Point-to-point connectivity between neuromorphic chips using address events

This paper discusses connectivity between neuromorphic chips, which use the timing of fixed-height fixed-width pulses to encode information. Address-events (log2 (N)-bit packets that uniquely identify one of N neurons) are used to transmit these pulses in real time on a random-access time-multiplexed communication channel. Activity is assumed to consist of neuronal ensembles--spikes clustered in space and in time. This paper quantifies tradeoffs faced in allocating bandwidth, granting access, and queuing, as well as throughput requirements, and concludes that an arbitered channel design is the best choice.The arbitered channel is implemented with a formal design methodology for asynchronous digital VLSI CMOS systems, after introducing the reader to this top-down synthesis technique. Following the evolution of three generations of designs, it is shown how the overhead of arbitrating, and encoding and decoding, can be reduced in area (from N to √N) by organizing neurons into rows and columns, and reduced in time (from log2 (N) to 2) by exploiting locality in the arbiter tree and in the row–column architecture, and clustered activity. Throughput is boosted by pipelining and by reading spikes in parallel. Simple techniques that reduce crosstalk in these mixed analog–digital systems are described

A burst-mode word-serial address-event link--II: receiver design

We present a receiver for a scalable multiple-access inter-chip link that communicates binary activity between two-dimensional arrays fabricated in deep submicron CMOS. Recipients are identified by row and column addresses but these addresses are not communicated simultaneously. The row address is followed sequentially by a column address for each active cell in that row; this cuts pad count in half without sacrificing communication capacity. Column addresses are decoded as they are received but cells are not written individually. An entire burst is written to a row in parallel; this increases communication capacity with integration density. Rows are written one by one but bursts are not processed one at a time. The next burst is decoded while the last one is being written; this increases capacity further. We synthesized an asynchronous implementation by performing a series of program decompositions, starting from a high-level description. Links using this design have been implemented successfully in three generations of submicron CMOS technology

A Silicon Retina that Reproduces Signals in the Optic Nerve

Prosthetic devices may someday be used to treat lesions of the central nervous system. Similar to neural circuits, these prosthetic devices should adapt their properties over time, independent of external control. Here we describe an artificial retina, constructed in silicon using single-transistor synaptic primitives, with two forms of locally controlled adaptation: luminance adaptation and contrast gain control. Both forms of adaptation rely on local modulation of synaptic strength, thus meeting the criteria of internal control. Our device is the first to reproduce the responses of the four major ganglion cell types that drive visual cortex, producing 3600 spiking outputs in total. We demonstrate how the responses of our device’s ganglion cells compare to those measured from the mammalian retina. Replicating the retina’s synaptic organization in our chip made it possible to perform these computations using a hundred times less energy than a microprocessor—and to match the mammalian retina in size and weight. With this level of efficiency and autonomy, it is now possible to develop fully implantable intraocular prostheses

A linear cochlear model with active bi-directional coupling

We present a linear active cochlear model that includes the outer hair cell (OHC) forces, which are delivered onto upstream and downstream basilar membrane (BM) segments through Deiters\u27 cells (DCs) and their phalangeal processes (PhPs). Due to the longitudinal tilt of the OHC towards the base and the oblique orientation of the PhP towards the apex, each BM segment receives both feed-forward and feed-backward OHC forces. Transverse BM fibers are actively coupled longitudinally through these bi-directional OHC forces, included in a cochlear model for the first time. We present simulation results that demonstrate large amplification and sharp tuning, and we analyze the underlying mechanism

A recurrent model of orientation maps with simple and complex cells

We describe a neuromorphic chip that utilizes transistor heterogeneity, introduced by the fabrication process, to generate orientation maps similar to those imaged in vivo. Our model consists of a recurrent network of excitatory and inhibitory cells in parallel with a push-pull stage. Similar to a previous model the recurrent network displays hotspots of activity that give rise to visual feature maps. Unlike previous work, however, the map for orientation does not depend on the sign of contrast. Instead, sign-independent cells driven by both ON and OFF channels anchor the map, while push-pull interactions give rise to sign-preserving cells. These two groups of orientation-selective cells are similar to complex and simple cells observed in V1

Optic Nerve Signals in a Neuromorphic Chip I: Outer and Inner Retina Models

We present a novel model for the mammalian retina and analyze its behavior. Our outer retina model performs bandpass spatiotemporal filtering. It is comprised of two reciprocally connected resistive grids that model the cone and horizontal cell syncytia. We show analytically that its sensitivity is proportional to the space-constant-ratio of the two grids while its half-max response is set by the local average intensity. Thus, this outer retina model realizes luminance adaptation. Our inner retina model performs high-pass temporal filtering. It features slow negative feedback whose strength is modulated by a locally computed measure of temporal contrast, modeling two kinds of amacrine cells, one narrow-field, the other wide-field.We show analytically that, when the input is spectrally pure, the corner-frequency tracks the input frequency. But when the input is broadband, the corner frequency is proportional to contrast. Thus, this inner retina model realizes temporal frequency adaptation as well as contrast gain control.We present CMOS circuit designs for our retina model in this paper as well. Experimental measurements from the fabricated chip, and validation of our analytical results, are presented in the companion paper [Zaghloul and Boahen (2004)]

A retinomorphic chip with parallel pathways : encoding INCREASING, ON, DECREASING, and OFF visual signals

Retinomorphic chips may improve their spike-coding efficiency by emulating the primate retina\u27s parallel pathways. To model the four predominant ganglion-cell types in the cat retina, I morphed outer and inner retina microcircuits into a silicon chip, Visio1. It has 104 x 96 photoreceptors, 4 x 52 x 48 ganglion-cells, a die size of 9.25 x 9.67 mm2 in 1.2 µm 5V CMOS, and consumes 11.5 mW at 5 spikes/second/ganglion-cell. Visio1 includes novel subthreshold current-mode circuits that model horizontal-cell autofeedback, to decouple spatial filtering from local gain control, and model amacrine-cell loop-gain modulation, to adapt temporal filtering to motion. Different ganglion cells respond to motion in a quadrature sequence, making it possible to detect edges of one contrast or the other moving in one direction or the other. I present results from a multichip 2-D motion system, which implements Watson and Ahumada\u27s model of human visual-motion sensing

Recurrently Connected Silicon Neurons with Active Dendrites for One-Shot Learning

We describe a neuromorphic chip designed to model active dendrites, recurrent connectivity, and plastic synapses to support one-shot learning. Specifically, it is designed to capture neural firing patterns (short-term memory), memorize individual patterns (long-term memory), and retrive them when primed (associative recall). It consists of a recurrently connected population of excitatory pyramidal cells and a recurrently connected population of inhibitory basket cells. In addition to their recurrent connections, the excitatory and inhibitory populations are reciprocally connected. The model is novel in that it utilizes recurrent connections and active dendrites to maintain short-term memories as well as to store long-term memories

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

A silicon implementation of the thalamic low threshold calcium current

A silicon model of the thalamic low threshold calcium current is presented. The channel current (IT) is the product of an activation and inactivation current, normalized by their sum. The individual currents are modeled by a simple current-mirror integrator circuit. A modified differential pair controls the threshold of activation while a leak transistor added to the inactivation mirror controls the rate of inactivation and deinactivation. The dynamics of IT are the result of the interaction between the fast activation and slow inactivation currents. By adjusting the base level of the activation current, we can realize a hyperpolarization activated cation current (Ih), responsible for rhythmic bursting in thalamic cells. By attaching the circuit to a constant leak integrate-and-fire neuron, we demonstrate in silicon both burst and tonic firing modes