranes. In this thesis, we seek to understand the mechanisms of water adsorption and transport in MFI-type zeolite pores through experimentation. Water adsorption on the surface and inside of the pore network was characterized by thermogravimetric analysis for varying Si/Al ratio zeolites. We estimated that the pore volume filled is -71% for a 23 Si/Al ratio MFI zeolite, -25% for an 80 Si/Al ratio MFI zeolite, and 0% for an infinite Si/Al ratio MFI zeolite. In addition, we characterized the transport of water into the MFI zeolite pore network by applying an increasing hydraulic pressure and measuring the change in volumetric displacement. We were able to corroborate the adsorbed pore volume from the TGA experiments and estimated that the pore volume filled was ~72% for a 23 Si/Al ratio MFI zeolite and ~34% for an 80 Si/Al ratio MFI zeolite. We also observed that the volumetric infiltration rate did not have an effect on the infiltration characteristics, which is distinctly different from continuum hydrodynamic behavior. Future work will focus on testing the water permeation and salt rejection of these types of zeolites. We have made significant progress in the fabrication of defect-free zeolite membranes. We also plan on investigating the adsorption and transport of water in MFI zeolite pores by using molecular dynamics simulations.by Thomas Humplik.S.M

Parametric Study on the Effect of Partial Charge on Water Infiltration Behavior in MFI Zeolites

This work analyzes the infiltration behavior of water into sub-nanometer MFI zeolite pores using molecular dynamics simulations. Infiltration simulations are run for a range of partial charge values on the zeolite atoms. Infiltration behavior is compared to partial charges to verify dependence and determine critical charge above which infiltration becomes severely inhibited even at high pressures. Attraction energy is calculated and correlated to the observed infiltration behavior. The critical partial charge of Si~1.8 occurs when the waterzeolite interaction energy becomes stronger than water-water attraction due to which water molecules get stuck and infiltration is significantly reduced. Topics: Wate

Interplay between hydrophilicity and surface barriers on water transport in zeolite membranes

A comprehensive understanding of molecular transport within nanoporous materials remains elusive in a broad variety of engineering and biomedical applications. Here, experiments and atomistic simulations are synergically used to elucidate the non-trivial interplay between nanopore hydrophilicity and surface barriers on the overall water transport through zeolite crystals. At these nanometre-length scales, these results highlight the dominating effect of surface imperfections with reduced permeability on the overall water transport. A simple diffusion resistance model is shown to be sufficient to capture the effects of both intracrystalline and surface diffusion resistances, thus properly linking simulation to experimental evidence. This work suggests that future experimental work should focus on eliminating/overcoming these surface imperfections, which promise an order of magnitude improvement in permeability.MITOR ProjectNANO-BRIDGE (PRIN 2012, grant number 2012LHPSJC)NANOSTEP (Fondazione CRT, Torino) projectsScuola Interpolitecnica di Dottorato—SCUDOISCRA initiative (CINECA award)Center for Clean Water and Clean Energy at MIT and KFUP

Language to Rewards for Robotic Skill Synthesis

Large language models (LLMs) have demonstrated exciting progress in acquiring diverse new capabilities through in-context learning, ranging from logical reasoning to code-writing. Robotics researchers have also explored using LLMs to advance the capabilities of robotic control. However, since low-level robot actions are hardware-dependent and underrepresented in LLM training corpora, existing efforts in applying LLMs to robotics have largely treated LLMs as semantic planners or relied on human-engineered control primitives to interface with the robot. On the other hand, reward functions are shown to be flexible representations that can be optimized for control policies to achieve diverse tasks, while their semantic richness makes them suitable to be specified by LLMs. In this work, we introduce a new paradigm that harnesses this realization by utilizing LLMs to define reward parameters that can be optimized and accomplish variety of robotic tasks. Using reward as the intermediate interface generated by LLMs, we can effectively bridge the gap between high-level language instructions or corrections to low-level robot actions. Meanwhile, combining this with a real-time optimizer, MuJoCo MPC, empowers an interactive behavior creation experience where users can immediately observe the results and provide feedback to the system. To systematically evaluate the performance of our proposed method, we designed a total of 17 tasks for a simulated quadruped robot and a dexterous manipulator robot. We demonstrate that our proposed method reliably tackles 90% of the designed tasks, while a baseline using primitive skills as the interface with Code-as-policies achieves 50% of the tasks. We further validated our method on a real robot arm where complex manipulation skills such as non-prehensile pushing emerge through our interactive system.Comment: https://language-to-reward.github.io

Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning

We investigate whether Deep Reinforcement Learning (Deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies in dynamic environments. We used Deep RL to train a humanoid robot with 20 actuated joints to play a simplified one-versus-one (1v1) soccer game. We first trained individual skills in isolation and then composed those skills end-to-end in a self-play setting. The resulting policy exhibits robust and dynamic movement skills such as rapid fall recovery, walking, turning, kicking and more; and transitions between them in a smooth, stable, and efficient manner - well beyond what is intuitively expected from the robot. The agents also developed a basic strategic understanding of the game, and learned, for instance, to anticipate ball movements and to block opponent shots. The full range of behaviors emerged from a small set of simple rewards. Our agents were trained in simulation and transferred to real robots zero-shot. We found that a combination of sufficiently high-frequency control, targeted dynamics randomization, and perturbations during training in simulation enabled good-quality transfer, despite significant unmodeled effects and variations across robot instances. Although the robots are inherently fragile, minor hardware modifications together with basic regularization of the behavior during training led the robots to learn safe and effective movements while still performing in a dynamic and agile way. Indeed, even though the agents were optimized for scoring, in experiments they walked 156% faster, took 63% less time to get up, and kicked 24% faster than a scripted baseline, while efficiently combining the skills to achieve the longer term objectives. Examples of the emergent behaviors and full 1v1 matches are available on the supplementary website.Comment: Project website: https://sites.google.com/view/op3-socce