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Related Concept Videos

Neuronal Communication01:28

Neuronal Communication

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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...
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Synaptic Signaling01:09

Synaptic Signaling

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Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...
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Electrical Synapses01:28

Electrical Synapses

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Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
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The Synapse02:47

The Synapse

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Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.
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Integration of Synaptic Events01:28

Integration of Synaptic Events

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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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The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

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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....
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Related Experiment Video

Updated: Jul 16, 2025

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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A 22-pJ/spike 73-Mspikes/s 130k-compartment neural array transceiver with conductance-based synaptic and membrane

Jongkil Park1,2,3, Sohmyung Ha2,4,5, Theodore Yu2,3

  • 1Center for Neuromorphic Engineering, Korea Institute of Science and Technology (KIST), Seoul, Republic of Korea.

Frontiers in Neuroscience
|September 13, 2023
PubMed
Summary

This study introduces the Integrate-and-Fire Array Transceiver (IFAT) chip, a novel neuromorphic computing system. The IFAT chip efficiently emulates large-scale biological neural networks with high biophysical realism and flexible connectivity.

Keywords:
address event representation (AER)asynchronous pipeliningconductance-based synapsedendritic computationintegrate-and-fire array transceiver (IFAT)log-domain translinear circuitsneuromorphic cognitive computingrectified linear unit (ReLU)

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Area of Science:

  • Neuromorphic Engineering
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Neuromorphic computing aims to replicate biological neural systems in silicon.
  • Existing approaches either focus on detailed biophysical simulation or abstract biological details for efficiency.
  • A hybrid approach is needed for large-scale, efficient, and realistic neural network emulation.

Purpose of the Study:

  • To develop a neuromorphic system that combines biophysical realism with large-scale implementation efficiency.
  • To create a flexible platform for emulating complex neuronal network dynamics and connectivity.
  • To enable efficient large-scale emulation of spiking neural networks and rate-based activations.

Main Methods:

  • Development of the Integrate-and-Fire Array Transceiver (IFAT) chip.
  • Emulation of 65,000 two-compartment neurons with conductance-based synapses.
  • Utilizing address-event representation for spike encoding and reconfigurable synaptic connectivity.
  • Implementing a two-tier micro-pipelining architecture for enhanced throughput and efficiency.
  • Employing digital control for synapse strength and single-point digital offset calibration.

Main Results:

  • The IFAT chip successfully emulates large-scale neuronal network dynamics with high biophysical realism.
  • Achieved a sustained peak throughput of 73 million spikes per second.
  • Demonstrated a chip-level energy efficiency of 22 picojoules per spike.
  • Enabled flexible, layered, and recurrent synaptic connectivity through hierarchical address-event routing.
  • Mitigated analog mismatch issues in synapse strength through digital calibration.

Conclusions:

  • The IFAT chip represents a significant advancement in neuromorphic cognitive computing.
  • It offers a highly efficient and flexible platform for large-scale emulation of biophysical spiking neural networks.
  • The system supports both detailed biophysical simulations and rate-based neural activation mapping.
  • This technology facilitates the development of more sophisticated artificial intelligence systems inspired by biological brains.