Hebbian learning vs backpropagation Backpropagation and contrastive Hebbian learning (CHL) are two super-vised learning algorithms for training networks with hidden neurons. Backpropagation forms an important part of a number of supervised learning algorithms for training feedforward neural networks, such as stochastic gradient descent. dis-nn] 13 Jan 2024 Jul 4, 2021 · Deep learning networks generally use non-biological learning methods. Many studies suggest that the brain may implement a similar algorithm. Because of its unsupervised nature, it will rather learn frequent properties of the input statistics than task-specific properties. cTheSNNperformsfeed-forwardrecognition fi a, b Backpropagation vs. According to this rule, connections between neurons presenting correlated activity are strengthened. Hebbian learn-ing, a completely unsupervised and feedback free learning technique is a strong contender for a biologically plausible alternative. 07110v1 [cond-mat. Combined with optimization techniques like gradient Feb 1, 2003 · Backpropagation and contrastive Hebbian learning are two methods of training networks with hidden neurons. e. Introduction The Backpropagation algorithm to train neural networks is considered to be biologically implausible. ef cientlyimplementedinhardware. In a very general framework of three-factor learning, plasticity is realized by changing a synaptic strength w with the following rule (1) w = F (p r e, p o s t, g, w), where pre and post are some functions Nov 1, 2021 · We propose to address the issue of sample efficiency, in Deep Convolutional Neural Networks (DCNN), with a semi-supervised training strategy that combines Hebbian learning with gradient descent: all internal layers (both convolutional and fully connected) are pre-trained using an unsupervised approach based on Hebbian learning, and the last fully connected layer (the classification layer) is 2. This process has similarities to prototype learning in A Hopfield network (or associative memory) is a form of recurrent neural network, or a spin glass system, that can serve as a content-addressable memory. ABSTRACT Backpropagation has revolutionized neural network training how-ever, its biological plausibility remains questionable. Backpropagation in Spiking Neural Networks (SNNs) engenders Spike-Timing-Dependent Plasticity (STDP)-like Hebbian Learning Behaviour. Sep 8, 2016 · The algorithm successfully learns how to learn the relevant associations from one-shot instruction, and fine-tunes the temporal dynamics of plasticity to allow for continual learning in response to changing environmental parameters. Oct 1, 1996 · By imposing saturation requirements on hidden-layer neural activations, a new learning algorithm is developed to improve robustness on classification … Apr 17, 2020 · Nonetheless, recent developments in neuroscience and the successes of artificial neural networks have reinvigorated interest in whether backpropagation offers insights for understanding learning Contrastive Hebbian Learning[7J (CHL), which is a generalization of the Hebbian rule, updates the weights prop<2!'~ionally to the difference in the crossproducts 10 of activations in a clamped and a free running phase. In this work May 9, 2024 · Mathematical models and computer simulations suggest that anti-Hebbian STDP, the type of plasticity observed between cortex and striatum, could support the learning of sequences in the brain. Solution: Combine Hebbian Learning with supervised methods like backpropagation to benefit from both unsupervised feature discovery and guided learning. ''Bidirectional Associative Memories: Unsupervised Hebbian Learning to Bidirectional Backpropagation,'' IEEE Transactions on Systems, Man, and Cybernetics, vol. Abstract This works focuses on evaluating whether the neural network transformer archi-tecture can be learned in a more biologically plausible manner than is currently done using backpropagation. To overcome the unrealistic symmetry in connections between layers, implicit in back-propagation, the feedback weights are separate from the feedforward weights. Contrastive Hebbian learning, which is a powerful rule inspired by gradient backpropagation, is based on Hebb's rule and the contrastive divergence algorithm. It provides a unifying perspective on Hebbian learning, gradient descent, and generalized linear models, for all of which we discuss the advantages and disadvantages. I think I've discovered something amazing: Hebbian learning naturally takes place during the backpropagation of SNNs. Backpropagation and contrastive Hebbian learning are two methods of training networks with hidden neurons. We show that deep networks can be trained using Hebbian updates yielding similar performance to ordinary back-propagation on challenging image datasets. supervised learning. Oct 8, 2024 · This might surprise you: Hebbian learning and backpropagation belong to different worlds — unsupervised vs. This course reveals why current algorithms fall short of biological learning and how predictive coding could lead to more adaptive, resilient, and efficient artificial systems. Aug 4, 2025 · Analog resistive synapses enable neuromorphic learning, but result in imprecision that limits inference accuracy. The document provides an extensive overview of Soft Computing and Artificial Neural Networks (ANN), detailing their definitions, characteristics, components, and applications. We analyze the stability of the stored memories: basins of attraction obtained by the Hebbian unlearning technique are found to be comparable in size to those The correlation learning rule is based on a similar principle as the Hebbian learning rule. Hebbian learning naturally occurs during the backpropagation of SNNs. It highlights the efficiency of Hebbian learning combined with supervised learning in forming a low-dimensional and coarse representation, and its role in many cognitive tasks by providing a basis activity patterns and dynamics. Nov 7, 2024 · View a PDF of the paper titled Rethinking Deep Learning: Non-backpropagation and Non-optimization Machine Learning Approach Using Hebbian Neural Networks, by Kei Itoh What is the state of research regarding hebbian/local learning rules, why haven't they gotten traction? I was also specifically interested in research concerned w/ finding algorithms to discover an optimal local rule for a task (a hebbian meta-learner if that makes sense). These insights present practical reasons for utilising Bio-learning rather than just its biological plausibility and also point towards interesting new directions for future work on Bio-learning. 6 Cost functions vs Loss functions in dense Hebbian networks 4 Conclusion and outlook License: CC BY 4. Bitking January 4, 2020, 3:18pm 47 Nov 28, 2023 · Abstract Given the unprecedented growth of deep learning applications, training acceleration is becoming a subject of strong academic interest. Backpropagation computes an error signal for the output neurons and spreads it over the hidden neurons. Many computational models propose that the hippocampus is an autoassociator that relies on Hebbian learning (i. For example, Hebbian learning with Winner-Takes-All (HWTA Hebbian learning is defined as a local and unsupervised learning mechanism that emphasizes the association of activity patterns in the brain, contributing to natural learning and memory association by utilizing spatial and temporal activity changes. I’ve been showing a network with An input layer Backpropagation has revolutionized neural network training however, its biological plausibility remains questionable. Connectionist theories of learning are essentially abstract implementations of general features of brain plasticity in architectures of artificial neural networks. MIT Department of Brain and Cognitive Sciences 9. 641J, Spring 2005 - Introduction to Neural Networks Instructor: Professor Sebastian Seung Dec 31, 2021 · The Hebbian unlearning algorithm, i. backprop or the training procedure has been very complex To me backpropagation is indeed biologically implausible due to "the requirement for symetric feedback", which we do not observe in natural NNs. Example: A self-taught musician who also takes lessons from a professional to refine their skills. Dual propagation [10] (DP), an algorithm similar in spirit to contrastive Hebbian learning, equilibrium propagation and coupled learning, is compatible with non-infinitesimal nudging by default. Backpropagation in Spiking Neural Networks (SNNs) engenders Spike-Timing-Dependent Plasticity (STDP)-like Hebbian learning behavior. While there’s growing evidence suggesting that models trained with backprop can accurately explain neuronal data, no backprop-like method has yet been discovered in the biological brain for learning. Oct 11, 2022 · The hippocampus plays a critical role in the rapid learning of new episodic memories. A fundamental question is how does learning take place in living neural networks? “Nature's little secret,” the learning algorithm practiced by nature at the neuron and synapse level, may well be the Hebbian-LMS algorithm. Starting from this simple principle, it is possible to formulate di er-ent variants of the Hebbian learning rule which are in-teresting also from the computer science point of view. Feb 19, 2024 · We show that Hebbian and anti-Hebbian learning on recurrent lateral connections can effectively extract the principal subspace of neural activities and enable orthogonal projection. Sep 2, 2019 · Backpropagation is the most common learning rule for artificial neural networks. But most of them require symmetric weights between neurons, which makes the models less biologically Aug 11, 2020 · Overview of the paper “Meta-Learning through Hebbian Plasticity in Random Networks” by E Najarro et al. the Hebbian Network. They tuned their synaptic weights with unsu-pervised Hebbian or competitive learning. The typical implementations of these rules change the synaptic strength on the basis of the co-occurrence of the neural events taking place at a certain time in the pre- and post-synaptic neurons. They are of interest, because they are generally applicable to wide classes of net-work architectures. In contrast, machine learning architectures based on artificial neural networks fail to learn multiple tasks in sequence and require data of all tasks to be present at once. However, so far, it has either not achieved high accuracy performance vs. It is one of the first and also easiest learning rules in the neural network. It is an attempt to explain synaptic plasticity, the adaptation of brain neurons during the learning process. The learning principle was first proposed by Hebb (1949), who postulated that a presynaptic neuron A, if successful in repeatedly activating a postsynaptic neuron B when itself (neuron Oct 6, 2025 · Backpropagation, short for Backward Propagation of Errors, is a key algorithm used to train neural networks by minimizing the difference between predicted and actual outputs. To overcome the unrealistic symmetry in connections between layers, implicit in . springer. As the brain is much more messy at least chemically and by neuronal connections. By addressing the limitations of traditional learning paradigms, the HAAM Hebbian Learning explains how neurons adapt and form stronger connections through repeated use. We consider two unsupervised learning approaches, Hebbian Winner-Takes-All (HWTA), and Hebbian Principal Component Analysis (HPCA). Earlier BAMs had no hidden neurons and did not use supervised learning. This method infers two sets of oppositely nudged and mutually tethered states simultaneously. In this work Aug 19, 2024 · In combination with centering, Hebbian descent leads to better continual learning capabilities. Specifically, the concept of Hebbian learning refers to a family of learn-ing rules, inspired by biology, according to which the weight associated with a synapse increases proportionally to the values of the pre-synaptic and post biological or analog neural networks is an open question. Contrastive Hebbian learning involves clamping the output neurons at desiredvalues and letting the effectspread through feedback connections over the entire network. We identify three main problems with the biological plausibility of backpropagation-based learning, the weight transport problem, the global loss problem and the asymmetry problem, and prescribe The majority or the connectionist theories of learning are based on the Hebbian Learning Rule (Hebb 1949). In a very general framework of three-factor learning, plasticity is realized by changing a synaptic strength w with the following rule (1) w = F (p r e, p o s t, g, w), where pre and post are some functions Nov 1, 2021 · We propose to address the issue of sample efficiency, in Deep Convolutional Neural Networks (DCNN), with a semi-supervised training strategy that combines Hebbian learning with gradient descent: all internal layers (both convolutional and fully connected) are pre-trained using an unsupervised approach based on Hebbian learning, and the last fully connected layer (the classification layer) is Dec 31, 2021 · The Hebbian unlearning algorithm, i. The backpropagation algorithm solves this problem in deep artificial neural networks, but historically it has been viewed as Hebbian versus Perceptron Learning It is instructive to compare the Hebbian and Oja learning rules with the Perceptron learning weight update rule we derived previously, namely: Nov 8, 2024 · Note, however, that here, unlike other work with Hebbian learning rules, due to the particular activity routed to the layers, the learning rule implements a supervised mechanism (backpropagation). Abstract Learning in biologically relevant neural-network models usually relies on Hebb learning rules. In May 4, 2017 · Keywords: artificial neural network, backpropagation algorithm, biologically plausible learning rule, contrastive hebbian learning, deep learning, fixed point, Hopfield networks, spike-timing dependent plasticity We would like to show you a description here but the site won’t allow us. 3. localized Hebbian learning. Sep 8, 2014 · Thus a Hebbian learning rule needs either the bilinear term c 11 corr (w i j) ν i ν j with c 11 corr > 0 or a higher-order term (such as c 21 (w i j) ν i 2 ν j ) that involves the activity of both pre- and postsynaptic neurons. Each learning curve is averaged over 10 different runs. 2 Contrastive Hebbian Learning h pure Hebbian learning (Hebb, 1949). Hebbian learning as a training strategy alternative to backpropagation presents a promising optimization approach due to its locality, lower computational complexity and parallelization potential. Contrastive Hebbian Learning with Dyadic Neurons In this section we present a framework inspired by con-trastive Hebbian learning, that is based on positively and negatively nudged internal states maintained for every neu-ron. Nodes which tend to be either both positive or negative at the same time result in strong positive weights while those which tend to be opposite result in strong negative weights. Abstract Recently, unsupervised local learning, based on Hebb's idea that change in synaptic efcacydependsontheactivityofthepre-andpostsynapticneurononly, hasshown potential as an alternative training mechanism to backpropagation. Apr 1, 2022 · Request PDF | Comparing the performance of Hebbian against backpropagation learning using convolutional neural networks | In this paper, we investigate Hebbian learning strategies applied to Apr 1, 2021 · The recent development of Hebbian learning re-evaluates its contribution to natural learning and memory association. Previous reports suggest that the opposite is true for humans: We learn better when trained on one task at a time. Oct 1, 2017 · Associative (Hebbian) learning indicates association between two factors (two sensory inputs or an input and an output), but such a learning is often influenced by a so-called third factor. Sep 3, 2021 · An amazing discovery has been made. Does hebbian learning utilize backpropagation? Here's another thing I read online about hebbian learning that makes me think the two are related - "A synapse between two neurons is strengthened when the neurons on either side of the synapse (input and output) have highly correlated outputs. The discriminative learning rule, such as Hebbian learning, can improve performance of learning by quickly adapting to discriminate between different classes defined by target task. Aug 28, 2024 · Recent efforts from the research community focused on the development of biologically plausible alternatives to the backpropagation algorithm for Deep Neural Network (DNN) training. Typically, Hebbian learning with highly correlated states leads to degraded memory performance. Quick Recap We’ve been looking at the classic example of recognizing handwritten digits. Nov 3, 2017 · Here we tackle backpropagation, the core algorithm behind how neural networks learn. Oct 28, 2017 · The backpropagation algorithm has played a critical role in training deep neural networks. Two-layer feedback BAMs always converge to fixed-point equilibria for threshold or We show that deep networks can be trained using Hebbian updates yielding similar performance to ordinary back-propagation on challenging image datasets. Each network is trained for 70 epochs. Backpropagation is an algorithm driven by error, whereas CHL is a Hebbian-type algorithm, with update rules based on the correlation of pre- and postsynaptic activi-ties. Seeking for more plausible models that mimic biological brains, researchers introduced several alternative learning rules for artificial Running head: HEBBIAN LEARNING How Far Can You Go with Hebbian Learning, and When Does it Lead you Astray? James L. We conclude that backpropagation of Hebbian plasticity offers a powerful model for lifelong learning. INTRODUCTION Aug 14, 2025 · Backpropagation has revolutionized neural network training however, its biological plausibility remains questionable. Jan 3, 2020 · Even though it’s hebbian-like learning it still is inspired with backpropagation and in my understanding it will need a symmetric set of neural pathways to update the weights during feedback which I believe is not true in the brain. Competitive Hebbian The weight between two neurons increases if the two neurons activate simultaneously and reduces if they activate separately. It contrasts Soft Computing with Hard Computing, highlighting the flexibility and robustness of Soft Computing techniques. In this The are variations of Hebbian learning that do provide powerful learning techniques for biologically plausible networks, such as Contrastive Hebbian Learning, but we shall adopt another approach for formulating learning algorithms for our networks. Keywords: artificial neural network, backpropagation algorithm, biologically plausible learning rule, contrastive hebbian learning, deep learning, fixed point, Hopfield networks, spike-timing dependent plasticity Jul 1, 2022 · Inspired by the role of the neuromodulator dopamine in synaptic modification, neo-Hebbian RL methods extend unsupervised Hebbian learning rules with value-based modulation to selectively reinforce associations. This article introduces latent predictive learning (LPL), a concep-tual learning framework that overcomes this limitation and reconciles SSL with Hebbian plasticity. Backpropagation has revolutionized neural network training however, its biological plausibility remains questionable. The proposed temporally local learning rule follows the backpropagation weight change updates applied at each time step. These connections are bidirectional and symmetric, meaning the weight of the connection Jul 17, 2024 · Backpropagation Algorithm: While not directly Hebbian, the widely-used backpropagation algorithm in deep learning can be seen as an extension of Hebb's ideas. This reinforcement allows for learning exploitative behaviors and produces RL models with strong biological plausibility. However, so far, it has neither achieved high accuracy performance vs. McClelland and Department of Psychology, Carne Pittsburgh, PA. Abstract | During learning, the brain modifies synapses to improve behaviour. Peng Zhou and colleagues propose and experimentally demonstrate analog Hebbian In transfer learning, for such models, the connection weights of the learned model should adapt to new target dataset with minimum effort. For example, Hebbian learning with Winner-Takes-All (HWTA Apr 1, 2025 · This in-depth tutorial on Neural Network Learning Rules explains Hebbian Learning and Perceptron Learning Algorithm with examples. From what I understood, here they update the network layer by layer based on an estimate of the mutual information between the layer itself, the input layer, and the output. Fig. When training a neural network by gradient descent, a 3. See full list on link. In this work The Local Learning Principle 179 This chapter and the following one leverage the principle of local learning [103] and use it to answer several fundamental questions, including: (1) what is the relationship between Hebbian learning and backpropagaton, in particular is backpropagation \Hebbian"? (2) what is the space of learning rules? (3) why has no one been able to carry out the Fukushima I understand that before back propagation was developed there were other methods used such as hebbian learning, and admittedly I know nothing about these old methods. The Hebbian learning rules are used to train the layers of a CNN in order to extract features that are then used for classification, without requiring May 16, 2024 · We show that optimizing the HSIC bottleneck via gradient descent emits a three-factor learning rule (Frémaux and Gerstner, 2016) composed of a local Hebbian component and a global layer-wise modulating signal. biological or analog neural networks is an open question. Here we explore the differences between Hebbian learning and backpropagation, both regarding accuracy and representations of data in hidden layers. Thus, the more accurate term would be "layer-wise learning" rather than BP-free. Each time a memory is recalled or an action is repeated, the neural pathways involved become more robust as they fire together, making that action or memory more intuitive or easy to reproduce. It operates in two phases, the forward (or free) phase, where the data are fed to the network, and a backward (or clamped) phase, where the target Abstract We discuss prototype formation in the Hopfield network. Moreover, employing a naive implementa-tion of backprop in the brain has several Abstract Learning in biologically relevant neural-network models usually relies on Hebb learning rules. Jan 19, 2023 · Author summary Humans can learn multiple tasks over their lifetime with minimal forgetting. Here, we propose that non-synaptic and synaptic plasticity are both essential Dec 2, 2024 · A Blog post by Jaward Sesay on Hugging Face What is Backpropagation? Backpropagation, short for backward propagation of errors, is a widely used method for calculating derivatives inside deep feedforward neural networks. Because pure Hebbian learning simply strengthens synapses that fire together, it induces a positive feedback loop which event ally drives many weights to infinity. Neural networks are commonly trained to make predictions through learning algorithms. 2 | Unsupervised learning with stochastic STT-MTJ switching. Learning curve of the Back-propagation network vs. Jun 26, 2025 · In conclusion, Hebbian learning and backpropagation each offer unique advantages and challenges. In this work, we explore one of these alternative learning rules—Hebbian learning—in the context of modern deep neural networks for image classifica-tion. 5 Recovering the free energy of dense Hebbian storage in the big data limit 3. Then I Nov 26, 2020 · Hebbian Learning Rule, also known as Hebb Learning Rule, was proposed by Donald O Hebb. 0 arXiv:2401. This process involves strengthening connections between neurons that fire together. Mar 9, 2025 · This paper presents the Hebbian-Augmented Associative Memory (HAAM) framework, which utilizes synaptic plasticity to improve few-shot learning capabilities in Spiking Neural Networks (SNNs). If you followed the last two lessons or if you’re jumping in with the appropriate background, you know what a neural network is and how it feeds forward information. CHL approaches handle this by splitting learning into two phases (Ga Jul 8, 2024 · One way brain neurons learn without backpropagation is through a process called Hebbian learning. The learning principle was first proposed by Hebb (1949), who postulated that a presynaptic neuron A, if successful in repeatedly activating a postsynaptic neuron B when itself (neuron A) is Hebbian learning is not a concrete learning rule, it is a postulate on the fundamental principle of biological learning. Abstract—Bio-inspired learning has been gaining popularity recently given that Backpropagation (BP) is not considered biologically plausible. Additionally, it covers the structure of ANNs, learning processes, types of architectures, and The backpropagation algorithm, or backprop, is a widely utilized op-timization technique in deep learning. We show this type of learning can lead to prototype formation, where unlearned states emerge as representatives of large correlated subsets of states, alleviating capacity woes. Dual propagation [20](DP), an algorithm similar in spirit to contrastive Hebbian learning, equilibrium propagation and coupled learning, is compatible with non-infinitesimal nudging by default. Keywords: artificial neural network, backpropagation algorithm, biologically plausible learning rule, contrastive hebbian learning, deep learning, fixed point, Hopfield networks, spike-timing dependent plasticity 1. Despite being initially developed for biologically inspired artificial networks, it is commonly known by neuroscience that this process is unlikely to be implemented by nature. Hebbian Learning Algorithms for Training Convolutional Neural Networks Gabriele Lagani Computer Science PhD University of Pisa Definition Hebbian learning is a form of activity-dependent synaptic plasticity where correlated activation of pre- and postsynaptic neurons leads to the strengthening of the connection between the two neurons. Nov 28, 2023 · Researchers are continuously exploring Hebbian learning as a biologically plausible alternative to backpropagation, aiming to bridge the gap between artificial neural networks and the human Jun 17, 2017 · I understand that backpropagation is good, but what are the main advantages (and disadvantaged) that it has over Hebbian learning? I'm mostly wondering about contrastive Hebbian learning, though arguments against Hebbian learning in general are welcomed. Nevertheless, due to the challenging optimization of Jan 1, 2024 · This algorithm has practical engineering applications and provides insight into learning in living neural networks. But as I've learned about back prop in wondering is there a line of research working on alternatives? Backpropagation and contrastive Hebbian learning are two methods of training networks with hidden neurons. However, Hebbian learning is computationally suboptimal as it does not learn in a way that is driven toward, and limited by, the objective of Dec 22, 2020 · Abstract and Figures Recent work has shown that biologically plausible Hebbian learning can be integrated with backpropagation learning (backprop), when training deep convolutional neural networks. Apr 1, 2022 · In this paper, we investigate Hebbian learning strategies applied to Convolutional Neural Network (CNN) training. How-ever, so far, it has neither achieved high accuracy performance vs. Here, we sought Bart Kosko , Fellow, IEEE Abstract—Bidirectional associative memories (BAMs) pass neural signals forward and backward through the same web of synapses. Backpropagation computes an errorsignalfor the output neurons and spreadsit over the hidden neurons. Backpropagation is the powerhouse behind most deep learning Firstly, it's important to clarify that even the Forward-Forward (FF) algorithm involves backpropagation but at the layer level. However, apart from overcoming the biological implausibility of BP, a strong motivation for using Bio-inspired algorithms remains lacking. Then I Mar 8, 2022 · View a PDF of the paper titled Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning, by Beren Millidge and 4 other authors Apr 2, 2021 · The backpropagation algorithm allows you to perform gradient descent on a network of neurons. Differential Hebbian learning (DHL) rules, instead, are able to update the synapse by taking Backpropagation is a machine learning algorithm for training neural networks by using the chain rule to compute how network weights contribute to a loss function. Feb 1, 2003 · Abstract. Jan 1, 2015 · Hebbian learning is a form of activity-dependent synaptic plasticity where correlated activation of pre- and postsynaptic neurons leads to the strengthening of the connection between the two neurons. In conclusion, we suggest that backpropagation of Hebbian plasticity is an efficient way to endow neural networks with lifelong learning abilities, while still being amenable to gradient descent. an unsupervised local procedure used to improve the retrieval properties in Hopfield-like neural networks, is numerically compared to a supervised algorithm to train a linear symmetric perceptron. 2 Unsupervised Hebbian learning 3. A possible biologically plausible learning mechanism could be based on the so-called Hebbian principle: \Neu-rons that re together wire together". I. Oct 12, 2023 · To study the interplay of Hebbian and predictive plasticity in sensory representational learning, we derived a plasticity model from an SSL objective function that is reminiscent of and extends Abstract Recently, unsupervised local learning, based on Hebb's idea that change in synaptic efcacydependsontheactivityofthepre-andpostsynapticneurononly, hasshown potential as an alternative training mechanism to backpropagation. By contrast, networks based on more biologically plausible learning, such as Hebbian learning, show comparatively poor performance and difficulties of implementation. Unfortunately, Hebbian learning remains experimental and rarely makes it way into standard deep learning frameworks. Biological constraints require neurons to use only locally available information to compute the weight updates, and neuroscientific observations suggest that synaptic plasticity follows the Hebbian model [1], [2 We would like to show you a description here but the site won’t allow us. Backpropagation in SNNs engenders STDP-like behavior. While Hebbian learning provides a window into the workings of the brain, backpropagation offers practical solutions for today's AI challenges. 51, no. Overall Hebbian networks performed considerably worse than conventional backpropagation-trained networks. Hebbian learning, a completely unsupervised and feedback free learning technique is a strong contender for a biologically plausible alternative. , “cells that fire together, wire together”). Mar 8, 2022 · View a PDF of the paper titled Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning, by Beren Millidge and 4 other authors Apr 2, 2021 · The backpropagation algorithm allows you to perform gradient descent on a network of neurons. Feb 10, 2022 · And more in particular I found in Wikipedia that: Hebbian theory is a neuroscientific theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell. Explore how the brain’s learning strategies can inspire the next generation of AI. It works by propagating errors backward through the network, using the chain rule of calculus to compute gradients and then iteratively updating the weights and biases. 1 Supervised dense Hebbian learning 3. So: At first I simply thought "hey, what about coding a Spiking Neural Network using an automatic differentiation framework?" Here it is. com Backpropagation is typically implemented in feedforward networks, whereas CHL is implemented in networks with feedback. In this Jul 7, 2019 · Hebbian learning naturally takes place during the backpropagation of Spiking Neural Networks (SNNs). Here we show that Hebbian learning in hierarchical, convolutional neural networks can be implemented almost trivially with modern deep learning frameworks, by Hebbian vs. In the cortex, synapses are embedded within multilayered networks, making it difficult to determine the effect of an individual synaptic modification on the behaviour of the system. 5. In addition, recent results indicate that non-synaptic plasticity processes such as the regulation of neural membrane properties contribute to memory formation, its functional role in memory and learning has however remained elusive. When we feed training data through an ANNs, we use the backpropagation algorithm to tell us how the weights should change. It assumes that weights between simultaneously responding neurons should be largely positive, and weights between neurons with opposite reaction should be largely negative. To investigate the Aug 20, 2021 · Backpropagation has revolutionized neural network training however, its biological plausibility remains questionable. backprop, nor is the training procedure simple. Dual propagation [20] (DP), an algorithm similar in spirit to contrastive Hebbian learning, equilibrium propagation and coupled learning, is compatible with non-infinitesimal nudging by default. The feedback weights are also updated with a local rule, the same as the An artificial neural network 's learning rule or learning process is a method, mathematical logic or algorithm which improves the network's performance and/or training time. Feb 22, 2019 · This paper proposes a novel supervised learning approach based on an event-based spike-timing-dependent plasticity (STDP) rule embedded in a network of integrate-and-fire (IF) neurons. Hebbian learning, specifically, is able to learn in just 5 epochs compared to around 100 epochs required by BP. Non-BP typically refers to models not trained with end-to-end backpropagation. Mar 17, 2025 · Synaptic plasticity is essential for memory formation and learning in the brain. Backpropagation computes an error For completeness,we consider two supervised Hebbian learning variants (Supervised Hebbian Classifiers—SHC, and Contrastive Hebbian Learning—CHL), for training the final classification layer, which are compared to Stochastic Gradient Descent training. The Hopfield network, named for John Hopfield, consists of a single layer of neurons, where each neuron is connected to every other neuron except itself. Many algorithms have been proposed in the literature which are all more biologically plausible than BP. 1, 103 - 115, January 2021.