  The paper proposes a new algorithm called SymBa that aims to achieve more
biologically plausible learning than Back-Propagation (BP). The algorithm is
based on the Forward-Forward (FF) algorithm, which is a BP-free method for
training neural networks. SymBa improves the FF algorithm's convergence
behavior by addressing the problem of asymmetric gradients caused by
conflicting converging directions for positive and negative samples. The
algorithm balances positive and negative losses to enhance performance and
convergence speed. Furthermore, it modifies the FF algorithm by adding
Intrinsic Class Pattern (ICP) containing class information to prevent the loss
of class information during training. The proposed algorithm has the potential
to improve our understanding of how the brain learns and processes information
and to develop more effective and efficient artificial intelligence systems.
The paper presents experimental results that demonstrate the effectiveness of
SymBa algorithm compared to the FF algorithm and BP.
