Massively parallel neural circuits for stereoscopic color vision: Encoding, decoding and identification

Lazar, Aurel A.; Slutskiy, Yevgeniy B; Zhou, Yiyin

doi:10.1016/j.neunet.2014.10.014

Cited by 7 publications

(6 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lazar and Slutskiy (2014a) have applied the mCIM method to particular types of inputs, namely spatial u(x, y), spectrotemporal u(ν, t) and spatiotemporal functions u(x, y, t). Lazar et al (2015) have introduced a particular type of CIM called colour video CIM, where the circuit input is a colour visual stimulus modelled as a vector-valued function u(x, y, t) = [u 1 (x, y, t), u 2 (x, y, t), u 3 (x, y, t)].…”

Section: Identification Methods For Different Circuit Structuresmentioning

confidence: 99%

Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits

Florescu¹

2017

Springer Theses

View full text Add to dashboard Cite

To my family iii The third problem addressed in this work is given by the need of developing neuromorphic engineering circuits that perform mathematical computations in the spike domain.In this respect, this thesis developed a new representation between the time encoded input and output of a linear filter, where the TEM is represented by an ideal IF neuron. A new practical algorithm is developed based on this representation. The proposed algorithm is significantly faster than the alternative approach, which involves reconstructing the input, simulating the linear filter, and subsequently encoding the resulting output into a spike train.

show abstract

Section: Identification Methods For Different Circuit Structuresmentioning

confidence: 99%

Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits

Florescu¹

2017

Springer Theses

View full text Add to dashboard Cite

show abstract

“…Efforts at reverse engineering the brain must ultimately confront the need to validate hypotheses regarding neural information processing against actual biological systems. In order to achieve biological validation of the Neurokernel, the computational modeling of the fruit fly brain must be tightly integrated with increasingly precise electrophysiological techniques and the recorded data evaluated with novel system identification methods [ 10 , 12 , 66 – 70 ]. This will enable direct comparison of the output of models executed by Neurokernel to that of corresponding neurons in the brain regions of interest.…”

Section: Future Developmentmentioning

confidence: 99%

Neurokernel: An Open Source Platform for Emulating the Fruit Fly Brain

Givon

Lazar

2016

PLoS ONE

Self Cite

View full text Add to dashboard Cite

We have developed an open software platform called Neurokernel for collaborative development of comprehensive models of the brain of the fruit fly Drosophila melanogaster and their execution and testing on multiple Graphics Processing Units (GPUs). Neurokernel provides a programming model that capitalizes upon the structural organization of the fly brain into a fixed number of functional modules to distinguish between these modules’ local information processing capabilities and the connectivity patterns that link them. By defining mandatory communication interfaces that specify how data is transmitted between models of each of these modules regardless of their internal design, Neurokernel explicitly enables multiple researchers to collaboratively model the fruit fly’s entire brain by integration of their independently developed models of its constituent processing units. We demonstrate the power of Neurokernel’s model integration by combining independently developed models of the retina and lamina neuropils in the fly’s visual system and by demonstrating their neuroinformation processing capability. We also illustrate Neurokernel’s ability to take advantage of direct GPU-to-GPU data transfers with benchmarks that demonstrate scaling of Neurokernel’s communication performance both over the number of interface ports exposed by an emulation’s constituent modules and the total number of modules comprised by an emulation.

show abstract

“…Neural circuits comprising complex cells constitute a highly nonlinear circuit as illustrated in Figure 1. Under the modeling framework of Time Encoding Machines (TEMs) [5,6,7], it has been shown that decoding of stimuli and functional identification of linear receptive fields of simple cells are dual to each other [8,9]. This led to mathematically rigorous identification algorithms for identifying linear receptive fields of simple cells [10].…”

Section: Introductionmentioning

confidence: 99%

“…The sparse duality result also enables us to evaluate the identified circuits in the input space. We achieve the latter by computing the mean square error or signal-to-noise (SNR) of novel stimuli decoded using the identified circuits [8,9].…”

Section: Introductionmentioning

confidence: 99%

Sparse Functional Identification of Complex Cells from Spike Times and the Decoding of Visual Stimuli

2018

Self Cite

View full text Add to dashboard Cite

We investigate the sparse functional identification of complex cells and the decoding of spatio-temporal visual stimuli encoded by an ensemble of complex cells. The reconstruction algorithm is formulated as a rank minimization problem that significantly reduces the number of sampling measurements (spikes) required for decoding. We also establish the duality between sparse decoding and functional identification and provide algorithms for identification of low-rank dendritic stimulus processors. The duality enables us to efficiently evaluate our functional identification algorithms by reconstructing novel stimuli in the input space. Finally, we demonstrate that our identification algorithms substantially outperform the generalized quadratic model, the nonlinear input model, and the widely used spike-triggered covariance algorithm.Electronic Supplementary MaterialThe online version of this article (10.1186/s13408-017-0057-1) contains supplementary material.

show abstract

Massively parallel neural circuits for stereoscopic color vision: Encoding, decoding and identification

Cited by 7 publications

References 32 publications

Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits

Reconstruction, Identification and Implementation Methods for Spiking Neural Circuits

Neurokernel: An Open Source Platform for Emulating the Fruit Fly Brain

Sparse Functional Identification of Complex Cells from Spike Times and the Decoding of Visual Stimuli

Contact Info

Product

Resources

About