Supports several algorithms. Trial versions are available. Can be extended writing new modules.

### Categories

Made up of C libraries to build networks, and pre-built simulators. Creates multilayer feedforward networks with support for both fully connected and sparse connected networks. Supports execution in fixed point, for fast execution on systems like the iPAQ.

Designed for predicting, classification and financial forecasting. Demo version available. Supports wizard-based GUI and performance monitoring charts. Digits can be drawn using the mouse and recognized by the applet in the browser. Uses backpropagation. Can handle missing values and categorical data. Interfaces with Matlab's Neural Network Toolbox. Evaluation version available. Mean-firing rate neurons do not simulate the emission of single spikes, but rather compute directly their instantaneous firing rate number of spikes per second, also expressed in Hz.

Mean-firing rate networks require much more intercommunication between neurons than spiking neurons, because the weighted sum has to be computed at each time step for all incoming synapses. In spiking networks, the weighted sum is only updated when some pre-synaptic neuron emits a spike, which is a relatively rare event.

The goal of ANNarchy is to provide a simulator that is equally optimized for both types of networks and allows for mixtures of the two frameworks hybrid networks. It is particularly intended for cognitive modeling, where emphasis is put on the global function performed by a network of heterogeneous populations rather than local interactions within a population.

ANNarchy 4. Sections 1. Introduction 1.

Why another neural simulator? Installation of ANNarchy 1. External documentation 1. Changelog 2. Manual 2. General structure 2. Parser 2.

- The United States of Wal-Mart!
- Top Authors.
- A Simulation Environment for Deep Neural Networks: Theory and Practice.
- Virtual and Adaptive Environments: Applications, Implications, and Human Performance Issues.

Rate-coded neurons 2. Spiking neurons 2. Rate-coded synapses 2. Spiking synapses 2. Populations 2. Projections 2. Connectivity 2. Setting inputs 2. Simulation 2.

Configuration 2. Equations and numerical methods 2.

**lopsemoucompmisctet.tk**

## A Closed-Loop Toolchain for Neural Network Simulations of Learning Autonomous Agents

Recording with Monitors 2. Saving and loading a network 2. Parallel simulations and networks 2. Hybrid networks 2.

## A Closed-Loop Toolchain for Neural Network Simulations of Learning Autonomous Agents

Structural plasticity 2. Convolution and pooling 2. Reporting 3. Library reference guide 3.

Module ANNarchy 3.