The probability of a hypothesis, given data.
Why math is useful in data science.
Why statistics are useful for data science.
Statistics is exploring data.
Graphical data exploration
Numerical data exploration
Simple description of statistical data.
Basics of hypothesis testing.
In this lecture, the speaker demonstrates Neurokernel's module interfacing feature by using it to integrate independently developed models of olfactory and vision LPUs based upon experimentally obtained connectivity information.
This lecture covers an introduction to connectomics, and image processing tools for the study of connectomics.
This lecture covers acquisition techniques, the physics of MRI, diffusion imaging, prediction using fMRI.
This lecture will provide an overview of neuroimaging techniques and their clinical applications.
Optical imaging offers a look inside the working brain. This lecture takes a look at orientation and ocular dominance columns in the visual cortex, and shows how they can be viewed with calcium imaging.
Functional imaging has led to the discovery of a plethora of visual cortical regions. This lecture introduces functional imaging techniques and their teachings about the visual cortex.
Investigating the structure of synapses with electron microscopy.
This lecture covers modeling the neuron in silicon, modeling vision and audition and sensory fusion using a deep network.
Presentation of a simulation software for spatial model neurons and their networks designed primarily for GPUs.
Presentation of past and present neurocomputing approaches and hybrid analog/digital circuits that directly emulate the properties of neurons and synapses.
Presentation of the Brian neural simulator, where models are defined directly by their mathematical equations and code is automatically generated for each specific target.
The lecture covers a brief introduction to neuromorphic engineering, some of the neuromorphic networks that the speaker has developed, and their potential applications, particularly in machine learning.