Cengiz and Jacob will be teaching APMTH 226: Neural Computation in the Fall.

This course introduces advanced mathematical methods and models used in theoretical neuroscience and theory of neural networks. We will explore computations and functions performed by the brain, and how they are implemented by neurons and their networks. We will cover selected topics from deep learning theory; spiking neuron models; population codes; normative theories of sensory representations; models of synaptic plasticity; computing with dynamics in recurrent neural networks; attractor network models of memory and spatial maps; neural models of probabilistic inference in the brain and drift-diffusion models of decision making. Concrete examples of applications of these ideas to the brain will be discussed. Topics at the research frontier will be emphasized.

  Course website: https://canvas.harvard.edu/courses/92774