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Related Concept Videos

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Neuronal Communication01:28

Neuronal Communication

Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
Network Function of a Circuit01:25

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential.
Neurons as Communicators of the Brain01:22

Neurons as Communicators of the Brain

Neurons, the fundamental units of the brain and nervous system, function as the primary transmitters of information throughout the body. Their ability to communicate through electrical and chemical signals is vital for every bodily function, from regulating the heartbeat to processing complex thoughts. Each neuron has three main components: the cell body (soma), dendrites, and an axon, each specialized to facilitate swift and efficient neural communication.
Cell Body
The cell body, also known...
Overview of Synapses01:25

Overview of Synapses

A synapse is a specialized structure where two neurons connect, allowing them to pass an electrical or chemical signal to another neuron. It is the point of communication between neurons. The term "synapse" is derived from the Greek word "synapsis," which means "conjunction." The entire process of neural communication revolves around the synapse. When activated, a neuron releases chemicals known as neurotransmitters into the synapse. These neurotransmitters cross the synapse and bind to...

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Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
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Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

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Six networks on a universal neuromorphic computing substrate.

Thomas Pfeil1, Andreas Grübl, Sebastian Jeltsch

  • 1Kirchhoff-Institute for Physics, Universität Heidelberg Heidelberg, Germany.

Frontiers in Neuroscience
|February 21, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a configurable neuromorphic computing substrate for emulating neural networks. The system offers high parallelism and acceleration, enabling neuroscientists to build diverse network models efficiently.

Keywords:
accelerated neuromorphic hardware systemclassifiercortical modelhighly configurablemixed-signal VLSIsoft winner-take-allspiking neural networksuniversal computing substrate

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Area of Science:

  • Neuroscience
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Neuromorphic computing aims to mimic the brain's structure and function.
  • Emulating complex neural networks requires specialized hardware for efficiency.
  • Current systems often lack flexibility in network topology and parameterization.

Purpose of the Study:

  • To present a highly configurable neuromorphic computing substrate.
  • To demonstrate its capability as a universal neural network emulator.
  • To showcase its application in emulating diverse neural network types.

Main Methods:

  • Development of a mixed-signal chip with analog neurons/synapses and digital action potential transmission.
  • Implementation of calibration routines to mitigate analog circuitry noise.
  • Creation of an integrated development environment for user-friendly operation.

Main Results:

  • Successful emulation of six distinct neural networks with varied structures and functions.
  • Demonstration of inherent parallelism and high acceleration factors compared to conventional computing.
  • Validation of the substrate's configurability for arbitrary network topologies and parameters.

Conclusions:

  • The presented neuromorphic substrate is a powerful and flexible tool for neural network emulation.
  • Its design facilitates complex network simulations with high performance.
  • The integrated development environment lowers the barrier for neuroscientists to utilize neuromorphic hardware.