Notes
A collection of fragments of understanding in the pursuit of deeper questions.
Contents
- Computational Vision
- Introduction to Data Science
- Translational Neuromodeling
- Deep Learning in Artificial & Biological Neuronal Networks
- Introduction to Neuroinformatics
- Intro to Neuroscience
- Systems Neuroscience
- Readings in Neuroinformatics
- Neurobiology of Consciousness
- Econometrics
- Principles of Finance
- Big Data & Databases
- Game Theory
- Intro to Blockchain
- Marketing Analytics
- Financial Accounting
- Fundamentals of Computer Science
- Foundamentals of Information Technology Law
- Statistics
- Computer Programming
- Macroeconomics
- EU Information Law & Data Economy
- Advanced Mathematics & Statistics
- Principles of Management
- Mathematics
- Microeconomics
Deep Learning with "Time to First Spike"
Deep Learning with "Time to First Spike" An Artificial Spiking Neural Network has been trained to utilize the time to first spike scheme. The (MNIST) 2D image information was encoded as shown in the picture. "Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function", this paper modelled spikes via the Alpha Function. And it can be trained to solve Boolean tasks (AND, OR, XOR), as well as MNIST classification. The alpha synaptic function is a mathematical model that describes the dynamics of synaptic neurotransmitter release. It is characterized by a time constant and a maximum conductance, and it can be used to model the behavior of different types of synapses. In the context of learning with backpropagation, the alpha synaptic function can be used to improve the accuracy of the network by allowing for more precise control of the timing of spikes. By using the alpha synaptic function, the network can more accurately represent temporal patterns in the input data and perform temporal coding.
