A FPGA real-time model of single and multiple visual cortex neurons

Li, Guangxing; Talebi, Vargha; Yoonessi, Ahmad; Baker, Curtis L.

doi:10.1016/j.jneumeth.2010.07.031

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…A hardware FPGA model of a simple type visual cortex neuron (Li et al, 2010) was used to test the data acquisition system, stimulus presentation software, and data analysis programs. Responses collected from the FPGA model verified that our system identification software could correctly yield the spatial RF and the temporal latency.…”

Section: Methodsmentioning

confidence: 99%

Natural versus Synthetic Stimuli for Estimating Receptive Field Models: A Comparison of Predictive Robustness

Talebi

Baker

2012

J. Neurosci.

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An ultimate goal of visual neuroscience is to understand the neural encoding of complex, everyday scenes. Yet most of our knowledge of neuronal receptive fields has come from studies using simple artificial stimuli (e.g., bars, gratings) that may fail to reveal the full nature of a neuron's actual response properties. Our goal was to compare the utility of artificial and natural stimuli for estimating receptive field (RF) models. Using extracellular recordings from simple type cells in cat A18, we acquired responses to three types of broadband stimulus ensembles: two widely used artificial patterns (white noise and short bars), and natural images. We used a primary dataset to estimate the spatiotemporal receptive field (STRF) with two hold-back datasets for regularization and validation. STRFs were estimated using an iterative regression algorithm with regularization and subsequently fit with a zero-memory nonlinearity. Each RF model (STRF and zero-memory nonlinearity) was then used in simulations to predict responses to the same stimulus type used to estimate it, as well as to other broadband stimuli and sinewave gratings. White noise stimuli often elicited poor responses leading to noisy RF estimates, while short bars and natural image stimuli were more successful in driving A18 neurons and producing clear RF estimates with strong predictive ability. Natural image-derived RF models were the most robust at predicting responses to other broadband stimulus ensembles that were not used in their estimation and also provided good predictions of tuning curves for sinewave gratings.

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Section: Methodsmentioning

confidence: 99%

Natural versus Synthetic Stimuli for Estimating Receptive Field Models: A Comparison of Predictive Robustness

Talebi

Baker

2012

J. Neurosci.

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“…Mishra et al [48] identified in their survey that many of SNN usually have about 10 4 ∼ 10 8 neurons and 10 10 ∼ 10 14 synapses and that high-performance neural hardware is essential for practical application. Li et al [49] proposed the implementation of visual cortex neurons on FPGAs. The implemented visual cortex neurons exhibited the same dynamics as those recorded from real neurons using multi-electrodes arrays.…”

Section: Background Researchmentioning

confidence: 99%

NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector

Machado¹,

Ferreira²,

Οικονόμου³

et al. 2021

Preprint

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“…One approach that provides a similar level of functionality to our approach is to use an optical front-end and FPGA as a combined system (Li et al 2010). The authors exhibit biologically realistic simple-cell-like response properties, including highly modulated Poisson spike trains, orientation selectivity, spatial/temporal frequency selectivity, and space-time receptive fields using FPGA platform.…”

Section: Related Workmentioning

confidence: 99%

A case for spiking neural network simulation based on configurable multiple-FPGA systems

Yang

2011

Cogn Neurodyn

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Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.

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A FPGA real-time model of single and multiple visual cortex neurons

Cited by 11 publications

References 14 publications

Natural versus Synthetic Stimuli for Estimating Receptive Field Models: A Comparison of Predictive Robustness

Natural versus Synthetic Stimuli for Estimating Receptive Field Models: A Comparison of Predictive Robustness

NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector

A case for spiking neural network simulation based on configurable multiple-FPGA systems

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