Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks

A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artificial neural networks (ANNs), which have fueled most recent advances in AI. A recent paper proposed that catastrophic forgetting in ANNs can be reduced by promoting modularity, which can limit forgetting by isolating task information to specific clusters of nodes and connections (functional modules). While the prior work did show that modular ANNs suffered less from catastrophic forgetting, it was not able to produce ANNs that possessed task-specific functional modules, thereby leaving the main theory regarding modularity and forgetting untested. We introduce diffusion-based neuromodulation, which simulates the release of diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up or down regulate) learning in a spatial region. On the simple diagnostic problem from the prior work, diffusion-based neuromodulation 1) induces task-specific learning in groups of nodes and connections (task-specific localized learning), which 2) produces functional modules for each subtask, and 3) yields higher performance by eliminating catastrophic forgetting. Overall, our results suggest that diffusion-based neuromodulation promotes task-specific localized learning and functional modularity, which can help solve the challenging, but important problem of catastrophic forgetting

SeNA-CNN: Overcoming Catastrophic Forgetting in Convolutional Neural Networks by Selective Network Augmentation

Lifelong learning aims to develop machine learning systems that can learn new tasks while preserving the performance on previous learned tasks. In this paper we present a method to overcome catastrophic forgetting on convolutional neural networks, that learns new tasks and preserves the performance on old tasks without accessing the data of the original model, by selective network augmentation. The experiment results showed that SeNA-CNN, in some scenarios, outperforms the state-of-art Learning without Forgetting algorithm. Results also showed that in some situations it is better to use SeNA-CNN instead of training a neural network using isolated learning.info:eu-repo/semantics/publishedVersio

NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways.

While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contextual state. Here, we demonstrate that dendritic N-Methyl-D-Aspartate spikes can, within physiological constraints, implement contextual modulation of feedforward processing. Such neuron-specific modulations exploit prior knowledge, encoded in stable feedforward weights, to achieve transfer learning across contexts. In a network of biophysically realistic neuron models with context-independent feedforward weights, we show that modulatory inputs to dendritic branches can solve linearly nonseparable learning problems with a Hebbian, error-modulated learning rule. We also demonstrate that local prediction of whether representations originate either from different inputs, or from different contextual modulations of the same input, results in representation learning of hierarchical feedforward weights across processing layers that accommodate a multitude of contexts

A Review of Findings from Neuroscience and Cognitive Psychology as Possible Inspiration for the Path to Artificial General Intelligence

This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by deep learning models in various domains, they still have shortcomings in abstract reasoning and causal understanding. Such capabilities should be ultimately integrated into artificial intelligence systems in order to surpass data-driven limitations and support decision making in a way more similar to human intelligence. This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures. The hope is that these concepts may offer insights for solutions in artificial general intelligence.Comment: 143 pages, 49 figures, 244 reference

Contributions of synaptic filters to models of synaptically stored memory

The question of how neural systems encode memories in one-shot without immediately disrupting previously stored information has puzzled theoretical neuroscientists for years and it is the central topic of this thesis. Previous attempts on this topic, have proposed that synapses probabilistically update in response to plasticity inducing stimuli to effectively delay the degradation of old memories in the face of ongoing memory storage. Indeed, experiments have shown that synapses do not immediately respond to plasticity inducing stimuli, since these must be presented many times before synaptic plasticity is expressed. Such a delay could be due to the stochastic nature of synaptic plasticity or perhaps because induction signals are integrated before overt strength changes occur.The later approach has been previously applied to control fluctuations in neural development by low-pass filtering induction signals before plasticity is expressed. In this thesis we consider memory dynamics in a mathematical model with synapses that integrate plasticity induction signals to a threshold before expressing plasticity. We report novel recall dynamics and considerable improvements in memory lifetimes against a prominent model of synaptically stored memory. With integrating synapses the memory trace initially rises before reaching a maximum and then falls. The memory signal dissociates into separate oblivescence and reminiscence components, with reminiscence initially dominating recall. Furthermore, we find that integrating synapses possess natural timescales that can be used to consider the transition to late-phase plasticity under spaced repetition patterns known to lead to optimal storage conditions. We find that threshold crossing statistics differentiate between massed and spaced memory repetition patterns. However, isolated integrative synapses obtain an insufficient statistical sample to detect the stimulation pattern within a few memory repetitions. We extend the modelto consider the cooperation of well-known intracellular signalling pathways in detecting storage conditions by utilizing the profile of postsynaptic depolarization. We find that neuron wide signalling and local synaptic signals can be combined to detect optimal storage conditions that lead to stable forms of plasticity in a synapse specific manner.These models can be further extended to consider heterosynaptic and neuromodulatory interactions for late-phase plasticity.<br/

Roadmap on Machine learning in electronic structure

AbstractIn recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century

Inference And Learning In Spiking Neural Networks For Neuromorphic Systems

Neuromorphic computing is a computing field that takes inspiration from the biological and physical characteristics of the neocortex system to motivate a new paradigm of highly parallel and distributed computing to take on the demands of the ever-increasing scale and computational complexity of machine intelligence esp. in energy-limited systems such as Edge devices, Internet-of-Things (IOT), and cyber physical systems (CPS). Spiking neural network (SNN) is often studied together with neuromorphic computing as the underlying computational model . Similar to the biological neural system, SNN is an inherently dynamic and stateful network. The state and output of SNN do not only dependent on the current input, but also dependent on the history information. Another distinct property of SNN is that the information is represented, transmitted, and processed as discrete spike events, also referred to as action potentials. All the processing happens in the neurons such that the computation itself is massively distributed and parallel. This enables low power information transmission and processing. However, it is inefficient to implement SNNs on traditional Von Neumann architecture due to the performance gap between memory and processor. This has led to the advent of energy-efficient large-scale neuromorphic hardware such as IBM\u27s TrueNorth and Intel\u27s Loihi that enables low power implementation of large-scale neural networks for real-time applications. And although spiking networks have theoretically been shown to have Turing-equivalent computing power, it remains a challenge to train deep SNNs; the threshold functions that generate spikes are discontinuous, so they do not have derivatives and cannot directly utilize gradient-based optimization algorithms for training. Biologically plausible learning mechanism spike-timing-dependent plasticity (STDP) and its variants are local in synapses and time but are unstable during training and difficult to train multi-layer SNNs. To better exploit the energy-saving features such as spike domain representation and stochastic computing provided by SNNs in neuromorphic hardware, and to address the hardware limitations such as limited data precision and neuron fan-in/fan-out constraints, it is necessary to re-design a neural network including its structure and computing. Our work focuses on low-level (activations, weights) and high-level (alternative learning algorithms) redesign techniques to enable inference and learning with SNNs in neuromorphic hardware. First, we focused on transforming a trained artificial neural network (ANN) to a form that is suitable for neuromorphic hardware implementation. Here, we tackle transforming Long Short-Term Memory (LSTM), a version of recurrent neural network (RNN) which includes recurrent connectivity to enable learning long temporal patterns. This is specifically a difficult challenge due to the inherent nature of RNNs and SNNs; the recurrent connectivity in RNNs induces temporal dynamics which require synchronicity, especially with the added complexity of LSTMs; and SNNs are asynchronous in nature. In addition, the constraints of the neuromorphic hardware provided a massive challenge for this realization. Thus, in this work, we invented a store-and-release circuit using integrate-and-fire neurons which allows the synchronization and then developed modules using that circuit to replicate various parts of the LSTM. These modules enabled implementation of LSTMs with spiking neurons on IBM’s TrueNorth Neurosynaptic processor. This is the first work to realize such LSTM networks utilizing spiking neurons and implement on a neuromorphic hardware. This opens avenues for the use of neuromorphic hardware in applications involving temporal patterns. Moving from mapping a pretrained ANN, we work on training networks on the neuromorphic hardware. Here, we first looked at the biologically plausible learning algorithm called STDP which is a Hebbian learning rule for learning without supervision. Simplified computational interpretations of STDP is either unstable and/or complex such that it is costly to implement on hardware. Thus, in this work, we proposed a stable version of STDP and applied intentional approximations for low-cost hardware implementation called Quantized 2-Power Shift (Q2PS) rule. With this version, we performed both unsupervised learning for feature extraction and supervised learning for classification in a multilayer SNN to achieve comparable to better accuracy on MNIST dataset compared to manually labelled two-layered networks. Next, we approached training multilayer SNNs on a neuromorphic hardware with backpropagation, a gradient-based optimization algorithm that forms the backbone of deep neural networks (DNN). Although STDP is biologically plausible, its not as robust for learning deep networks as backpropagation is for DNNs. However, backpropagation is not biologically plausible and not suitable to be directly applied to SNNs, neither can it be implemented on a neuromorphic hardware. Thus, in the first part of this work, we devise a set of approximations to transform backprogation to the spike domain such that it is suitable for SNNs. After the set of approximations, we adapted the connectivity and weight update rule in backpropagation to enable learning solely based on the locally available information such that it resembled a rate-based STDP algorithm. We called this Error-Modulated STDP (EMSTDP). In the next part of this work, we implemented EMSTDP on Intel\u27s Loihi neuromorphic chip to realize online in-hardware supervised learning of deep SNNs. This is the first realization of a fully spike-based approximation of backpropagation algorithm implemented on a neuromorphic processor. This is the first step towards building an autonomous machine that learns continuously from its environment and experiences

Inference and Learning in Spiking Neural Networks for Neuromorphic Systems

Neuromorphic computing is a computing field that takes inspiration from the biological and physical characteristics of the neocortex system to motivate a new paradigm of highly parallel and distributed computing to take on the demands of the ever-increasing scale and computational complexity of machine intelligence esp. in energy-limited systems such as Edge devices, Internet-of-Things (IOT), and cyber physical systems (CPS). Spiking neural network (SNN) is often studied together with neuromorphic computing as the underlying computational model . Similar to the biological neural system, SNN is an inherently dynamic and stateful network. The state and output of SNN do not only dependent on the current input, but also dependent on the history information. Another distinct property of SNN is that the information is represented, transmitted, and processed as discrete spike events, also referred to as action potentials. All the processing happens in the neurons such that the computation itself is massively distributed and parallel. This enables low power information transmission and processing. However, it is inefficient to implement SNNs on traditional Von Neumann architecture due to the performance gap between memory and processor. This has led to the advent of energy-efficient large-scale neuromorphic hardware such as IBM\u27s TrueNorth and Intel\u27s Loihi that enables low power implementation of large-scale neural networks for real-time applications. And although spiking networks have theoretically been shown to have Turing-equivalent computing power, it remains a challenge to train deep SNNs; the threshold functions that generate spikes are discontinuous, so they do not have derivatives and cannot directly utilize gradient-based optimization algorithms for training. Biologically plausible learning mechanism spike-timing-dependent plasticity (STDP) and its variants are local in synapses and time but are unstable during training and difficult to train multi-layer SNNs. To better exploit the energy-saving features such as spike domain representation and stochastic computing provided by SNNs in neuromorphic hardware, and to address the hardware limitations such as limited data precision and neuron fan-in/fan-out constraints, it is necessary to re-design a neural network including its structure and computing. Our work focuses on low-level (activations, weights) and high-level (alternative learning algorithms) redesign techniques to enable inference and learning with SNNs in neuromorphic hardware. First, we focused on transforming a trained artificial neural network (ANN) to a form that is suitable for neuromorphic hardware implementation. Here, we tackle transforming Long Short-Term Memory (LSTM), a version of recurrent neural network (RNN) which includes recurrent connectivity to enable learning long temporal patterns. This is specifically a difficult challenge due to the inherent nature of RNNs and SNNs; the recurrent connectivity in RNNs induces temporal dynamics which require synchronicity, especially with the added complexity of LSTMs; and SNNs are asynchronous in nature. In addition, the constraints of the neuromorphic hardware provided a massive challenge for this realization. Thus, in this work, we invented a store-and-release circuit using integrate-and-fire neurons which allows the synchronization and then developed modules using that circuit to replicate various parts of the LSTM. These modules enabled implementation of LSTMs with spiking neurons on IBM\u27s TrueNorth Neurosynaptic processor. This is the first work to realize such LSTM networks utilizing spiking neurons and implement on a neuromorphic hardware. This opens avenues for the use of neuromorphic hardware in applications involving temporal patterns. Moving from mapping a pretrained ANN, we work on training networks on the neuromorphic hardware. Here, we first looked at the biologically plausible learning algorithm called STDP which is a Hebbian learning rule for learning without supervision. Simplified computational interpretations of STDP is either unstable and/or complex such that it is costly to implement on hardware. Thus, in this work, we proposed a stable version of STDP and applied intentional approximations for low-cost hardware implementation called Quantized 2-Power Shift (Q2PS) rule. With this version, we performed both unsupervised learning for feature extraction and supervised learning for classification in a multilayer SNN to achieve comparable to better accuracy on MNIST dataset compared to manually labelled two-layered networks. Next, we approached training multilayer SNNs on a neuromorphic hardware with backpropagation, a gradient-based optimization algorithm that forms the backbone of deep neural networks (DNN). Although STDP is biologically plausible, its not as robust for learning deep networks as backpropagation is for DNNs. However, backpropagation is not biologically plausible and not suitable to be directly applied to SNNs, neither can it be implemented on a neuromorphic hardware. Thus, in the first part of this work, we devise a set of approximations to transform backprogation to the spike domain such that it is suitable for SNNs. After the set of approximations, we adapted the connectivity and weight update rule in backpropagation to enable learning solely based on the locally available information such that it resembled a rate-based STDP algorithm. We called this Error-Modulated STDP (EMSTDP). In the next part of this work, we implemented EMSTDP on Intel\u27s Loihi neuromorphic chip to realize online in-hardware supervised learning of deep SNNs. This is the first realization of a fully spike-based approximation of backpropagation algorithm implemented on a neuromorphic processor. This is the first step towards building an autonomous machine that learns continuously from its environment and experiences

Roadmap on machine learning in electronic structure

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century