Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Digital integrated circuits: a design perspective
Digital integrated circuits: a design perspective
Speech recognition experiments with silicon auditory models
Neuromorphic systems engineering
Analog versus digital: extrapolating from electronics to neurobiology
Neural Computation
Spikes: exploring the neural code
Spikes: exploring the neural code
An Analog VLSI System for Stereoscopic Vision
An Analog VLSI System for Stereoscopic Vision
Learning on Silicon: Adaptive VLSI Neural Systems
Learning on Silicon: Adaptive VLSI Neural Systems
Feedback Control of Dynamic Systems
Feedback Control of Dynamic Systems
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
Analog-to-digital converter survey and analysis
IEEE Journal on Selected Areas in Communications
System level design language extensions for timed/untimed digital-analog combined system design
GLSVLSI '05 Proceedings of the 15th ACM Great Lakes symposium on VLSI
Using kolmogorov inspired gates for low power nanoelectronics
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Continuous real-world inputs can open up alternative accelerator designs
Proceedings of the 40th Annual International Symposium on Computer Architecture
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We outline a hybrid analog-digital scheme for computing with three important features that enable it to scale to systems of large complexity: First, like digital computation, which uses several one-bit precise logical units to collectively compute a precise answer to a computation, the hybrid scheme uses several moderate-precision analog units to collectively compute a precise answer to a computation. Second, frequent discrete signal restoration of the analog information prevents analog noise and offset from degrading the computation. And, third, a state machine enables complex computations to be created using a sequence of elementary computations. A natural choice for implementing this hybrid scheme is one based on spikes because spike-count codes are digital, while spike-time codes are analog. We illustrate how spikes afford easy ways to implement all three components of scalable hybrid computation. First, as an important example of distributed analog computation, we show how spikes can create a distributed modular representation of an analog number by implementing digital carry interactions between spiking analog neurons. Second, we show how signal restoration may be performed by recursive spike-count quantization of spike-time codes. And, third, we use spikes from an analog dynamical system to trigger state transitions in a digital dynamical system, which reconfigures the analog dynamical system using a binary control vector; such feedback interactions between analog and digital dynamical systems create a hybrid state machine (HSM). The HSM extends and expands the concept of a digital finite-state-machine to the hybrid domain. We present experimental data from a two-neuron HSM on a chip that implements error-correcting analog-to-digital conversion with the concurrent use of spike-time and spike-count codes. We also present experimental data from silicon circuits that implement HSM-based pattern recognition using spike-time synchrony. We outline how HSMs may be used to perform learning, vector quantization, spike pattern recognition and generation, and how they may be reconfigured.