Analog VLSI and neural systems
Analog VLSI and neural systems
Computational neuroscience
Reduction of conductance-based models with slow synapses to neural nets
Neural Computation
Characterization of subthreshold MOS mismatch in transistors for VLSI systems
Journal of VLSI Signal Processing Systems - Joint special issue on Analog VLSI computation; also see Analog Integrated Circuits Signal Process., Vol. 6, No. 1
The NEURON simulation environment
Neural Computation
The book of GENESIS (2nd ed.): exploring realistic neural models with the GEneral NEural SImulation System
Analog Integrated Circuits and Signal Processing
Dynamics of the firing probability of noisy integrate-and-fire neurons
Neural Computation
Permitted and forbidden sets in symmetric threshold-linear networks
Neural Computation
Rate models for conductance-based cortical neuronal networks
Neural Computation
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Synaptic Dynamics in Analog VLSI
Neural Computation
A Neuromorphic aVLSI network chip with configurable plastic synapses
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
A winner-take-all mechanism based on presynaptic inhibition feedback
Neural Computation
State-dependent computation using coupled recurrent networks
Neural Computation
Belief propagation in networks of spiking neurons
Neural Computation
IEEE Transactions on Neural Networks
Synchrony in Silicon: The Gamma Rhythm
IEEE Transactions on Neural Networks
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An increasing number of research groups are developing custom hybrid analog/digital very large scale integration (VLSI) chips and systems that implement hundreds to thousands of spiking neurons with biophysically realistic dynamics, with the intention of emulating brainlike real-world behavior in hardware and robotic systems rather than simply simulating their performance on general-purpose digital computers. Although the electronic engineering aspects of these emulation systems is proceeding well, progress toward the actual emulation of brainlike tasks is restricted by the lack of suitable high-level configuration methods of the kind that have already been developed over many decades for simulations on general-purpose computers. The key difficulty is that the dynamics of the CMOS electronic analogs are determined by transistor biases that do not map simply to the parameter types and values used in typical abstract mathematical models of neurons and their networks. Here we provide a general method for resolving this difficulty. We describe a parameter mapping technique that permits an automatic configuration of VLSI neural networks so that their electronic emulation conforms to a higher-level neuronal simulation. We show that the neurons configured by our method exhibit spike timing statistics and temporal dynamics that are the same as those observed in the software simulated neurons and, in particular, that the key parameters of recurrent VLSI neural networks (e.g., implementing soft winner-take-all) can be precisely tuned. The proposed method permits a seamless integration between software simulations with hardware emulations and intertranslatability between the parameters of abstract neuronal models and their emulation counterparts. Most important, our method offers a route toward a high-level task configuration language for neuromorphic VLSI systems.