Linear Filters

2011 ◽  
pp. 77-96
Keyword(s):  
Linear Filters

PARALLEL, LOCALLY CONNECTED ALGORITHMS FOR NONLINEAR ADAPTIVE FILTERING

1993 ◽  
Vol 04 (01) ◽  
pp. 85-98 ◽  
Author(s):  
HASSAN M. AHMED ◽  
FAWAD RAUF
Keyword(s):  
Adaptive Filtering ◽  
Layered Structure ◽  
Adaptive Filters ◽  
Computationally Efficient ◽  
Local Connectivity ◽  
Linear Filters ◽  
New Approach ◽  
Vlsi Layout ◽  
State Dependent ◽  
Nonlinear Adaptive Filtering

A new adaptive modular realization for nonlinear filters is presented whereby construction is both computationally efficient and readily implemented. The proposed layered structure consists of locally connected, locally adapted linear filters. Modularity and local connectivity make efficient VLSI layout easy and amenable to automation. The layered structure is based on "state dependent embedding", a new approach to the design of series based nonlinear adaptive filters.

Surrogate Population Models for Large-Scale Neural Simulations

2015 ◽  
Vol 27 (6) ◽  
pp. 1186-1222 ◽  
Author(s):  
Bryan P. Tripp
Keyword(s):  
Surrogate Model ◽  
Latent Variables ◽  
Large Scale ◽  
Neural Model ◽  
Surrogate Models ◽  
Weighted Sums ◽  
Neural Systems ◽  
Neuron Models ◽  
Linear Filters ◽  
Population Activity

Because different parts of the brain have rich interconnections, it is not possible to model small parts realistically in isolation. However, it is also impractical to simulate large neural systems in detail. This article outlines a new approach to multiscale modeling of neural systems that involves constructing efficient surrogate models of populations. Given a population of neuron models with correlated activity and with specific, nonrandom connections, a surrogate model is constructed in order to approximate the aggregate outputs of the population. The surrogate model requires less computation than the neural model, but it has a clear and specific relationship with the neural model. For example, approximate spike rasters for specific neurons can be derived from a simulation of the surrogate model. This article deals specifically with neural engineering framework (NEF) circuits of leaky-integrate-and-fire point neurons. Weighted sums of spikes are modeled by interpolating over latent variables in the population activity, and linear filters operate on gaussian random variables to approximate spike-related fluctuations. It is found that the surrogate models can often closely approximate network behavior with orders-of-magnitude reduction in computational demands, although there are certain systematic differences between the spiking and surrogate models. Since individual spikes are not modeled, some simulations can be performed with much longer steps sizes (e.g., 20 ms). Possible extensions to non-NEF networks and to more complex neuron models are discussed.

Sign in / Sign up

Export Citation Format

Share Document