Computational Neuroscience

Go to Course: https://www.coursera.org/learn/computational-neuroscience

Introduction

## Course Review: Computational Neuroscience on Coursera ### Overview Computational neuroscience is an exciting and dynamic field that combines elements of neuroscience, mathematics, and computer science to understand how the nervous system processes information. The **Computational Neuroscience** course offered on Coursera provides a comprehensive introduction to the computational methods used to study brain function. From sensory perception to learning and memory, this course navigates the intricate landscape of neural systems and offers valuable insights into how our brains operate. ### Course Structure The course is structured in a series of modules, each focusing on different aspects of computational neuroscience. Below, I outline the modules and their highlights: #### 1. **Introduction & Basic Neurobiology (Rajesh Rao)** This foundational module introduces students to the basics of computational neuroscience and essential principles of neurobiology. It sets the stage for more advanced topics, ensuring that learners have the necessary background to delve deeper into subsequent modules. #### 2. **What do Neurons Encode? Neural Encoding Models (Adrienne Fairhall)** This module provides an engaging examination of how neurons encode information. Students explore the technologies used for recording brain activity and develop mathematical models to characterize neuronal spikes as a form of code. The discussion on variability and noise is particularly relevant for those interested in real-world applications. #### 3. **Extracting Information from Neurons: Neural Decoding (Adrienne Fairhall)** Focusing on neural decoding, this module answers the question of how we can understand what the brain is processing based solely on neural activity. The inclusion of a guest lecture by renowned computational neuroscientist Fred Rieke enriches this module’s content. #### 4. **Information Theory & Neural Coding (Adrienne Fairhall)** Here, students delve into the connections between information theory and brain function. This module is a must for those who want to appreciate the quantitative aspects of how the brain encodes and processes information. #### 5. **Computing in Carbon (Adrienne Fairhall)** Students explore biophysical models, including the famed Hodgkin-Huxley model, which describes action potential generation in neurons. This module provides essential knowledge for understanding the biological underpinnings of neuron behavior. #### 6. **Computing with Networks (Rajesh Rao)** This module covers complex network models of interconnected neurons. Insights into synaptic connections and network dynamics illuminate how neurons communicate and process information as a system. #### 7. **Networks that Learn: Plasticity in the Brain & Learning (Rajesh Rao)** In this module, students examine models of synaptic plasticity, Hebbian learning, and advanced theories of brain function, including predictive coding. It is vital for understanding how learning occurs in the neural context. #### 8. **Learning from Supervision and Rewards (Rajesh Rao)** The final module explores concepts of supervised learning and reinforcement learning. Students will learn practical applications, including how to predict rewards and optimize decision-making using computational models reflective of brain functions. ### Practical Applications Throughout the course, students will have opportunities to engage with tools like MATLAB, Octave, and Python, which are essential for modeling and simulating computational neuroscience concepts. The hands-on aspect of the course ensures that theoretical knowledge is complemented by practical learning. ### Course Recommendations - **Target Audience**: This course is ideal for students and professionals with a background in biology, computer science, or mathematics who are interested in the interdisciplinary field of computational neuroscience. - **Prerequisites**: While a basic understanding of biology and mathematics is beneficial, those with a strong interest and willingness to learn will find the material accessible. - **Engagement**: The mix of theoretical lectures, practical applications, and guest lectures ensures a rich learning experience that keeps students engaged and motivated. ### Conclusion The **Computational Neuroscience** course on Coursera is a brilliant learning opportunity for anyone fascinated by how the brain works. Its comprehensive syllabus and diverse instructional methods make it a valuable addition to any learner's academic journey. Whether you intend to pursue a career in neuroscience, machine learning, or bioengineering, this course lays a strong foundation for understanding the computational aspects of neuroscience. I highly recommend enrolling in this course for anyone looking to deepen their understanding of the brain and its computational processes.

Syllabus

Introduction & Basic Neurobiology (Rajesh Rao)

This module includes an Introduction to Computational Neuroscience, along with a primer on Basic Neurobiology.

This module introduces you to the captivating world of neural information coding. You will learn about the technologies that are used to record brain activity. We will then develop some mathematical formulations that allow us to characterize spikes from neurons as a code, at increasing levels of detail. Finally we investigate variability and noise in the brain, and how our models can accommodate them.

In this module, we turn the question of neural encoding around and ask: can we estimate what the brain is seeing, intending, or experiencing just from its neural activity? This is the problem of neural decoding and it is playing an increasingly important role in applications such as neuroprosthetics and brain-computer interfaces, where the interface must decode a person's movement intentions from neural activity. As a bonus for this module, you get to enjoy a guest lecture by well-known computational neuroscientist Fred Rieke.

This module will unravel the intimate connections between the venerable field of information theory and that equally venerable object called our brain.

This module takes you into the world of biophysics of neurons, where you will meet one of the most famous mathematical models in neuroscience, the Hodgkin-Huxley model of action potential (spike) generation. We will also delve into other models of neurons and learn how to model a neuron's structure, including those intricate branches called dendrites.

This module explores how models of neurons can be connected to create network models. The first lecture shows you how to model those remarkable connections between neurons called synapses. This lecture will leave you in the company of a simple network of integrate-and-fire neurons which follow each other or dance in synchrony. In the second lecture, you will learn about firing rate models and feedforward networks, which transform their inputs to outputs in a single "feedforward" pass. The last lecture takes you to the dynamic world of recurrent networks, which use feedback between neurons for amplification, memory, attention, oscillations, and more!

This module investigates models of synaptic plasticity and learning in the brain, including a Canadian psychologist's prescient prescription for how neurons ought to learn (Hebbian learning) and the revelation that brains can do statistics (even if we ourselves sometimes cannot)! The next two lectures explore unsupervised learning and theories of brain function based on sparse coding and predictive coding.

In this last module, we explore supervised learning and reinforcement learning. The first lecture introduces you to supervised learning with the help of famous faces from politics and Bollywood, casts neurons as classifiers, and gives you a taste of that bedrock of supervised learning, backpropagation, with whose help you will learn to back a truck into a loading dock.The second and third lectures focus on reinforcement learning. The second lecture will teach you how to predict rewards à la Pavlov's dog and will explore the connection to that important reward-related chemical in our brains: dopamine. In the third lecture, we will learn how to select the best actions for maximizing rewards, and examine a possible neural implementation of our computational model in the brain region known as the basal ganglia. The grand finale: flying a helicopter using reinforcement learning!