Though widely hypothesized, limited evidence exists that human brain functions organize in global gradients of abstraction starting from sensory cortical inputs. Hierarchical representation is accepted in computational networks, and tentatively in visual neuroscience, yet no direct holistic demonstrations exist in vivo. Our methods developed network models enriched with tiered directionality, by including input locations, a critical feature for localizing representation in networks generally. Grouped primary sensory cortices defined network inputs, displaying global connectivity to fused inputs. Depth-oriented networks guided analyses of fMRI databases (~17,000 experiments;~1/4 of fMRI literature). Formally, we tested whether network depth predicted localization of abstract versus concrete behaviors over the whole set of studied brain regions. For our results, new cortical graph metrics, termed network-depth, ranked all databased cognitive function activations by network-depth. Thus, we objectively sorted stratified landscapes of cognition, starting from grouped sensory inputs in parallel, progressing deeper into cortex. This exposed escalating amalgamation of function or abstraction with increasing network-depth, globally. Nearly 500 new participants confirmed our results. In conclusion, data-driven analyses defined a hierarchically ordered connectome, revealing a related continuum of cognitive function. Progressive functional abstraction over network depth may be a fundamental feature of brains, and is observed in artificial networks.

Overview: We model energy constraints in a network of spiking neurons, while exploring general questions of resource limitation on network function abstractly.Background: Metabolic states like dietary ketosis or hypoglycemia have a large impact on brain function and disease outcomes. Glia provide metabolic support for neurons, among other functions. Yet, in computational models of glia-neuron cooperation, there have been no previous attempts to explore the effects of direct realistic energy costs on network activity in spiking neurons. Currently, biologically realistic spiking neural networks assume that membrane potential is the main driving factor for neural spiking, and do not take into consideration energetic costs.Methods: We define local energy pools to constrain a neuron model, termed Spiking Neuron Energy Pool (SNEP), which explicitly incorporates energy limitations. Each neuron requires energy to spike, and resources in the pool regenerate over time. Our simulation displays an easy-to-use GUI, which can be run locally in a web browser, and is freely available.Results: Energy dependence drastically changes behavior of these neural networks, causing emergent oscillations similar to those in networks of biological neurons. We analyze the system via Lotka-Volterra equations, producing several observations: (1) energy can drive self-sustained oscillations, (2) the energetic cost of spiking modulates the degree and type of oscillations, (3) harmonics emerge with frequencies determined by energy parameters, and (4) varying energetic costs have non-linear effects on energy consumption and firing rates.Conclusions: Models of neuron function which attempt biological realism may benefit from including energy constraints. Further, we assert that observed oscillatory effects of energy limitations exist in networks of many kinds, and that these findings generalize to abstract graphs and technological applications.

Free-standing iodine-doped composite samples of poly[2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylenevinylene] (MEH-PPV) with carbon nanotubes (NTs) showed thermoelectric (TE) power factors (PFs) up to 33 μW·m·K after optimizing multiple factors, including: (1) sample fabrication solvent, (2) doping time, (3) average MEH-PPV molecular weight, (4) NT fraction in the composite, and (5) use of single-wall versus multi-wall nanotubes (SWNT and MWNT, respectively). Composite fabrication from halogenated solvents gave the best TE performance after iodine doping times of 2-4 h; performance drops substantially in ∼20 h doped samples. TE performance dropped after at least 24 h of removal from iodine vapor but was fully restored upon re-exposure to the dopant. Longer-chain MEH-PPV gave not only mechanically stronger films but also higher PFs in doped SWNT composites. MWNT composites gave low PFs, attributed to poor NT dispersion. Scanning electron microscopy showed increasingly extensive network formation as NT fraction increased in the composites; this phase separation provides charge transport pathways that improve thermoelectric PFs. The results support a strategy of producing phase-separated materials having both electrical conduction enhanced regions and Seebeck thermopower retaining regions to maximize organic TE response.

Objective:We developed an extensively general closed-loop system to improve human interaction in various multitasking scenarios, with semi-autonomous agents, processes, and robots.Background: Much technology is converging toward semi-independent processes with intermittent human supervision distributed over multiple computerized agents. Human operators multitask notoriously poorly, in part due to cognitive load and limited working memory. To multitask optimally, users must remember task order, e.g., the most neglected task, since longer times not monitoring an element indicates greater probability of need for user input. The secondary task of monitoring attention history over multiple spatial tasks requires similar cognitive resources as primary tasks themselves. Humans can not reliably make more than ∼ 2 decisions/s. Methods: Participants managed a range of 4-10 semi-autonomous agents performing rescue tasks. To optimize monitoring and controlling multiple agents, we created an automated short-term memory aid, providing visual cues from users' gaze history. Cues indicated when and where to look next, and were derived from an inverse of eye fixation recency.Results: Contingent eye tracking algorithms drastically improved operator performance, increasing multitasking capacity. The gaze aid reduced biases, and reduced cognitive load, measured by smaller pupil dilation. Conclusion:Our eye aid likely helped by delegating short-term memory to the computer, and by reducing decision-making load. Past studies used eye position for gaze-aware control and interactive updating of displays in application-specific scenarios, but ours is the first to successfully implement domain-general algorithms. Procedures should generalize well to process control, factory operations, robot control, surveillance, aviation, air traffic control, driving, military, mobile search and rescue, and many tasks where probability of utility is predicted by duration since last attention to a task.