Neural network models are potential tools for improving our understanding of complex brain
functions. To address this goal, these models need to be neurobiologically realistic …

Environmental sounds form part of our daily life. With the advancement of deep learning
models and the abundance of training data, the performance of automatic sound …

Different learning modes and mechanisms allow faster and better acquisition of skills as
widely studied in humans and many animals. Specific neurons, called mirror neurons, are …

Humans perform remarkably well for speech recognition using sparse and asynchronous
events carried by electrical impulses. Motivated by the observations that human brains …

We develop a fine-grained image classifier using a general deep convolutional neural
network (DCNN). We improve the fine-grained image classification accuracy of a DCNN …

With the increasing popularity of social media and smart devices, the face as one of the key
biometrics becomes vital for person identification. Among those face recognition algorithms …

Human beings can achieve reliable and fast visual pattern recognition with limited time and
learning samples. Underlying this capability, ventral stream plays an important role in object …

Spiking neural networks (SNNs) with a lattice architecture are introduced in this work,
combining several desirable properties of SNNs and self-organized maps (SOMs). Networks …

Real-time learning needs algorithms operating in a fast speed comparable to human or
animal, however this is a huge challenge in processing visual inputs. Research shows a …

This paper intends to enhance the learning performance of radial basis function neural
network (RBFnn) using self-organizing map (SOM) neural network (SOMnn). In addition, the …