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

MOS Capacitor01:25

MOS Capacitor

A Metal-Oxide-Semiconductor (MOS) capacitor is a fundamental structure used extensively in semiconductor device technology, particularly in the fabrication of integrated circuits and MOSFETs (metal-oxide-semiconductor field-effect transistors). The MOS capacitor consists of three layers: a metal gate, a dielectric oxide, and a semiconductor substrate.
The metal gate is typically made from highly conductive materials such as aluminum or polysilicon. Beneath the metal gate lies a thin layer of...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

You might also read

Related Articles

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

Sort by
Same author

Optimizing the Definition of Ischemic Core in CT Perfusion: Influence of Infarct Growth and Tissue-Specific Thresholds.

AJNR. American journal of neuroradiology·2022
Same author

COVID-19 and Acute Respiratory Distress Syndrome. Impact of corticosteroid treatment and predictors of poor outcome.

Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia·2020
Same author

[Floating right heart thrombus causing pulmonary embolism in a patient with acute ischaemic stroke: A case report and review of literature].

Neurologia·2020
Same author

Neuroimaging in hypoglycaemic encephalopathy diagnosis and prognosis: A case report.

Neurologia·2018
Same author

Multicasting mesh AER: a scalable assembly approach for reconfigurable neuromorphic structured AER systems. Application to ConvNets.

IEEE transactions on biomedical circuits and systems·2013
Same author

STDP and STDP variations with memristors for spiking neuromorphic learning systems.

Frontiers in neuroscience·2013

Related Experiment Videos

A CMOS analog adaptive BAM with on-chip learning and weight refreshing.

B Linares-Barranco1, E Sanchez-Sinencio, A Rodriguez-Vazquez

  • 1Centro Nacional de Microelectron., Seville.

IEEE Transactions on Neural Networks
|January 1, 1993
PubMed
Summary

This study demonstrates on-chip Hebbian learning in analog neural network hardware using a transconductance-mode approach. It evaluates transistor mismatch effects on learning performance in a bidirectional associative memory (BAM) prototype.

Related Experiment Videos

Area of Science:

  • Analog computing
  • Neuromorphic engineering
  • Integrated circuit design

Background:

  • The transconductance-mode (T-mode) approach enables analog neural network hardware.
  • On-chip learning and weight storage are crucial for robust neural hardware.
  • Device mismatches can impact the performance of analog neural networks.

Purpose of the Study:

  • To extend the T-mode approach for on-chip Hebbian learning and analog weight storage.
  • To evaluate the impact of learning circuit mismatches on neural network performance.
  • To validate theoretical predictions with experimental results.

Main Methods:

  • Implementation of a 5+5-neuron bidirectional associative memory (BAM) prototype using a standard CMOS process.
  • Theoretical computation and Monte Carlo HSPICE simulations to estimate transistor mismatch effects.
  • Experimental verification using the fabricated BAM prototype.

Main Results:

  • On-chip Hebbian learning and analog weight storage were successfully demonstrated.
  • Transistor mismatches in learning circuits were quantified and their impact assessed.
  • Theoretical predictions of mismatch effects were validated by experimental measurements.

Conclusions:

  • The T-mode approach effectively supports on-chip Hebbian learning in analog neural networks.
  • While on-chip learning can compensate for some nonidealities, learning circuit mismatches require careful consideration.
  • The study confirms the feasibility and validates the performance of the proposed analog neural network hardware.