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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...
Storage01:23

Storage

A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze each...
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...
Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...
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.
The Synapse02:47

The Synapse

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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Related Experiment Video

Updated: Jun 6, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Parsing recursive sentences with a connectionist model including a neural stack and synaptic gating.

Anna Fedor1, Péter Ittzés2, Eörs Szathmáry3

  • 1Institute of Biology, Eötvös Loránd University, 1/C Pázmány Péter stny, H-1117 Budapest, Hungary; Collegium Budapest (Institute for Advanced Study), 2 Szentháromság utca, H-1014 Budapest, Hungary.

Journal of Theoretical Biology
|December 4, 2010
PubMed
Summary

This study introduces a minimalist neural network capable of parsing complex human language structures and simpler primate grammars. The novel neural stack memory efficiently processes hierarchical information without biologically unrealistic algorithms.

Related Experiment Videos

Last Updated: Jun 6, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

  • Computational neuroscience
  • Cognitive science
  • Artificial intelligence

Background:

  • Humans may possess a genetic predisposition for recognizing center-embedded recursive structures (context-free grammars), unlike primates limited to tail-recursive structures (finite state grammars).
  • Developing biologically plausible computational models is crucial for understanding hierarchical processing in the brain.

Purpose of the Study:

  • To design and implement a minimalist neural network model.
  • To enable efficient parsing of both context-free and finite state grammars.
  • To avoid biologically unrealistic learning algorithms like backpropagation.

Main Methods:

  • Construction of a minimalist neural network.
  • Integration of a neural stack-like memory with synaptic gating for push/pop operations.
  • Training the network on artificial sentences from both grammar types.

Main Results:

  • The neural network successfully categorized novel sentences from both context-free and finite state grammars after training.
  • The model demonstrated efficient parsing capabilities.

Conclusions:

  • The proposed neural stack memory is a potentially significant component for biological hierarchical processors.
  • The minimalist design supports the search for realistic neural architectures underlying hierarchical processing.