This video gives a brief introduction to Neuro4ML's lessons on neuromorphic computing - the use of specialized hardware which either directly mimics brain function or is inspired by some aspect of the way the brain computes.
In this lesson, you will learn in more detail about neuromorphic computing, that is, non-standard computational architectures that mimic some aspect of the way the brain works.
This video provides a very quick introduction to some of the neuromorphic sensing devices, and how they offer unique, low-power applications.
This lesson presents a simulation software for spatial model neurons and their networks designed primarily for GPUs.
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.
This lecture covers computational principles that growth cones employ to detect and respond to environmental chemotactic gradients, focusing particularly on growth-cone shape dynamics.
In this lecture you will learn that in developing mouse somatosensory cortex, endogenous Btbd3 translocate to the cell nucleus in response to neuronal activity and oriente primary dendrites toward active axons in the barrel hollow.
In this presentation, the speaker describes some of their recent efforts to characterize the transcriptome of the developing human brain, and and introduction to the BrainSpan project.
This lesson provides an overview of the current status in the field of neuroscientific ontologies, presenting examples of data organization and standards, particularly from neuroimaging and electrophysiology.
Following the previous lesson on neuronal structure, this lesson discusses neuronal function, particularly focusing on spike triggering and propogation.
This lesson introduces the practical exercises which accompany the previous lessons on animal and human connectomes in the brain and nervous system.
This lesson discusses a gripping neuroscientific question: why have neurons developed the discrete action potential, or spike, as a principle method of communication?
This lecture covers NeuronUnit, a library that builds upon SciUnit and integrates with several existing neuroinformatics resources to support validating single-neuron models using data gathered by neurophysiologists.
This lesson provides an introduction to the NeuroElectro project, which aims to organize information on cellular neurophysiology.
This lesson covers simultaneously recorded neurons in non-human primates coordinate their spiking activity in a sequential manner that mirrors the dominant wave propagation directions of the local field potentials.
This talk covers statistical analysis of spike train data, the modeling approach GLM, and the problem of assessing neural synchrony.
This talk covers statistical methods for characterizing neural population responses and extracting structure from high-dimensional neural data.
This presentation discusses research aimed at understanding the activity of single neurons and populations of neurons in the visual system.
Learn how to create a standard extracellular electrophysiology dataset in NWB using Python.
Learn how to create a standard calcium imaging dataset in NWB using Python.