Fan-In analysis of a leaky integrator circuit using charge transfer synapses

It is shown that a simple leaky integrator (LI) circuit operating in a dynamic mode can allow spatial and temporal summation of weighted synaptic outputs. The circuit incorporates a current mirror configuration to sum charge packets released from charge transfer synapses and an n-channel MOSFET, operating in subthreshold, serves to implement a leakage capability, which sets the decay time for the postsynaptic response. The focus of the paper is to develop an analytical model for fan-in and validate the model against simulation and experimental results obtained from a prototype chip fabricated in the AMS 0.35 μm mixed signal CMOS technology. We show that the model predicts the theoretical limit on fan-in, relates the magnitude of the postsynaptic response to weighted synaptic inputs and captures the transient response of the LI when stimulated with spike inputs

Bifurcation analysis in a silicon neuron

International audienceIn this paper, we describe an analysis of the nonlinear dynamical phenomenon associated with a silicon neuron. Our silicon neuron integrates Hodgkin-Huxley (HH) model formalism, including the membrane voltage dependency of temporal dynamics. Analysis of the bifurcation conditions allow us to identify different regimes in the parameter space that are desirable for biasing our silicon neuron. This approach of studying bifurcations is useful because it is believed that computational properties of neurons are based on the bifurcations exhibited by these dynamical systems in response to some changing stimulus. We describe numerical simulations and measurements of the Hopf bifurcation which is characteristic of class 2 excitability in the HH model. We also show a phenomenon observed in biological neurons and termed excitation block. Hence, by showing that this silicon neuron has similar bifurcations to a certain class of biological neurons, we can claim that the silicon neuron can also perform similar computation

Neuronal Ion-Channel Dynamics in Silicon

We present a simple silicon circuit for modeling voltage-dependent ion channels found within neural cells, capturing both the gating particle\u27s sigmoidal activation (or inactivation) and the bell-shaped time constant. In its simplest form, our ion-channel analog consists of two MOS transistors and a unity-gain inverter. We present equations describing its nonlinear dynamics and measurements from a chip fabricated in a 0.25 /spl µ/m CMOS process. The channel analog\u27s simplicity allows tens of thousands to be built on a single chip, facilitating the implementation of biologically realistic models of neural computation

Thermodynamically Equivalent Silicon Models of Voltage-Dependent Ion Channels

We model ion channels in silicon by exploiting similarities between the thermodynamic principles that govern ion channels and those that govern transistors. Using just eight transistors, we replicate—for the first time in silicon—the sigmoidal voltage dependence of activation (or inactivation) and the bell-shaped voltage-dependence of its time constant. We derive equations describing the dynamics of our silicon analog and explore its flexibility by varying various parameters. In addition, we validate the design by implementing a channel with a single activation variable. The design’s compactness allows tens of thousands of copies to be built on a single chip, facilitating the study of biologically realistic models of neural computation at the network level in silicon

Neuromodulation of Neuromorphic Circuits

We present a novel methodology to enable control of a neuromorphic circuit in close analogy with the physiological neuromodulation of a single neuron. The methodology is general in that it only relies on a parallel interconnection of elementary voltage-controlled current sources. In contrast to controlling a nonlinear circuit through the parameter tuning of a state-space model, our approach is purely input-output. The circuit elements are controlled and interconnected to shape the current-voltage characteristics (I-V curves) of the circuit in prescribed timescales. In turn, shaping those I-V curves determines the excitability properties of the circuit. We show that this methodology enables both robust and accurate control of the circuit behavior and resembles the biophysical mechanisms of neuromodulation. As a proof of concept, we simulate a SPICE model composed of MOSFET transconductance amplifiers operating in the weak inversion regime.The research leading to these results has received funding from the European Research Council under the Advanced ERC Grant Agreement Switchlet n.67064

Synthesis of neuromorphic circuits with neuromodulatory properties

The field of neuromorphic engineering shows great promise in delivering novel devices inspired by biological principles that would undertake sensory and processing tasks with an unprecedented level of efficiency. In order to achieve that, engineers are required to understand and implement the many complex biological regulatory mechanisms that allow the nervous system to robustly operate and adapt over scales covering many orders of magnitude, while at the same time using unreliable and noisy components. As a step towards that, this thesis aims at discussing and implementing the principles of neuromodulation in neuromorphic hardware, mechanisms which allow neurons to change and regulate their behaviour through the continuous control of their internal currents. We discuss how neural dynamics and its modulation can be broken down into four essential feedback loops, and we introduce a simplified model of the neural membrane respecting this fundamental structure. We present a novel methodology for controlling the neuron's behaviour through the shaping of its I-V curves in distinct timescales, thus characterising the behaviour of the neural circuit through its input-output properties. We show how modulation of the feedback loops affects the behaviour, and importantly, captures the transition between spiking and bursting oscillatory regimes, two major signalling modes of neurons. We then show how the architecture can be easily implemented using well-known neuromorphic building blocks based on subthreshold MOSFET circuits. Finally, we discuss how the excitability switch captured by the model can be exploited in simple network settings, thus opening up the possibility for future research into novel architectures where the control of cellular properties is utilised to shape the global behaviour of the network

A Codimension-2 Bifurcation Controlling Endogenous Bursting Activity and Pulse-Triggered Responses of a Neuron Model

The dynamics of individual neurons are crucial for producing functional activity in neuronal networks. An open question is how temporal characteristics can be controlled in bursting activity and in transient neuronal responses to synaptic input. Bifurcation theory provides a framework to discover generic mechanisms addressing this question. We present a family of mechanisms organized around a global codimension-2 bifurcation. The cornerstone bifurcation is located at the intersection of the border between bursting and spiking and the border between bursting and silence. These borders correspond to the blue sky catastrophe bifurcation and the saddle-node bifurcation on an invariant circle (SNIC) curves, respectively. The cornerstone bifurcation satisfies the conditions for both the blue sky catastrophe and SNIC. The burst duration and interburst interval increase as the inverse of the square root of the difference between the corresponding bifurcation parameter and its bifurcation value. For a given set of burst duration and interburst interval, one can find the parameter values supporting these temporal characteristics. The cornerstone bifurcation also determines the responses of silent and spiking neurons. In a silent neuron with parameters close to the SNIC, a pulse of current triggers a single burst. In a spiking neuron with parameters close to the blue sky catastrophe, a pulse of current temporarily silences the neuron. These responses are stereotypical: the durations of the transient intervals–the duration of the burst and the duration of latency to spiking–are governed by the inverse-square-root laws. The mechanisms described here could be used to coordinate neuromuscular control in central pattern generators. As proof of principle, we construct small networks that control metachronal-wave motor pattern exhibited in locomotion. This pattern is determined by the phase relations of bursting neurons in a simple central pattern generator modeled by a chain of oscillators

Microfluidic Devices and Systems for Neuroscience Studies in Caenorhabditiselegans (C. elegans).

C. elegans, a tiny, transparent roundworm with a simple nervous system (302 neurons) and a diverse repertoire of behavioral outputs, has been extensively used as a model organism in neuroscience. Its optically accessible, compact nervous system offers a unique advantage for understanding the ability of the nervous system to compute various behaviors of an organism. C. elegans, however poses a challenge as a model organism – due to its small size (1 mm in length and 40 – 50 µm in diameter), conducting experimental procedures on the worm is skill-intensive and time consuming. To this end, microfluidic technology has recently emerged as a preferred tool for conducting experimental procedures on the worm and this thesis contributes towards the development of such microfluidic approaches. We demonstrated the design and development of microfluidic devices and systems that serve the following applications : a) Immobilization - We developed two microfluidic approaches for immobilizing C. elegans on-chip. These approaches are easy to implement, allow worm recovery within a few seconds after immobilization and can be easily adopted for conducting cell developmental and neuron regeneration studies in C. elegans. b) Calcium Imaging - We developed an automated microfluidic platform for collecting stimulus-evoked calcium imaging data from single neurons. We utilized the platform to monitor neuronal activity in the chemosensory neuron - ASH - in response to different stimuli (chemical and electrical) and characterized its dependence on the age of the worm. The platform enabled us to hypothesize that the neuronal functionality is altered with age. We believe that the use of microfluidic devices will allow the observation of large scale neuronal dynamics in C. elegans. Consequently, we foresee the use of computational procedures for uncovering new insights about the worm’s nervous system. To this end, we propose a hardware based computational platform for emulating the worm’s nervous system. And, as a step towards this futuristic goal, we present an analog circuit that emulates the observed ASH neuronal activity in C. elegans. We envision that the work demonstrated in this thesis will expand the toolsets available for conducting neuroscience studies in C. elegans.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89766/1/tchokshi_1.pd

Networks of spiking neurons and plastic synapses: implementation and control

The brain is an incredible system with a computational power that goes further beyond those of our standard computer. It consists of a network of 1011 neurons connected by about 1014 synapses: a massive parallel architecture that suggests that brain performs computation according to completely new strategies which we are far from understanding. To study the nervous system a reasonable starting point is to model its basic units, neurons and synapses, extract the key features, and try to put them together in simple controllable networks. The research group I have been working in focuses its attention on the network dynamics and chooses to model neurons and synapses at a functional level: in this work I consider network of integrate-and-fire neurons connected through synapses that are plastic and bistable. A synapses is said to be plastic when, according to some kind of internal dynamics, it is able to change the “strength”, the efficacy, of the connection between the pre- and post-synaptic neuron. The adjective bistable refers to the number of stable states of efficacy that a synapse can have; we consider synapses with two stable states: potentiated (high efficacy) or depressed (low efficacy). The considered synaptic model is also endowed with a new stop-learning mechanism particularly relevant when dealing with highly correlated patterns. The ability of this kind of systems of reproducing in simulation behaviors observed in biological networks, give sense to an attempt of implementing in hardware the studied network. This thesis situates at this point: the goal of this work is to design, control and test hybrid analog-digital, biologically inspired, hardware systems that behave in agreement with the theoretical and simulations predictions. This class of devices typically goes under the name of neuromorphic VLSI (Very-Large-Scale Integration). Neuromorphic engineering was born from the idea of designing bio-mimetic devices and represents a useful research strategy that contributes to inspire new models, stimulates the theoretical research and that proposes an effective way of implementing stand-alone power-efficient devices. In this work I present two chips, a prototype and a larger device, that are a step towards endowing VLSI, neuromorphic systems with autonomous learning capabilities adequate for not too simple statistics of the stimuli to be learnt. The main novel features of these chips are the implemented type of synaptic plasticity and the configurability of the synaptic connectivity. The reported experimental results demonstrate that the circuits behave in agreement with theoretical predictions and the advantages of the stop-learning synaptic plasticity when highly correlated patterns have to be learnt. The high degree of flexibility of these chips in the definition of the synaptic connectivity is relevant in the perspective of using such devices as building blocks of parallel, distributed multi-chip architectures that will allow to scale up the network dimensions to systems with interesting computational abilities capable to interact with real-world stimuli