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Related Concept Videos

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: The Axon01:21

Neurons: The Axon

Axons are long, cytoplasmic processes of nerve cells capable of propagating electrical impulses known as action potentials. The cytoplasm or axoplasm of an axon contains neurofibrils, neurotubules, small vesicles, lysosomes, mitochondria, and various enzymes, all encased within the axolemma, the plasma membrane of the axon.
The axon attaches to the cell body at a cone-shaped elevation called the axon hillock. The initial part of the axon, closest to the hillock, is known as the initial segment.
Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
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...
Action Potential01:14

Action Potential

Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...
Integration of Synaptic Events01:28

Integration of Synaptic Events

Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...

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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Spiking neuron computation with the time machine.

Vaibhav Garg1, Ravi Shekhar, John G Harris

  • 1Texas Instruments Incorpoarted, Dallas, TX 75266, USA. vaibhav@cnel.ufl.edu

IEEE Transactions on Biomedical Circuits and Systems
|July 16, 2013
PubMed
Summary
This summary is machine-generated.

The Time Machine (TM) architecture uses synaptic weights in time, enabling virtual synapses for efficient, flexible neuromorphic computing. This spike-based system supports advanced algorithms and has been realized in both analog and digital hardware.

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Area of Science:

  • Neuromorphic Engineering
  • Computer Architecture
  • Artificial Intelligence

Background:

  • Traditional synapse architectures face limitations in flexibility and hardware usage.
  • Spike-based computation offers a promising alternative for energy-efficient processing.
  • Representing synaptic weights in time is an underexplored area in neuromorphic design.

Purpose of the Study:

  • To introduce the Time Machine (TM) architecture for spike-based computation.
  • To demonstrate the advantages of time-based synaptic weight representation.
  • To present hardware implementations and simulation tools for the TM architecture.

Main Methods:

  • Developed the Time Machine (TM) architecture with virtual synapses.
  • Created SpikeSim, a behavioral hardware simulator for the TM.
  • Implemented TM in custom hybrid digital/analog and fully digital (FPGA) systems.
  • Fabricated an analog chip and a FPGA-based digital realization.

Main Results:

  • The TM architecture allows arbitrary synaptic connections with efficient hardware usage.
  • SpikeSim successfully simulated edge detection and object recognition algorithms.
  • An analog chip (32 neurons, 1024 synapses) and a FPGA implementation (6,144 neurons) were realized.
  • Both implementations achieved high spike routing throughput (up to 34 million synapses/sec).

Conclusions:

  • The Time Machine architecture offers a flexible and efficient approach to neuromorphic computing.
  • Time-based synaptic weight representation is a viable strategy for advanced AI hardware.
  • The TM architecture is suitable for both traditional spike-based and novel time-mode operations.