Dynamic Computation in a Recurrent Network of Heterogeneous Silicon Neurons

We describe a neuromorphic chip with a two-layer excitatory-inhibitory recurrent network of that exhibits localized clusters of neural activity. Unlike other recurrent networks, the clusters in our network are pinned to certain locations due to transistor mismatch introduced in fabrication. As described in previous work, our pinned clusters respond selectively to oriented stimuli and the neurons\u27 preferred orientations are distributed similar to the visual cortex. Here we show that orientation computation is rapid when activity alternates between layers (staccato-like), dislodging pinned clusters, which promotes fast cluster diffusion

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

Gibbs Sampling with Low-Power Spiking Digital Neurons

Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in a wide variety of applications including image classification and speech recognition. Inference and learning in these algorithms uses a Markov Chain Monte Carlo procedure called Gibbs sampling. A sigmoidal function forms the kernel of this sampler which can be realized from the firing statistics of noisy integrate-and-fire neurons on a neuromorphic VLSI substrate. This paper demonstrates such an implementation on an array of digital spiking neurons with stochastic leak and threshold properties for inference tasks and presents some key performance metrics for such a hardware-based sampler in both the generative and discriminative contexts.Comment: Accepted at ISCAS 201

Neuromorphic Implementation of Orientation Hypercolumns

Neurons in the mammalian primary visual cortex are selective along multiple stimulus dimensions, including retinal position, spatial frequency, and orientation. Neurons tuned to different stimulus features but the same retinal position are grouped into retinotopic arrays of hypercolumns. This paper describes a neuromorphic implementation of orientation hypercolumns, which consists of a single silicon retina feeding multiple chips, each of which contains an array of neurons tuned to the same orientation and spatial frequency, but different retinal locations. All chips operate in continuous time, and communicate with each other using spikes transmitted by the address-event representation protocol. This system is modular in the sense that orientation coverage can be increased simply by adding more chips, and expandable in the sense that its output can be used to construct neurons tuned to other stimulus dimensions. We present measured results from the system, demonstrating neuronal selectivity along position, spatial frequency and orientation. We also demonstrate that the system supports recurrent feedback between neurons within one hypercolumn, even though they reside on different chips. The measured results from the system are in excellent concordance with theoretical predictions

Implementation of Olfactory Bulb Glomerular-Layer Computations in a Digital Neurosynaptic Core

We present a biomimetic system that captures essential functional properties of the glomerular layer of the mammalian olfactory bulb, specifically including its capacity to decorrelate similar odor representations without foreknowledge of the statistical distributions of analyte features. Our system is based on a digital neuromorphic chip consisting of 256 leaky-integrate-and-fire neurons, 1024 × 256 crossbar synapses, and address-event representation communication circuits. The neural circuits configured in the chip reflect established connections among mitral cells, periglomerular cells, external tufted cells, and superficial short-axon cells within the olfactory bulb, and accept input from convergent sets of sensors configured as olfactory sensory neurons. This configuration generates functional transformations comparable to those observed in the glomerular layer of the mammalian olfactory bulb. Our circuits, consuming only 45 pJ of active power per spike with a power supply of 0.85 V, can be used as the first stage of processing in low-power artificial chemical sensing devices inspired by natural olfactory systems