Artificial Neuronal Ensembles with Learned Context Dependent Gating

• URL: http://arxiv.org/abs/2301.07187v1
• Date: Tue, 17 Jan 2023 20:52:48 GMT
• Title: Artificial Neuronal Ensembles with Learned Context Dependent Gating
• Authors: Matthew James Tilley, Michelle Miller, David Freedman
• Abstract summary: We introduce Learned Context Dependent Gating (LXDG), a method to flexibly allocate and recall artificial neuronal ensembles' Activities in the hidden layers of the network are modulated by gates, which are dynamically produced during training. We demonstrate the ability of this method to alleviate catastrophic forgetting on continual learning benchmarks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Biological neural networks are capable of recruiting different sets of neurons to encode different memories. However, when training artificial neural networks on a set of tasks, typically, no mechanism is employed for selectively producing anything analogous to these neuronal ensembles. Further, artificial neural networks suffer from catastrophic forgetting, where the network's performance rapidly deteriorates as tasks are learned sequentially. By contrast, sequential learning is possible for a range of biological organisms. We introduce Learned Context Dependent Gating (LXDG), a method to flexibly allocate and recall `artificial neuronal ensembles', using a particular network structure and a new set of regularization terms. Activities in the hidden layers of the network are modulated by gates, which are dynamically produced during training. The gates are outputs of networks themselves, trained with a sigmoid output activation. The regularization terms we have introduced correspond to properties exhibited by biological neuronal ensembles. The first term penalizes low gate sparsity, ensuring that only a specified fraction of the network is used. The second term ensures that previously learned gates are recalled when the network is presented with input from previously learned tasks. Finally, there is a regularization term responsible for ensuring that new tasks are encoded in gates that are as orthogonal as possible from previously used ones. We demonstrate the ability of this method to alleviate catastrophic forgetting on continual learning benchmarks. When the new regularization terms are included in the model along with Elastic Weight Consolidation (EWC) it achieves better performance on the benchmark `permuted MNIST' than with EWC alone. The benchmark `rotated MNIST' demonstrates how similar tasks recruit similar neurons to the artificial neuronal ensemble.

Related papers

• Decorrelating neurons using persistence [29.25969187808722]
  We present two regularisation terms computed from the weights of a minimum spanning tree of a clique. We demonstrate that naive minimisation of all correlations between neurons obtains lower accuracies than our regularisation terms. We include a proof of differentiability of our regularisers, thus developing the first effective topological persistence-based regularisation terms.
  arXiv  Detail & Related papers  (2023-08-09T11:09:14Z)
• Spike-based computation using classical recurrent neural networks [1.9171404264679484]
  Spiking neural networks are artificial neural networks in which communication between neurons is only made of events, also called spikes. We modify the dynamics of a well-known, easily trainable type of recurrent neural network to make it event-based. We show that this new network can achieve performance comparable to other types of spiking networks in the MNIST benchmark.
  arXiv  Detail & Related papers  (2023-06-06T12:19:12Z)
• How neural networks learn to classify chaotic time series [77.34726150561087]
  We study the inner workings of neural networks trained to classify regular-versus-chaotic time series. We find that the relation between input periodicity and activation periodicity is key for the performance of LKCNN models.
  arXiv  Detail & Related papers  (2023-06-04T08:53:27Z)
• Spiking neural network for nonlinear regression [68.8204255655161]
  Spiking neural networks carry the potential for a massive reduction in memory and energy consumption. They introduce temporal and neuronal sparsity, which can be exploited by next-generation neuromorphic hardware. A framework for regression using spiking neural networks is proposed.
  arXiv  Detail & Related papers  (2022-10-06T13:04:45Z)
• Consistency of Neural Networks with Regularization [0.0]
  This paper proposes the general framework of neural networks with regularization and prove its consistency. Two types of activation functions: hyperbolic function(Tanh) and rectified linear unit(ReLU) have been taken into consideration.
  arXiv  Detail & Related papers  (2022-06-22T23:33:39Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• And/or trade-off in artificial neurons: impact on adversarial robustness [91.3755431537592]
  Presence of sufficient number of OR-like neurons in a network can lead to classification brittleness and increased vulnerability to adversarial attacks. We define AND-like neurons and propose measures to increase their proportion in the network. Experimental results on the MNIST dataset suggest that our approach holds promise as a direction for further exploration.
  arXiv  Detail & Related papers  (2021-02-15T08:19:05Z)
• Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity [87.32169414230822]
  Recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) In this study the focus is drawn on searching the best-possible operator set(s) for the hidden neurons of the network based on the Synaptic Plasticity paradigm that poses the essential learning theory in biological neurons. Experimental results over highly challenging problems demonstrate that the elite ONNs even with few neurons and layers can achieve a superior learning performance than GIS-based ONNs.
  arXiv  Detail & Related papers  (2020-08-21T19:03:23Z)
• Finding trainable sparse networks through Neural Tangent Transfer [16.092248433189816]
  In deep learning, trainable sparse networks that perform well on a specific task are usually constructed using label-dependent pruning criteria. In this article, we introduce Neural Tangent Transfer, a method that instead finds trainable sparse networks in a label-free manner.
  arXiv  Detail & Related papers  (2020-06-15T08:58:01Z)
• Triple Memory Networks: a Brain-Inspired Method for Continual Learning [35.40452724755021]
  A neural network adjusts its parameters when learning a new task, but then fails to conduct the old tasks well. The brain has a powerful ability to continually learn new experience without catastrophic interference. Inspired by such brain strategy, we propose a novel approach named triple memory networks (TMNs) for continual learning.
  arXiv  Detail & Related papers  (2020-03-06T11:35:24Z)
• Non-linear Neurons with Human-like Apical Dendrite Activations [81.18416067005538]
  We show that a standard neuron followed by our novel apical dendrite activation (ADA) can learn the XOR logical function with 100% accuracy. We conduct experiments on six benchmark data sets from computer vision, signal processing and natural language processing.
  arXiv  Detail & Related papers  (2020-02-02T21:09:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.