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– Nengo has a GUI and command-line interface.
– It is composed of a unified and well-documented Java library, a Python plugin, and a set of command-line applications.
– It supports both the NEF and the NEURON simulators.
– Nengo makes it easy for beginners and experts to develop models using NEF, and provides many utilities for running and testing these models.
Nengo uses a GUI that is tightly coupled to the NEF, so that the user can interactively manipulate the model, capture model output, or run the model on specified inputs. It also has a command line that lets the user script and execute NEF model runs in full script mode.
The GUI (and GUI-like apps) is written using JavaFX. Python-based apps can be added to Nengo relatively easily, using a Python plugin.
The overall architecture of the GUI. The GUI communicates with the Nengo core using two packages: the core package, and the JNengo package. The JNengo package provides the Java bindings for the interface of the Nengo core. The core package provides the user interface for manipulating and running the NEF model, and provides the glue between the JNengo package and the GUI.
Relevant Information (links and resources):
– SciPy
– Gigamon or Xively
– Nengo User’s Guide
– Hack-A-Day: Python Challenge #19: Nengo
– Nengo Python Plugin
– Nengo Documentation
– Visualizing Neuron Behavior in Biological Units: Nengo
– Nengo: Programming a Spiking Neural Network in Python
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jSlink is a JavaScript Framework for creating network diagrams. It allows to create and manipulate network diagrams easily and intuitively by using simple and logical elements.
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There are tons of Network Diagrams in the world. Each network has a different structure and is made up of different elements that must be connected. In a daily life, we need to be familiar with
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Nengo is built around the NEF and is comprised of two parts:
– simulation using synapses, membrane voltage, and spike generation with the NEF;
– simulation using synapses, membrane voltage, and spike generation without the NEF.
These are embedded within a modular and extensible object model. The entire suite can be built from scratch without ever writing any code. For more information, see the Nengo GUI documentation.
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Fundamentally, IoT devices are controlled through various routes of communication, amongst those are Wireless (Wifi, Bluetooth, and others), Zigbee, RFID, NFC, USB, and so on. Wireless IoT devices communicate with each other and devices running off of the network by broadcasting radio waves in the frequency range between 300KHz and 300GHz. If you have a sensor that can detect RF emissions then you can also use it to monitor your network.
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It is also pretty obvious that physical things exist in some sort of spacetime continuum — one cannot remember a past event without the intervention of space-time.
But you can’t directly measure spacial-temporal changes in spacetime — you can only observe changes in relative position of the object and the thing-detector — and the object detector could be another space-time. So it all seems muddled.
In this course, we will give a very gentle introduction to the more mathematical and more generalized algebraic concepts involved in discrete mathematics in a manner suitable for anyone wanting to use them later on as a stepping stone to probability and linear algebra. The topics that will be covered in this course are:
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What’s New in the?
A visual programming environment for creating NEF models using spiking point neurons.
Nengo contains the following components:
Nengo
Nengo commands Editor enables creation of.npy models Python files
Control GUI or Python Console enables execution of.npy models using the Python interpreter.
Script Package allows you to share.npy files across different Python sessions in Nengo, so that they can be executed together.
Model Description:
A tutorial describing how to set up a NEF model in a Nengo GUI.
Forget me Notifications Service, which allows you to send a notification when the model has reached a particular state.
Nengo contains five basic building blocks:
Nengo Point Neuron
The point neuron model is a widely-used, simple, but effective model of how neurons behave. It is used in a wide variety of NEF models.
Nengo Point Neuron
Nengo Spiking Integrate and Fire:
This example models a cell that is based on the integrate-and-fire model. The cell fires a spike every time it reaches a certain threshold. This input is effectively converted into a current pulse. The input can also be modelled as a firing rate.
Inputs:
I1 – Input current
T1 – Membrane potential threshold
I2 – Integrate and fire voltage
T2 – Integrate and fire voltage threshold
Module Input Details:
Input Current Input name: I1
Input Threshold Membrane potential Threshold name: T1
Integrate and Fire Voltage Integrate and fire voltage threshold name: T2
Module Output Details:
Signal Output name: Spike
Nengo Point Neuron:
The Nengo point neuron is a spiking model of a neuron.
The behaviour of this model is controlled via a single tuple. You can choose a default behaviour, which is then overridden when the user has inputted a different value for some of the parameters.
Nengo Point Neuron:
Nengo Spiking Linear Threshold:
This example models a cell that is based on the integrate-and-fire model. The cell fires a spike every time it reaches a certain threshold. This input is effectively converted into a current pulse. The input can also be modelled as a firing rate.
Inputs:
I1 – Input current
T1 – Membrane potential threshold
I2 – Integrate
System Requirements For Nengo:
Minimum:
OS: Microsoft® Windows® 7/Vista/XP with SP2 or newer
Processor: 1.5GHz processor with at least 2 GB of RAM
Additional:
Operating System: Microsoft® Windows® 7/Vista/XP with SP2 or newer
Audio:
4.0
Network:
LAN: 10Mbps or faster