Computational Simulation of Gene Regulatory Networks Implementing an Extendable Synchronous Single-Input Delay Flip-Flop and State Machine

We present a detailed and extendable design of the first synchronous single-input delay flip-flop implemented as a gene regulatory network in Escherichia coli (E. coli). The device, which we call the BioD, has one data input (trans-acting RNA), one clock input (far-red light) and an output that reports the state of the device using green fluorescent protein (GFP). The proposed design builds on Gardner’s toggle switch, to provide a more sophisticated device that can be synchronized with other devices within or without the same cell, and which requires only one data input. We provide a mathematical model of the system and simulation results. The results show that the device behaves in line with desired functionality. Further, we discuss the constraints of the design, which pertain to ranges of parameter values. The BioD is extended via the addition of an update function and input and output interfaces. The result is the BioFSM, which constitutes a synchronous and modular finite state machine, which uses an update function to change its state, stored in the BioD. The BioFSM uses its input and output interfaces for inter-cellular communications. This opens the door to the design of a circular cellular automata (the BioCell), which is envisioned as a number of communicating E. coli colonies, each made of clones of one BioFSM

Continuous-time analog circuits for statistical signal processing

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2003.Vita.Includes bibliographical references (p. 205-209).This thesis proposes an alternate paradigm for designing computers using continuous-time analog circuits. Digital computation sacrifices continuous degrees of freedom. A principled approach to recovering them is to view analog circuits as propagating probabilities in a message passing algorithm. Within this framework, analog continuous-time circuits can perform robust, programmable, high-speed, low-power, cost-effective, statistical signal processing. This methodology will have broad application to systems which can benefit from low-power, high-speed signal processing and offers the possibility of adaptable/programmable high-speed circuitry at frequencies where digital circuitry would be cost and power prohibitive. Many problems must be solved before the new design methodology can be shown to be useful in practice: Continuous-time signal processing is not well understood. Analog computational circuits known as "soft-gates" have been previously proposed, but a complementary set of analog memory circuits is still lacking. Analog circuits are usually tunable, rarely reconfigurable, but never programmable. The thesis develops an understanding of the convergence and synchronization of statistical signal processing algorithms in continuous time, and explores the use of linear and nonlinear circuits for analog memory. An exemplary embodiment called the Noise Lock Loop (NLL) using these design primitives is demonstrated to perform direct-sequence spread-spectrum acquisition and tracking functionality and promises order-of-magnitude wins over digital implementations. A building block for the construction of programmable analog gate arrays, the "soft-multiplexer" is also proposed.by Benjamin Vigoda.Ph.D

Global neural rhythm control by local neuromodulation

Neural oscillations are a ubiquitous form of neural activity seen across scales and modalities. These neural rhythms correlate with diverse cognitive functions and brain states. One mechanism for changing the oscillatory dynamics of large neuronal populations is through neuromodulator activity. An intriguing phenomenon explored here is when local neuromodulation of a distinct neuron type within a single brain nucleus exerts a powerful influence on global cortical rhythms. One approach to investigate the impact of local circuits on global rhythms is through optogenetic techniques. My first project involves the statistical analysis of electrophysiological recordings of an optogenetically-mediated Parkinsonian phenotype. Empirical studies demonstrate that Parkinsonian motor deficits correlate with the emergence of exaggerated beta frequency (15-30 Hz) oscillations throughout the cortico-basal ganglia-thalamic network. However, the mechanism of these aberrant oscillatory dynamics is not well understood. A previous modeling study predicted that cholinergic neuromodulation of medium spiny neurons in the striatum of the basal ganglia may mediate the pathologic beta rhythm. Here, this hypothesis was tested using selective optogenetic stimulation of striatal cholinergic interneurons in normal mice; stimulation robustly and reversibly amplified beta oscillations and Parkinsonian motor symptoms. The modulation of global rhythms by local networks was further studied using computational modeling in the context of intrathalamic neuromodulation. While intrathalamic vasoactive intestinal peptide (VIP) is known to cause long-lasting excitation in vitro, its in vivo dynamical effects have not been reported. Here, biophysical computational models were used to elucidate the impact of VIP on thalamocortical dynamics during sleep and propofol general anesthesia. The modeling results suggest that VIP can form robust sleep spindle oscillations and control aspects of sleep architecture through a novel homeostatic mechanism. This homeostatic mechanism would be inhibited by general anesthesia, representing a new mechanism contributing to anesthetic-induced loss of consciousness. While the previous two projects differed in their use of empirical versus theoretical methods, a challenge common to both domains is the difficulty in visualizing and analyzing large multi-dimensional datasets. A tool to mitigate these issues is introduced here: GIMBL-Vis is a Graphical Interactive Multi-dimensional extensiBLe Visualization toolbox for Matlab. This toolbox simplifies the process of exploring multi-dimensional data in Matlab by providing a graphical interface for visualization and analysis. Furthermore, it provides an extensible open platform for distributed development by the community

VLSI Implementation of a Spiking Neural Network

Im Rahmen der vorliegenden Arbeit wurden Konzepte und dedizierte Hardware entwickelt, die es erlauben, großskalige pulsgekoppelte neuronale Netze in Hardware zu realisieren. Die Arbeit basiert auf dem analogen VLSI-Modell eines pulsgekoppelten neuronalen Netzes, welches synaptische Plastizität (STPD) in jeder einzelnen Synapse beinhaltet. Das Modell arbeitet analog mit einem Geschwindigkeitszuwachs von bis zu 10^5 im Vergleich zur biologischen Echtzeit. Aktionspotentiale werden als digitale Ereignisse übertragen. Inhalt dieser Arbeit sind vornehmlich die digitale Hardware und die Übertragung dieser Ereignisse. Das analoge VLSI-Modell wurde in Verbindung mit Digitallogik, welche zur Verarbeitung neuronaler Ereignisse und zu Konfigurationszwecken dient, in einen gemischt analog-digitalen ASIC integriert, wobei zu diesem Zweck ein automatisierter Arbeitsablauf entwickelt wurde. Außerdem wurde eine entsprechende Kontrolleinheit in programmierbarer Logik implementiert und eine Hardware-Plattform zum parallelen Betrieb mehrerer neuronaler Netzwerkchips vorgestellt. Um das VLSI-Modell auf mehrere neuronale Netzwerkchips ausdehnen zu können, wurde ein Routing-Algorithmus entwickelt, welcher die Übertragung von Ereignissen zwischen Neuronen und Synapsen auf unterschiedlichen Chips ermöglicht. Die zeitlich korrekte Übertragung der Ereignisse, welche eine zwingende Bedingung für das Funktionieren von Plastizitätsmechanismen ist, wird durch diesen Algorithmus sichergestellt. Die Funktionalität des Algorithmus wird mittels Simulationen verifiziert. Weiterhin wird die korrekte Realisierung des gemischt analog-digitalen ASIC in Verbindung mit dem zugehörigen Hardware-System demonstriert und die Durchführbarkeit biologisch realistischer Experimente gezeigt. Das vorgestellte großskalige physikalische Modell eines neuronalen Netzwerks wird aufgrund seiner schnellen und parallelen Arbeitsweise für Experimentierzwecke in den Neurowissenschaften einsetzbar sein. Als Ergänzung zu numerischen Simulationen bietet es vor allem die Möglichkeit der intuitiven und umfangreichen Suche nach geeigneten Modellparametern

A Practical Investigation into Achieving Bio-Plausibility in Evo-Devo Neural Microcircuits Feasible in an FPGA

Many researchers has conjectured, argued, or in some cases demonstrated, that bio-plausibility can bring about emergent properties such as adaptability, scalability, fault-tolerance, self-repair, reliability, and autonomy to bio-inspired intelligent systems. Evolutionary-developmental (evo-devo) spiking neural networks are a very bio-plausible mixture of such bio-inspired intelligent systems that have been proposed and studied by a few researchers. However, the general trend is that the complexity and thus the computational cost grow with the bio-plausibility of the system. FPGAs (Field- Programmable Gate Arrays) have been used and proved to be one of the flexible and cost efficient hardware platforms for research' and development of such evo-devo systems. However, mapping a bio-plausible evo-devo spiking neural network to an FPGA is a daunting task full of different constraints and trade-offs that makes it, if not infeasible, very challenging. This thesis explores the challenges, trade-offs, constraints, practical issues, and some possible approaches in achieving bio-plausibility in creating evolutionary developmental spiking neural microcircuits in an FPGA through a practical investigation along with a series of case studies. In this study, the system performance, cost, reliability, scalability, availability, and design and testing time and complexity are defined as measures for feasibility of a system and structural accuracy and consistency with the current knowledge in biology as measures for bio-plausibility. Investigation of the challenges starts with the hardware platform selection and then neuron, cortex, and evo-devo models and integration of these models into a whole bio-inspired intelligent system are examined one by one. For further practical investigation, a new PLAQIF Digital Neuron model, a novel Cortex model, and a new multicellular LGRN evo-devo model are designed, implemented and tested as case studies. Results and their implications for the researchers, designers of such systems, and FPGA manufacturers are discussed and concluded in form of general trends, trade-offs, suggestions, and recommendations

Stochastic and complex dynamics in mesoscopic brain networks

The aim of this thesis is to deepen into the understanding of the mechanisms responsible for the generation of complex and stochastic dynamics, as well as emerging phenomena, in the human brain. We study typical features from the mesoscopic scale, i.e., the scale in which the dynamics is given by the activity of thousands or even millions of neurons. At this scale the synchronous activity of large neuronal populations gives rise to collective oscillations of the average voltage potential. These oscillations can easily be recorded using electroencephalography devices (EEG) or measuring the Local Field Potentials (LFPs). In Chapter 5 we show how the communication between two cortical columns (mesoscopic structures) can be mediated efficiently by a microscopic neural network. We use the synchronization of both cortical columns as a probe to ensure that an effective communication is established between the three neural structures. Our results indicate that there are certain dynamical regimes from the microscopic neural network that favor the correct communication between the cortical columns: therefore, if the LFP frequency of the neural network is of around 40Hz, the synchronization between the cortical columns is more robust compared to the situation in which the neural network oscillates at a lower frequency (10Hz). However, microscopic topological characteristics of the network also influence communication, being a small-world structure the one that best promotes the synchronization of the cortical columns. Finally, this Chapter shows how the mediation exerted by the neural network cannot be substituted by the average of its activity, that is, the dynamic properties of the microscopic neural network are essential for the proper transmission of information between all neural structures. The oscillatory brain electrical activity is largely dependent on the interplay between excitation and inhibition. In Chapter 6 we study how groups of cortical columns show complex patterns of cortical excitation and inhibition taking into account their topological features and the strength of their couplings. These cortical columns segregate between those dominated by excitation and those dominated by inhibition, affecting the synchronization properties of networks of cortical columns. In Chapter 7 we study a dynamic regime by which complex patterns of synchronization between chaotic oscillators appear spontaneously in a network. We show what conditions must a set of coupled dynamical systems fulfill in order to display heterogeneity in synchronization. Therefore, our results are related to the complex phenomenon of synchronization in the brain, which is a focus of study nowadays. Finally, in Chapter 8 we study the ability of the brain to compute and process information. The novelty here is our use of complex synchronization in the brain in order to implement basic elements of Boolean computation. In this way, we show that the partial synchronization of the oscillations in the brain establishes a code in terms of synchronization / non-synchronization (1/0, respectively), and thus all simple Boolean functions can be implemented (AND, OR, XOR, etc.). We also show that complex Boolean functions, such as a flip-flop memory, can be constructed in terms of states of dynamic synchronization of brain oscillations.L'objectiu d'aquesta Tesi és aprofundir en la comprensió dels mecanismes responsables de la generació de dinàmica complexa i estocàstica, així com de fenòmens emergents, en el cervell humà. Estudiem la fenomenologia característica de l'escala mesoscòpica, és a dir, aquella en la que la dinàmica característica ve donada per l'activitat de milers de neurones. En aquesta escala l'activitat síncrona de grans poblacions neuronals dóna lloc a un fenomen col·lectiu pel qual es produeixen oscil·lacions del seu potencial mitjà. Aquestes oscil·lacions poden ser fàcilment enregistrades mitjançant aparells d'electroencefalograma (EEG) o enregistradors de Potencials de Camp Local (LFP). En el Capítol 5 mostrem com la comunicació entre dos columnes corticals (estructures mesoscòpiques) pot ser conduïda de forma eficient per una xarxa neuronal microscòpica. De fet, emprem la sincronització de les dues columnes corticals per comprovar que s'ha establert una comunicació efectiva entre les tres estructures neuronals. Els resultats indiquen que hi ha règims dinàmics de la xarxa neuronal microscòpica que afavoreixen la correcta comunicació entre les columnes corticals: si la freqüència típica de LFP a la xarxa neuronal està al voltant dels 40Hz la sincronització entre les columnes corticals és més robusta que a una menor freqüència (10Hz). La topologia de la xarxa microscòpica també influeix en la comunicació, essent una estructura de tipus món petit (small-world) la que més afavoreix la sincronització. Finalment, la mediació de xarxa neuronal no pot ser substituïda per la mitjana de la seva activitat, és a dir, les propietats dinàmiques microscòpiques són imprescindibles per a la correcta transmissió d'informació entre totes les escales cerebrals. L'activitat elèctrica oscil·latòria cerebral ve donada en gran mesura per la interacció entre excitació i inhibició neuronal. En el Capítol 6 estudiem com grups de columnes corticals mostren patrons complexos d'excitació i inhibició segons quina sigui la seva topologia i d'acoblament. D'aquesta manera les columnes corticals se segreguen entre aquelles dominades per l'excitació i aquelles dominades per la inhibició, influint en les capacitats de sincronització de xarxes de columnes corticals. En el Capítol 7 estudiem un règim dinàmic segons el qual patrons complexos de sincronització apareixen espontàniament en xarxes d'oscil·ladors caòtics. Mostrem quines condicions s'han de donar en un conjunt de sistemes dinàmics acoblats per tal de mostrar heterogeneïtat en la sincronització, és a dir, coexistència de sincronitzacions. D'aquesta manera relacionem els nostres resultats amb el fenomen de sincronització complexa en el cervell. Finalment, en el Capítol 8 estudiem com el cervell computa i processa informació. La novetat aquí és l'ús que fem de la sincronització complexa de columnes corticals per tal d'implementar elements bàsics de computació Booleana. Mostrem com la sincronització parcial de les oscil·lacions cerebrals estableix un codi neuronal en termes de sincronització/no sincronització (1/0, respectivament) amb el qual totes les funcions Booleanes simples poden ésser implementades (AND, OR, XOR, etc). Mostrem, també, com emprant xarxes mesoscòpiques extenses les capacitats de computació creixen proporcionalment. Així funcions Booleanes complexes, com una memòria del tipus flip-flop, pot ésser construïda en termes d'estats de sincronització dinàmica d'oscil·lacions cerebrals.Postprint (published version

Social Insect-Inspired Adaptive Hardware

Modern VLSI transistor densities allow large systems to be implemented within a single chip. As technologies get smaller, fundamental limits of silicon devices are reached resulting in lower design yields and post-deployment failures. Many-core systems provide a platform for leveraging the computing resource on offer by deep sub-micron technologies and also offer high-level capabilities for mitigating the issues with small feature sizes. However, designing for many-core systems that can adapt to in-field failures and operation variability requires an extremely large multi-objective optimisation space. When a many-core reaches the size supported by the densities of modern technologies (thousands of processing cores), finding design solutions in this problem space becomes extremely difficult. Many biological systems show properties that are adaptive and scalable. This thesis proposes a self-optimising and adaptive, yet scalable, design approach for many-core based on the emergent behaviours of social-insect colonies. In these colonies there are many thousands of individuals with low intelligence who contribute, without any centralised control, to complete a wide range of tasks to build and maintain the colony. The experiments presented translate biological models of social-insect intelligence into simple embedded intelligence circuits. These circuits sense low-level system events and use this manage the parameters of the many-core's Network-on-Chip (NoC) during runtime. Centurion, a 128-node many-core, was created to investigate these models at large scale in hardware. The results show that, by monitoring a small number of signals within each NoC router, task allocation emerges from the social-insect intelligence models that can self-configure to support representative applications. It is demonstrated that emergent task allocation supports fault tolerance with no extra hardware overhead. The response-threshold decision making circuitry uses a negligible amount of hardware resources relative to the size of the many-core and is an ideal technology for implementing embedded intelligence for system runtime management of large-complexity single-chip systems

Rôle de deux groupes de vésicules dans la transmission synaptique

Les synapses formées par les fibres moussues (FM) sur les cellules principales de la région CA3 (FM-CA3) jouent un rôle crucial pour la formation de la mémoire spatiale dans l’hippocampe. Une caractéristique des FM est la grande quantité de zinc localisée avec le glutamate dans les vésicules synaptiques recyclées par la voie d’endocytose dépendante de l’AP3. En combinant l’imagerie calcique et l’électrophysiologie, nous avons étudié le rôle des vésicules contenant le zinc dans la neurotransmission aux synapses FM-CA3. Contrairement aux études précédentes, nous n’avons pas observé de rôle pour le zinc dans l’induction des vagues calciques. Nos expériences ont révélé que les vagues calciques sont dépendantes de l’activation des récepteurs métabotropiques et ionotropiques du glutamate. D’autre part, nos données indiquent que les vésicules dérivées de la voie dépendante de l’AP3 forment un groupe de vésicules possédant des propriétés spécifiques. Elles contribuent principalement au relâchement asynchrone du glutamate. Ainsi, les cellules principales du CA3 de souris n’exprimant pas la protéine AP3 avaient une probabilité inférieure de décharge et une réduction de la synchronie des potentiels d’action lors de la stimulation à fréquences physiologiques. Cette diminution de la synchronie n’était pas associée avec un changement des paramètres quantiques ou de la taille des groupes de vésicules. Ces résultats supportent l’hypothèse que deux groupes de vésicules sont présents dans le même bouton synaptique. Le premier groupe est composé de vésicules recyclées par la voie d’endocytose utilisant la clathrine et participe au relâchement synchrone du glutamate. Le second groupe est constitué de vésicules ayant été recyclées par la voie d’endocytose dépendante de l’AP3 et contribue au relâchement asynchrone du glutamate. Ces deux groupes de vésicules sont nécessaires pour l’encodage de l’information et pourraient être importants pour la formation de la mémoire. Ainsi, les décharges de courte durée à haute fréquence observées lorsque les animaux pénètrent dans les places fields pourraient causer le relâchement asynchrone de glutamate. Finalement, les résultats de mon projet de doctorat valident l’existence et l’importance de deux groupes de vésicules dans les MF qui sont recyclées par des voies d’endocytoses distinctes et relâchées durant différents types d’activités.Mossy fiber-CA3 pyramidal cell synapses play a crucial role in the hippocampal formation of spatial memories. These synaptic connections possess a number of unique features substantial for its role in the information processing and coding. One of these features is presence of zinc co-localized with glutamate within a subpopulation of synaptic vesicles recycling through AP3-dependent bulk endocytosis. Using Ca2+ imaging and electrophysiological recordings we investigated role of these zinc containing vesicles in the neurotransmission. In contrast to previous reports, we did not observe any significant role of vesicular zinc in the induction of large postsynaptic Ca2+ waves triggered by burst stimulation. Moreover, our experiments revealed that Ca2+ waves mediated by Ca2+ release from internal stores are dependent not only on the activation of metabotropic, but also ionotropic glutamate receptors. Nevertheless, subsequent experiments unveiled that the vesicles derived via AP3-dependent endocytosis primary contribute to the asynchronous, but not synchronous mode of glutamate release. Futhermore, knockout mice lacking adaptor protein AP3 had a reduced synchronization of postsynaptic action potentials and impaired information transfer; this was not associated with any changes in the synchronous release quantal parameters and vesicle pool size. These findings strongly support the idea that within a single presynaptic bouton two heterogeneous pools of releasable vesicles are present. One pool of readily releasable vesicles forms via clathrin mediated endocytosis and mainly participates in the synchronous release; a second pool forms through bulk endocytosis and primarily supplies asynchronous release. The existence of two specialized pools is essential for the information coding and transfer within hippocampus. It also might be important for hippocampal memory formation. In contrast to low firing rates at rest, dentate gyrus granule cells tend to fire high frequency bursts once an animal enters a place field. These burst activities, embedded in the lower gamma frequency, should be especially efficient in the triggering of substantial asynchronous glutamate release. Therefore, the results of my PhD project for the first time provide strong evidence for the presence and physiological importance of two vesicle pools with heterogeneous release and recycling properties via separate endocytic pathways within the same mossy fiber bouton

Multichannel biomedical telemetry system using delta modulation

Telemetering of biomedical data from unrestrained subjects requires a system to be compact, reliable and efficient. A survey of the existing multi-channel biomedical telemetry showed that most of the systems employ analogue or uncoded (digital) techniques of encoding biomedical signals. These techniques are less reliable, employ wider bandwidth and are difficult to implement compared to the coded (digital) techniques of modulation. A theoretical study of the coded techniques of modulation for encoding biomedical signals showed-that pulse code modulation, though more efficient, calls for extensive circuitry and makes it expensive and difficult to implement. Delta modulation and delta sigma modulation were found to be simpler, easier to Implement and efficient. [Continues.