Neural Networks

NS 504 Neural Networks/Neural Informatics

Network or computational neuroscience explores the brain at many different levels, from single cell activity, to small local network computation, to the dynamics of large neuronal populations across the brain. This course will introduce students to a multifaceted array of approaches that span biology, physics, mathematics, computer science and engineering and facilitate the integration of modeling and quantitative techniques to investigate neural activity at these different levels.


The ultimate goal of the course is to exposure students to conceptual frameworks for how electrical activity is organized in the brain, how complex activity underpins behavior and ultimately how resilience/disturbances in these networks can lead to brain health and disorders. Topics to be covered include:


  • Neurons, synapses and circuits: membrane properties, the Nernst potential, derivation of the Hodgkin-Huxley model, action potential generation, action potential propagation, probabilistic models for ion channel gating, synaptic currents, excitatory and inhibitory network dynamics, firing rate models, neural coding.
  • Measuring, Perturbing and analyzing brain networks: Deep brain stimulation, Imaging functional networks with MRI EEG and Non-invasive Brain stimulation.
  • Network Disorders: Parkinson’s Disease; Schizophrenia, Major Depressive Disorder and Autism Spectrum Disorders.


No required textbook. Readings and homework problems are selected from a number of different texts. Course requirements will include homework assignments containing a combination of analytical and numerical-based problems, a longer-term modeling project and an oral presentation of the project to the class at the end of the semester.