Finite Impulse Response (FIR) neural network models each synapse as a linear filter to provide dynamic interconnectivity. Temporal backpropagation is used to train the network in which error terms are symmetrically filtered backward through the network

firnet\BUGLIST
......\architecture.m
......\batchbp2.m
......\bp_ffnet2.mexsol
......\bp_firnet2.mexsol
......\bp_firnet3.mexsol
......\bp_ffnet2.mexlx
......\bp_firnet2.mexlx
......\bp_firnet3.mexlx
......\cv_net.m
......\demo1ff2.m
......\ffnet2.mexsol
......\demo1fir2.m
......\firnet2.mexsol
......\firnet3.mexsol
......\demo1fir3.m
......\demo_cv.m
......\ffnet.m
......\ffnet2.mexlx
......\firnet2.mexlx
......\firnet3.mexlx
......\henonN.dat
......\iterate_ffnet2.m
......\jacobX.mexlx
......\jacobXW.m
......\sunspotsR.dat
......\weight_init.m
......\SOURCE
......\......\jacobW.c
......\......\jacobX.c
......\......\bp_ffnet2.c
......\......\bp_firnet2.c
......\......\bp_firnet3.c
......\......\ffnet2.c
......\......\firnet2.c
......\......\firnet3.c
......\MATLAB4
......\.......\demo1ff2.m
......\.......\demo1fir2.m
......\.......\demo1fir3.m
......\.......\demo_cv.m
......\.......\bp_ffnet2.mexsol
......\.......\bp_firnet2.mexsol
......\.......\bp_firnet3.mexsol
......\.......\ffnet2.mexsol
......\.......\firnet2.mexsol
......\.......\firnet3.mexsol
......\.......\architecture.m
......\.......\cv_net.m
......\.......\ffnet.m
......\README.txt
firnet

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