Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Neuronal Communication01:28

Neuronal Communication

4.0K
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...
4.0K
Neurons as Communicators of the Brain01:22

Neurons as Communicators of the Brain

3.9K
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...
3.9K
Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

7.2K
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...
7.2K
Understanding Memory01:19

Understanding Memory

1.7K
Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
1.7K
Neuroplasticity01:01

Neuroplasticity

2.2K
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
2.2K
Neural Circuits01:25

Neural Circuits

3.1K
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...
3.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Inferring causal connectivity from pairwise recordings and optogenetics.

PLoS computational biology·2023
Same author

Neural signatures of natural behavior in socializing macaques.

bioRxiv : the preprint server for biology·2023
Same author

The study of plasticity has always been about gradients.

The Journal of physiology·2023
Same author

Overfitting to 'predict' suicidal ideation.

Nature human behaviour·2023
Same author

Neural spiking for causal inference and learning.

PLoS computational biology·2023
Same author

On PDE Characterization of Smooth Hierarchical Functions Computed by Neural Networks.

Neural computation·2021
Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Mar 8, 2026

Compartmentalization of Human Stem Cell-Derived Neurons within Pre-Assembled Plastic Microfluidic Chips
06:46

Compartmentalization of Human Stem Cell-Derived Neurons within Pre-Assembled Plastic Microfluidic Chips

Published on: May 3, 2019

67.2K

Could a Neuroscientist Understand a Microprocessor?

Eric Jonas1, Konrad Paul Kording2,3

  • 1Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, California, United States of America.

Plos Computational Biology
|January 13, 2017
PubMed
Summary
This summary is machine-generated.

Neuroscience data analysis methods reveal patterns but not the core logic of information processing in a microprocessor. This suggests current approaches may need refinement for understanding complex systems.

More Related Videos

Use of Pre-Assembled Plastic Microfluidic Chips for Compartmentalizing Primary Murine Neurons
10:50

Use of Pre-Assembled Plastic Microfluidic Chips for Compartmentalizing Primary Murine Neurons

Published on: November 2, 2018

52.3K
Microdissection of Mouse Brain into Functionally and Anatomically Different Regions
08:06

Microdissection of Mouse Brain into Functionally and Anatomically Different Regions

Published on: February 15, 2021

57.3K

Related Experiment Videos

Last Updated: Mar 8, 2026

Compartmentalization of Human Stem Cell-Derived Neurons within Pre-Assembled Plastic Microfluidic Chips
06:46

Compartmentalization of Human Stem Cell-Derived Neurons within Pre-Assembled Plastic Microfluidic Chips

Published on: May 3, 2019

67.2K
Use of Pre-Assembled Plastic Microfluidic Chips for Compartmentalizing Primary Murine Neurons
10:50

Use of Pre-Assembled Plastic Microfluidic Chips for Compartmentalizing Primary Murine Neurons

Published on: November 2, 2018

52.3K
Microdissection of Mouse Brain into Functionally and Anatomically Different Regions
08:06

Microdissection of Mouse Brain into Functionally and Anatomically Different Regions

Published on: February 15, 2021

57.3K

Area of Science:

  • Neuroscience
  • Computer Engineering
  • Artificial Intelligence

Background:

  • A prevailing view in neuroscience posits that large, complex datasets analyzed by advanced algorithms will unlock fundamental insights into brain function.
  • Current neuroscience lacks the extensive, multimodal datasets required to test this hypothesis and validate algorithmic findings.
  • The complexity and completeness of understanding in artificial systems like microprocessors offer a unique testing ground.

Purpose of the Study:

  • To evaluate the efficacy of popular neuroscience data analysis methods in elucidating information processing within a well-understood complex system.
  • To assess whether current analytical approaches can meaningfully describe the hierarchical information processing in a classical microprocessor.
  • To advocate for the use of systems with known ground truth, such as microprocessors, for validating analytical methods.

Main Methods:

  • Utilized a classical microprocessor as a model system due to its comprehensive understandability at multiple levels (transistor, logic gate, overall architecture).
  • Performed arbitrary experiments on the microprocessor to generate data.
  • Applied popular data analysis methods from neuroscience to the generated dataset.

Main Results:

  • Neuroscience data analysis techniques identified interesting structures within the microprocessor data.
  • These methods failed to meaningfully describe the hierarchical information processing inherent to the microprocessor.
  • The findings question the sufficiency of current analytical approaches for generating deep understanding, irrespective of data volume.

Conclusions:

  • Current popular data analysis methods in neuroscience may not be adequate for uncovering the fundamental principles of information processing in complex systems.
  • There is a critical need for validation platforms with known ground truth, like microprocessors, to rigorously test and refine time-series and structure discovery methods.
  • Future neuroscience research may benefit from incorporating validation studies on engineered systems to ensure the meaningfulness of algorithmic insights.