PMID- 16102939
OWN - NLM
STAT- MEDLINE
DCOM- 20051109
LR  - 20050905
IS  - 0893-6080 (Print)
IS  - 0893-6080 (Linking)
VI  - 18
IP  - 5-6
DP  - 2005 Jun-Jul
TI  - On-chip visual perception of motion: a bio-inspired connectionist model on FPGA.
PG  - 557-65
AB  - Visual motion provides useful information to understand the dynamics of a scene 
      to allow intelligent systems interact with their environment. Motion computation 
      is usually restricted by real time requirements that need the design and 
      implementation of specific hardware architectures. In this paper, the design of 
      hardware architecture for a bio-inspired neural model for motion estimation is 
      presented. The motion estimation is based on a strongly localized bio-inspired 
      connectionist model with a particular adaptation of spatio-temporal Gabor-like 
      filtering. The architecture is constituted by three main modules that perform 
      spatial, temporal, and excitatory-inhibitory connectionist processing. The 
      biomimetic architecture is modeled, simulated and validated in VHDL. The 
      synthesis results on a Field Programmable Gate Array (FPGA) device show the 
      potential achievement of real-time performance at an affordable silicon area.
FAU - Torres-Huitzil, Cesar
AU  - Torres-Huitzil C
AD  - LORIA-INRIA Lorraine, Nancy, France. torres@loria.fr
FAU - Girau, Bernard
AU  - Girau B
FAU - Castellanos-Sanchez, Claudio
AU  - Castellanos-Sanchez C
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Neural Netw
JT  - Neural networks : the official journal of the International Neural Network 
      Society
JID - 8805018
SB  - IM
MH  - *Models, Neurological
MH  - Motion Perception/*physiology
MH  - Neural Pathways/physiology
MH  - Space Perception/physiology
MH  - Synapses/physiology
MH  - Visual Cortex/physiology
MH  - Visual Perception/*physiology
EDAT- 2005/08/17 09:00
MHDA- 2005/11/10 09:00
CRDT- 2005/08/17 09:00
PHST- 2005/08/17 09:00 [pubmed]
PHST- 2005/11/10 09:00 [medline]
PHST- 2005/08/17 09:00 [entrez]
AID - S0893-6080(05)00115-2 [pii]
AID - 10.1016/j.neunet.2005.06.029 [doi]
PST - ppublish
SO  - Neural Netw. 2005 Jun-Jul;18(5-6):557-65. doi: 10.1016/j.neunet.2005.06.029.