183 research outputs found
Tunable Neuromimetic Integrated System for Emulating Cortical Neuron Models
Nowadays, many software solutions are currently available for simulating neuron models. Less conventional than software-based systems, hardware-based solutions generally combine digital and analog forms of computation. In previous work, we designed several neuromimetic chips, including the Galway chip that we used for this paper. These silicon neurons are based on the Hodgkin–Huxley formalism and they are optimized for reproducing a large variety of neuron behaviors thanks to tunable parameters. Due to process variation and device mismatch in analog chips, we use a full-custom fitting method in voltage-clamp mode to tune our neuromimetic integrated circuits. By comparing them with experimental electrophysiological data of these cells, we show that the circuits can reproduce the main firing features of cortical cell types. In this paper, we present the experimental measurements of our system which mimic the four most prominent biological cells: fast spiking, regular spiking, intrinsically bursting, and low-threshold spiking neurons into analog neuromimetic integrated circuit dedicated to cortical neuron simulations. This hardware and software platform will allow to improve the hybrid technique, also called “dynamic-clamp,” that consists of connecting artificial and biological neurons to study the function of neuronal circuits
Synchronous Behavior of Two Coupled Electronic Neurons
We report on experimental studies of synchronization phenomena in a pair of
analog electronic neurons (ENs). The ENs were designed to reproduce the
observed membrane voltage oscillations of isolated biological neurons from the
stomatogastric ganglion of the California spiny lobster Panulirus interruptus.
The ENs are simple analog circuits which integrate four dimensional
differential equations representing fast and slow subcellular mechanisms that
produce the characteristic regular/chaotic spiking-bursting behavior of these
cells. In this paper we study their dynamical behavior as we couple them in the
same configurations as we have done for their counterpart biological neurons.
The interconnections we use for these neural oscillators are both direct
electrical connections and excitatory and inhibitory chemical connections: each
realized by analog circuitry and suggested by biological examples. We provide
here quantitative evidence that the ENs and the biological neurons behave
similarly when coupled in the same manner. They each display well defined
bifurcations in their mutual synchronization and regularization. We report
briefly on an experiment on coupled biological neurons and four dimensional ENs
which provides further ground for testing the validity of our numerical and
electronic models of individual neural behavior. Our experiments as a whole
present interesting new examples of regularization and synchronization in
coupled nonlinear oscillators.Comment: 26 pages, 10 figure
Synthesis and analysis of nonlinear, analog, ultra low power, Bernoulli cell based CytoMimetic circuits for biocomputation
A novel class of analog BioElectronics is introduced for the systematic implementation of ultra-low power microelectronic circuits, able to compute nonlinear biological dynamics. This class of circuits is termed ``CytoMimetic Circuits'', in an attempt to highlight their actual function, which is mimicking biological responses, as observed experimentally. Inspired by the ingenious Bernoulli Cell Formalism (BCF), which was originally formulated for the modular synthesis and analysis of linear, time-invariant, high-dynamic range, logarithmic filters, a new, modified mathematical framework has been conceived, termed Nonlinear Bernoulli Cell Formalism (NBCF), which forms the core mathematical framework, characterising the operation of CytoMimetic circuits. The proposed nonlinear, transistor-level mathematical formulation exploits the striking similarities existing between the NBCF and coupled ordinary differential equations, typically appearing in models of naturally encountered biochemical systems. The resulting continuous-time, continuous-value, low-power CytoMimetic electronic circuits succeed in simulating with good accuracy cellular and molecular dynamics and found to be in very good agreement with their biological counterparts. They usually occupy an area of a fraction of a square millimetre, while consuming between hundreds of nanowatts and few tenths of microwatts of power. The systematic nature of the NBCF led to the transformation of a wide variety of biochemical reactions into nonlinear Log-domain circuits, which span a large area of different biological model types.
Moreover, a detailed analysis of the robustness and performance of the proposed circuit class is also included in this thesis. The robustness examination has been conducted via post-layout simulations of an indicative CytoMimetic circuit and also by providing fabrication-related variability simulations, obtained by means of analog Monte Carlo statistical analysis for each one of the proposed circuit topologies. Furthermore, a detailed mathematical analysis that is carefully addressing the effect of process-parameters and MOSFET geometric properties upon subthreshold translinear circuits has been conducted for the fundamental translinear blocks, CytoMimetic topologies are comprised of. Finally, an interesting sub-category of Neuromorphic circuits, the ``Log-Domain Silicon Synapses'' is presented and representative circuits are thoroughly analysed by a novel, generalised BC operator framework. This leads to the conclusion that the BC operator consists the heart of such Log-domain circuits, therefore, allows the establishment of a general class of BC-based silicon synaptic circuits, which includes most of the synaptic circuits, implemented so far in Log-domain.Open Acces
Towards a Brain-inspired Information Processing System: Modelling and Analysis of Synaptic Dynamics: Towards a Brain-inspired InformationProcessing System: Modelling and Analysis ofSynaptic Dynamics
Biological neural systems (BNS) in general and the central nervous system (CNS) specifically
exhibit a strikingly efficient computational power along with an extreme flexible and adaptive basis
for acquiring and integrating new knowledge. Acquiring more insights into the actual mechanisms
of information processing within the BNS and their computational capabilities is a core objective
of modern computer science, computational sciences and neuroscience. Among the main reasons
of this tendency to understand the brain is to help in improving the quality of life of people suffer
from loss (either partial or complete) of brain or spinal cord functions. Brain-computer-interfaces
(BCI), neural prostheses and other similar approaches are potential solutions either to help these
patients through therapy or to push the progress in rehabilitation. There is however a significant
lack of knowledge regarding the basic information processing within the CNS. Without a better
understanding of the fundamental operations or sequences leading to cognitive abilities, applications
like BCI or neural prostheses will keep struggling to find a proper and systematic way to
help patients in this regard. In order to have more insights into these basic information processing
methods, this thesis presents an approach that makes a formal distinction between the essence
of being intelligent (as for the brain) and the classical class of artificial intelligence, e.g. with
expert systems. This approach investigates the underlying mechanisms allowing the CNS to be
capable of performing a massive amount of computational tasks with a sustainable efficiency and
flexibility. This is the essence of being intelligent, i.e. being able to learn, adapt and to invent.
The approach used in the thesis at hands is based on the hypothesis that the brain or specifically a
biological neural circuitry in the CNS is a dynamic system (network) that features emergent capabilities.
These capabilities can be imported into spiking neural networks (SNN) by emulating the
dynamic neural system. Emulating the dynamic system requires simulating both the inner workings
of the system and the framework of performing the information processing tasks. Thus, this
work comprises two main parts. The first part is concerned with introducing a proper and a novel
dynamic synaptic model as a vital constitute of the inner workings of the dynamic neural system.
This model represents a balanced integration between the needed biophysical details and being
computationally inexpensive. Being a biophysical model is important to allow for the abilities of
the target dynamic system to be inherited, and being simple is needed to allow for further implementation
in large scale simulations and for hardware implementation in the future. Besides, the
energy related aspects of synaptic dynamics are studied and linked to the behaviour of the networks
seeking for stable states of activities. The second part of the thesis is consequently concerned with
importing the processing framework of the dynamic system into the environment of SNN. This
part of the study investigates the well established concept of binding by synchrony to solve the information binding problem and to proposes the concept of synchrony states within SNN. The
concepts of computing with states are extended to investigate a computational model that is based
on the finite-state machines and reservoir computing. Biological plausible validations of the introduced
model and frameworks are performed. Results and discussions of these validations indicate
that this study presents a significant advance on the way of empowering the knowledge about the
mechanisms underpinning the computational power of CNS. Furthermore it shows a roadmap on
how to adopt the biological computational capabilities in computation science in general and in
biologically-inspired spiking neural networks in specific. Large scale simulations and the development
of neuromorphic hardware are work-in-progress and future work. Among the applications
of the introduced work are neural prostheses and bionic automation systems
Digital neural circuits : from ions to networks
PhD ThesisThe biological neural computational mechanism is always fascinating to human beings since it shows several state-of-the-art characteristics: strong fault tolerance, high power efficiency and self-learning capability. These behaviours lead the developing trend of designing the next-generation digital computation platform. Thus investigating and understanding how the neurons talk with each other is the key to replicating these calculation features. In this work I emphasize using tailor-designed digital circuits for exactly implementing bio-realistic neural network behaviours, which can be considered a novel approach to cognitive neural computation. The first advance is that biological real-time computing performances allow the presented circuits to be readily adapted for real-time closed-loop in vitro or in vivo experiments, and the second one is a transistor-based circuit that can be directly translated into an impalpable chip for high-level neurologic disorder rehabilitations. In terms of the methodology, first I focus on designing a heterogeneous or multiple-layer-based architecture for reproducing the finest neuron activities both in voltage-and calcium-dependent ion channels. In particular, a digital optoelectronic neuron is developed as a case study. Second, I focus on designing a network-on-chip architecture for implementing a very large-scale neural network (e.g. more than 100,000) with human cognitive functions (e.g. timing control mechanism). Finally, I present a reliable hybrid bio-silicon closed-loop system for central pattern generator prosthetics, which can be considered as a framework for digital neural circuit-based neuro-prosthesis implications. At the end, I present the general digital neural circuit design principles and the long-term social impacts of the presented work
Psyche, Signals and Systems
For a century or so, the multidisciplinary nature of neuroscience has left the field fractured into distinct areas of research. In particular, the subjects of consciousness and perception present unique challenges in the attempt to build a unifying understanding bridging between the micro-, meso-, and macro-scales of the brain and psychology. This chapter outlines an integrated view of the neurophysiological systems, psychophysical signals, and theoretical considerations related to consciousness. First, we review the signals that correlate to consciousness during psychophysics experiments. We then review the underlying neural mechanisms giving rise to these signals. Finally, we discuss the computational and theoretical functions of such neural mechanisms, and begin to outline means in which these are related to ongoing theoretical research
The stochastic neural network in VLSI for studying noise communication in crayfish
L'attivita neurale in natura presenta un andamento stocastico e gioca un ruolo significativo nel cervello. Tuttavia, la maggior parte degli articoli si limitano alla simulazione di neuroni stocastici. In questa tesi, proponiamo un nuovo modello stocastico secondo il formalismo di Hodgkin-Huxley basato su equazioni dierenziali stocastiche e moto browniano. Il nuovo modello di equazione dierenziale stocastiche riproduce una vasta gamma di dinamiche in modo piu realistico rispetto ai precedenti modelli deterministici. Tale modello stocastico e stato applicata a una semplice rete neurale che si trova sulla coda di un gambero chiamato CPR (caudal photoreceptor). Presentiamo una libreria di operatori analogici stocastici utilizzati per il calcolo analogico in tempo reale. Questa libreria
permette di ottenere una implementazione in silicio della rete stocastica CPR che sarà collegata alle cellule nervose del gambero. L'interazione vivente-articiali permettera ai
biologisti di comprendere meglio i fenomeni nervosi //
The Neural activity in nature presents a stochastic trend and plays an important role in the brain. However, most papers are limited simulating stochastic neurons. In this thesis, we propose a novel stochastic model according to the Hodgkin{Huxley formalism using stochastic dierential equations and Brownian motion. The new stochastic dierential equation model reproduces a large range of dynamics more realistically than previous deterministic models. Such stochastic model has been applied to simple neural network that is located on the tail of the craysh called CPR (caudal photoreceptor). We present
a library of stochastic analog operators used for the analog real-time computation. This library allows to obtain a silicon implementation of the CPR stochastic network that will
be connected to the nerve cells of the craysh. The living-articial interaction will allow biologists to better understand the nervous phenomen
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