Neural complexity: a graph theoretic interpretation

One of the central challenges facing modern neuroscience is to explain the ability of the nervous system to coherently integrate information across distinct functional modules in the absence of a central executive. To this end Tononi et al. [Proc. Nat. Acad. Sci. USA 91, 5033 (1994)] proposed a measure of neural complexity that purports to capture this property based on mutual information between complementary subsets of a system. Neural complexity, so defined, is one of a family of information theoretic metrics developed to measure the balance between the segregation and integration of a system's dynamics. One key question arising for such measures involves understanding how they are influenced by network topology. Sporns et al. [Cereb. Cortex 10, 127 (2000)] employed numerical models in order to determine the dependence of neural complexity on the topological features of a network. However, a complete picture has yet to be established. While De Lucia et al. [Phys. Rev. E 71, 016114 (2005)] made the first attempts at an analytical account of this relationship, their work utilized a formulation of neural complexity that, we argue, did not reflect the intuitions of the original work. In this paper we start by describing weighted connection matrices formed by applying a random continuous weight distribution to binary adjacency matrices. This allows us to derive an approximation for neural complexity in terms of the moments of the weight distribution and elementary graph motifs. In particular we explicitly establish a dependency of neural complexity on cyclic graph motifs

Equilibria and Dynamics of a Neural Network Model for Opponent Muscle Control

One of the advantages of biological skeleto-motor systems is the opponent muscle design, which in principle makes it possible to achieve facile independent control of joint angle and joint stiffness. Prior analysis of equilibrium states of a biologically-based neural network for opponent muscle control, the FLETE model, revealed that such independent control requires specialized interneuronal circuitry to efficiently coordinate the opponent force generators. In this chapter, we refine the FLETE circuit variables specification and update the equilibrium analysis. We also incorporate additional neuronal circuitry that ensures efficient opponent force generation and velocity regulation during movement.National Science Foundation (IRI-90-24877); Consejo Nacional de Ciencia y Tecnologia, Méxic

Cerebellar Learning in an Opponent Motor Controller for Adaptive Load Compensation and Synergy Formation

This paper shows how a minimal neural network model of the cerebellum may be embedded within a sensory-neuro-muscular control system that mimics known anatomy and physiology. With this embedding, cerebellar learning promotes load compensation while also allowing both coactivation and reciprocal inhibition of sets of antagonist muscles. In particular, we show how synaptic long term depression guided by feedback from muscle stretch receptors can lead to trans-cerebellar gain changes that are load-compensating. It is argued that the same processes help to adaptively discover multi-joint synergies. Simulations of rapid single joint rotations under load illustrates design feasibility and stability.National Science Foundation (IRI-90-24877, IRI-87-16960); Office of Naval Research (N00014-92-J-1309); Consejo Nacional de Ciencia y Technología (63462); Air Force Office of Scientific Research (F49620-92-J-0499); Defense Advanced Research Projects Agency (AFOSR 90-0083, ONR N00014-92-J-4015

Machine Learning Optimization of p-Type Transparent Conducting Films

p-Type transparent conducting materials (p-TCMs) are important components of optoelectronic devices including solar cells, photodetectors, displays, and flexible sensors. Cu-Zn-S thin films prepared by chemical bath deposition (CBD) can have both high transparency in the visible range (>80%) as well as excellent hole conductivity (>1000 S cm-1). However, the interplay between the deposition parameters in the CBD process (metal and sulfur precursor concentrations, temperature, pH, complexing agents, etc.) creates a multidimensional parameter space such that optimization for a specific application is challenging and time-consuming. Here we show that strategic design of experiment combined with machine learning (ML) allows for the efficient optimization of p-TCM performance. The approach is guided by a figure of merit (FOM) calculated from the film conductivity and optical transmission in the desired spectral range. A specific example is shown using two steps of optimization using a selected subset of four experimental CBD factors. The ML model is based on support vector regression employing a radial basis function as the kernel function. 10-fold cross-validation was performed to mitigate overfitting. After the first round of optimization, predicted areas in the parameter space with maximal FOMs were selected for a second round of optimization. Films with optimal FOMs were incorporated into heterojunction solar cells and transparent photodiodes. The optimization approach shown here will be generally applicable to any materials synthesis process with multiple parameters

Asymptotically Optimal Quantum Circuits for d-level Systems

As a qubit is a two-level quantum system whose state space is spanned by |0>, |1>, so a qudit is a d-level quantum system whose state space is spanned by |0>,...,|d-1>. Quantum computation has stimulated much recent interest in algorithms factoring unitary evolutions of an n-qubit state space into component two-particle unitary evolutions. In the absence of symmetry, Shende, Markov and Bullock use Sard's theorem to prove that at least C 4^n two-qubit unitary evolutions are required, while Vartiainen, Moettoenen, and Salomaa (VMS) use the QR matrix factorization and Gray codes in an optimal order construction involving two-particle evolutions. In this work, we note that Sard's theorem demands C d^{2n} two-qudit unitary evolutions to construct a generic (symmetry-less) n-qudit evolution. However, the VMS result applied to virtual-qubits only recovers optimal order in the case that d is a power of two. We further construct a QR decomposition for d-multi-level quantum logics, proving a sharp asymptotic of Theta(d^{2n}) two-qudit gates and thus closing the complexity question for all d-level systems (d finite.) Gray codes are not required, and the optimal Theta(d^{2n}) asymptotic also applies to gate libraries where two-qudit interactions are restricted by a choice of certain architectures.Comment: 18 pages, 5 figures (very detailed.) MatLab files for factoring qudit unitary into gates in MATLAB directory of source arxiv format. v2: minor change

Synthesis of Quantum Logic Circuits

We discuss efficient quantum logic circuits which perform two tasks: (i) implementing generic quantum computations and (ii) initializing quantum registers. In contrast to conventional computing, the latter task is nontrivial because the state-space of an n-qubit register is not finite and contains exponential superpositions of classical bit strings. Our proposed circuits are asymptotically optimal for respective tasks and improve published results by at least a factor of two. The circuits for generic quantum computation constructed by our algorithms are the most efficient known today in terms of the number of expensive gates (quantum controlled-NOTs). They are based on an analogue of the Shannon decomposition of Boolean functions and a new circuit block, quantum multiplexor, that generalizes several known constructions. A theoretical lower bound implies that our circuits cannot be improved by more than a factor of two. We additionally show how to accommodate the severe architectural limitation of using only nearest-neighbor gates that is representative of current implementation technologies. This increases the number of gates by almost an order of magnitude, but preserves the asymptotic optimality of gate counts.Comment: 18 pages; v5 fixes minor bugs; v4 is a complete rewrite of v3, with 6x more content, a theory of quantum multiplexors and Quantum Shannon Decomposition. A key result on generic circuit synthesis has been improved to ~23/48*4^n CNOTs for n qubit

CARCERAL EXTRACTIVISM, LIVELIHOOD STRATEGIES, AND “ACTING RIGHT” IN THE U.S. SOUTH

Mass incarceration and its effects are well documented and carceral privatization is hotly contested on moral and economic grounds. This dissertation examines the local effects of carceral privatization in the U.S. south in historical context. Tallulah is a small, rural predominately African American town in northeastern Louisiana that endures high rates of poverty, unemployment, and low educational attainment. It also hosts four private prisons operated by LaSalle Corrections, LLC. Two primary and overlapping questions guide the research. 1) How has an history of carceral entrepreneurship and mass incarceration impacted the way persons and communities create livelihoods and imagine futures, and how have these strategies changed over time? 2) In what ways does for-profit incarceration in Tallulah sustain historically racialized social and economic patterns of low educational attainment, unemployment, crime, and poverty? Findings presented here draw on 13 months of ethnographic data collected from 2015–2019 where I conducted informal interviews with multi-generational participants and partial life histories with persons aged 19-92, participant observation in community spaces and public meetings, as well as guided tours in the community and surrounding area and local archival research. The dissertation provides an overview of Louisiana’s carceral economy spanning chattel slavery, convict leasing, and sharecropping up to the more recent history of carceral entrepreneurship in Tallulah recounted from local newspaper archives, publicly available documents, and resident’s experiences. I argue that incarceration and prisons be understood as an extractive industrial enterprise (carceral extractivism) within a longer trajectory of expropriative racial capitalism. Examining the local history and effects of carceral entrepreneurship as it materialized locally in Tallulah during the 1990’s in the building of a men’s detention facility and a youth prison, since converted to a women’s transitional facility, illustrates how these processes involve private individual investors, the community, and state actors that national debates often leave unexamined. I argue that carceral entrepreneurship in Tallulah influences community livelihood strategies and well-being overtime through changing employment possibilities and wage migration but must be understood alongside and with other processes including periods of school integration, state policy towards social services, and the legacies of deep poverty, disenfranchisement, and criminalization in the south. Similarly, carceral entrepreneurship in Tallulah exacerbates socioeconomic challenges in the community, materially in the form of financial resources diverted from the city to private companies and the Sheriff’s office, but also in terms of imagined life outcomes and of living day to day in a “prison town” where the facilities predominate on the landscape. In response to the challenges presented by mass incarceration in the community I examine the ways in which people make lives worth living alongside extractive carceral institutions through various forms of work, including scrapping metal, cottage food industries, and involvements with local churches. Through these livelihood creating activities, centered around an ethic of “acting right,” non-carceral spaces of social reproduction are created, resisting, even as they are constrained by, carceral entrepreneurship in the community and broader region

Considering the Human and Nonhuman in Literary Studies: Notes for a Biographic Network Approach for the Study of Literary Objects

In recent years critical projects spanning philosophy, the social sciences, science studies, and nearly everywhere that has employed the term ecology have engaged in thinking humans and non-humans together as collectively producing outcomes, where objects do work beyond how humans perceive or make use of them. Taking Zelda Fitzgerald’s Save Me the Waltz as its focus, this thesis explores how this reorientation might contribute to literary studies and to literary criticism more specifically. The thesis considers a notion that novels constitute objects with biographies running “against” the biographic material of their authors, mobilizes actor network theory as a manner of mapping that biographic assemblage, and tentatively develops a biographic network approach as one alternative to traditional literary interpretative practices. Attending to the novel as an actor shifts critical focus away from its interior – the “text” or content – and expands traditional literary criticism’s default practice – interpretation – and logic – mimetic representation – in hopes of facilitating a discussion of Zelda’s novel in a manner which destabilizes the overdetermined themes that continue to scaffold her imaginary. Ultimately, this work argues that a biographic network approach can prove instructive as a “method” for dealing with other texts which remain relatively obscured at the margins of literary consciousness