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Related Concept Videos

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...
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
Long-term Potentiation01:35

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Long-term Potentiation01:25

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when presynaptic neurons...
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...

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Related Experiment Video

Updated: May 26, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Accounting for network effects in neuronal responses using L1 regularized point process models.

Ryan C Kelly1, Robert E Kass, Matthew A Smith

  • 1Computer Science Department Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh, PA 15213 rkelly@cs.cmu.edu.

Advances in Neural Information Processing Systems
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

Neural activity depends on more than just local input. Surrounding neuron spikes and global brain states significantly influence individual neuron firing rates, improving response predictions.

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Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
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Examining Local Network Processing using Multi-contact Laminar Electrode Recording
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Published on: September 8, 2011

Related Experiment Videos

Last Updated: May 26, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

Examining Local Network Processing using Multi-contact Laminar Electrode Recording
13:40

Examining Local Network Processing using Multi-contact Laminar Electrode Recording

Published on: September 8, 2011

Area of Science:

  • Neuroscience
  • Computational Neuroscience

Background:

  • Neuronal activity is influenced by both local stimuli and broader network states.
  • Understanding these influences is key to deciphering neural processing.

Purpose of the Study:

  • To investigate the contribution of neural and global network context to individual neuron firing rates.
  • To assess how surrounding neuronal activity and local field potentials affect neuronal responses in V1.

Main Methods:

  • Implemented an L1 regularized point process model.
  • Recorded simultaneous neuronal activity from V1 using a 96-electrode Utah array.
  • Analyzed the predictive power of surrounding spikes and local field potentials on single-unit firing.

Main Results:

  • Spikes from surrounding neurons strongly predicted individual neuron responses, beyond receptive field properties.
  • Local field potentials, reflecting global network states, also accounted for neuronal firing.
  • Incorporating network fluctuations improved estimates of single-trial firing rates.

Conclusions:

  • Neuronal firing rates are significantly shaped by neural and global network context.
  • Accounting for network states enhances the accuracy of predicting single-trial neuronal responses.
  • This approach offers improved insights into stimulus-response transfer functions in neural circuits.