Examines computational structures inspired by biological neural systems, such as feedforward and recurrent networks.

Analyzes hierarchical layer design — convolutional, pooling, and fully connected — for complex data processing.

Explores Hodgkin–Huxley and integrate-and-fire models representing electrical behavior of real neurons.

Covers the mathematical optimization processes that adjust network weights to minimize prediction error.

Focuses on biologically realistic models that transmit information through time-dependent spikes.

Investigates structural and functional connectomes that define the wiring and activity flow in brain systems.

Merges deep neural computation with symbolic reasoning for explainable intelligence models.

Explores gradient descent, Hebbian learning, and reinforcement strategies shaping adaptive intelligence.

Studies pruning, regularization, and parameter tuning techniques to enhance performance and generalization.

Investigates neuromorphic chips and cognitive architectures mimicking the efficiency of human brain function.