Dynamic Pursuit with a Bio-inspired Neural Model

Sánchez, Claudio Castellanos; Girau, Bernard

doi:10.1007/11558484_36

Cited by 3 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To give a possible future application of the sparse implementation, local motion detectors (MT cells in the visual cortex) could be combined in a single space, where units would share the two spatial dimensions of the retina, one dimension for the direction of movement and a forth one for the velocity. Such a 4D space would be an alternative to using a large set of maps, each dedicated to a specific direction and speed [9]. By dynamically biasing the activity in a specific part of this space, the model would track extended objects following a determined trajectory.…”

Section: Discussionmentioning

confidence: 99%

Competition in High Dimensional Spaces Using a Sparse Approximation of Neural Fields

Quinton

Girau

Lefort

2011

Advances in Experimental Medicine and Biology

Self Cite

View full text Add to dashboard Cite

The Continuum Neural Field Theory implements competition within topologically organized neural networks with lateral inhibitory connections. However, due to the polynomial complexity of matrix-based implementations, updating dense representations of the activity becomes computationally intractable when an adaptive resolution or an arbitrary number of input dimensions is required. This paper proposes an alternative to self-organizing maps with a sparse implementation based on Gaussian mixture models, promoting a trade-off in redundancy for higher computational efficiency and alleviating constraints on the underlying substrate.This version reproduces the emergent attentional properties of the original equations, by directly applying them within a continuous approximation of a high dimensional neural field. The model is compatible with preprocessed sensory flows but can also be interfaced with artificial systems. This is particularly important for sensorimotor systems, where decisions and motor actions must be taken and updated in real-time. Preliminary tests are performed on a reactive color tracking application, using spatially distributed color features.

show abstract

Section: Discussionmentioning

confidence: 99%

Competition in High Dimensional Spaces Using a Sparse Approximation of Neural Fields

Quinton

Girau

Lefort

2011

Advances in Experimental Medicine and Biology

Self Cite

View full text Add to dashboard Cite

show abstract

A Bio-inspired Connectionist Approach for Motion Description Through Sequences of Images

Castellanos-Sanchez

2007

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Randomly Spiking Dynamic Neural Fields

Vangel

Torres-Huitzil

Girau

2015

J. Emerg. Technol. Comput. Syst.

Self Cite

View full text Add to dashboard Cite

Bio-inspired neural computation attracts a lot of attention as a possible solution for the future challenges in designing computational resources. Dynamic neural fields (DNF) provide cortically inspired models of neural populations to which computation can be applied for a wide variety of tasks, such as perception and sensorimotor control. DNFs are often derived from continuous neural field theory (CNFT). In spite of the parallel structure and regularity of CNFT models, few studies of hardware implementations have been carried out targeting embedded real-time processing. In this article, a hardware-friendly model adapted from the CNFT is introduced, namely the RSDNF model (randomly spiking dynamic neural fields). Thanks to their simplified 2D structure, RSDNFs achieve scalable parallel implementations on digital hardware while maintaining the behavioral properties of CNFT models. Spike-based computations within neurons in the field are introduced to reduce interneuron connection bandwidth. Additionally, local stochastic spike propagation ensures inhibition and excitation broadcast without a fully connected network. The behavioral soundness and robustness of the model in the presence of noise and distracters is fully validated through software and hardware. A field programmable gate array (FPGA) implementation shows how the RSDNF model ensures a level of density and scalability out of reach for previous hardware implementations of dynamic neural field models.

show abstract

Dynamic Pursuit with a Bio-inspired Neural Model

Cited by 3 publications

References 6 publications

Competition in High Dimensional Spaces Using a Sparse Approximation of Neural Fields

Competition in High Dimensional Spaces Using a Sparse Approximation of Neural Fields

A Bio-inspired Connectionist Approach for Motion Description Through Sequences of Images

Randomly Spiking Dynamic Neural Fields

Contact Info

Product

Resources

About