Analog VLSI and neural systems
Analog VLSI and neural systems
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
Smoothing methods for convex inequalities and linear complementarity problems
Mathematical Programming: Series A and B
Dynamics of the firing probability of noisy integrate-and-fire neurons
Neural Computation
Rate models for conductance-based cortical neuronal networks
Neural Computation
Solving unsymmetric sparse systems of linear equations with PARDISO
Future Generation Computer Systems - Special issue: Selected numerical algorithms
Mathematical Programming: Series A and B
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
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 winner-take-all mechanism based on presynaptic inhibition feedback
Neural Computation
A systematic method for configuring vlsi networks of spiking neurons
Neural Computation
Dynamical estimation of neuron and network properties I: variational methods
Biological Cybernetics
IEEE Transactions on Neural Networks
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Neuroscientists often propose detailed computational models to probe the properties of the neural systems they study. With the advent of neuromorphic engineering, there is an increasing number of hardware electronic analogs of biological neural systems being proposed as well. However, for both biological and hardware systems, it is often difficult to estimate the parameters of the model so that they are meaningful to the experimental system under study, especially when these models involve a large number of states and parameters that cannot be simultaneously measured. We have developed a procedure to solve this problem in the context of interacting neural populations using a recently developed dynamic state and parameter estimation (DSPE) technique. This technique uses synchronization as a tool for dynamically coupling experimentally measured data to its corresponding model to determine its parameters and internal state variables. Typically experimental data are obtained from the biological neural system and the model is simulated in software; here we show that this technique is also efficient in validating proposed network models for neuromorphic spike-based very large-scale integration (VLSI) chips and that it is able to systematically extract network parameters such as synaptic weights, time constants, and other variables that are not accessible by direct observation. Our results suggest that this method can become a very useful tool for model-based identification and configuration of neuromorphic multichip VLSI systems.