117 research outputs found
Training Spiking Neural Networks Using Lessons From Deep Learning
The brain is the perfect place to look for inspiration to develop more
efficient neural networks. The inner workings of our synapses and neurons
provide a glimpse at what the future of deep learning might look like. This
paper serves as a tutorial and perspective showing how to apply the lessons
learnt from several decades of research in deep learning, gradient descent,
backpropagation and neuroscience to biologically plausible spiking neural
neural networks. We also explore the delicate interplay between encoding data
as spikes and the learning process; the challenges and solutions of applying
gradient-based learning to spiking neural networks; the subtle link between
temporal backpropagation and spike timing dependent plasticity, and how deep
learning might move towards biologically plausible online learning. Some ideas
are well accepted and commonly used amongst the neuromorphic engineering
community, while others are presented or justified for the first time here. A
series of companion interactive tutorials complementary to this paper using our
Python package, snnTorch, are also made available:
https://snntorch.readthedocs.io/en/latest/tutorials/index.htm
Embodied neuromorphic intelligence
The design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies â from perception to motor control â represents a promising approach for the creation of robots which can seamlessly integrate in society. We present initial attempts in this direction, highlight open challenges, and propose actions required to overcome current limitations
Exploring Neuromodulatory Systems for Dynamic Learning
In a continual learning system, the network has to dynamically learn new tasks from few samples throughout its lifetime. It is observed that neuromodulation acts as a key factor in continual and dynamic learning in the central nervous system. In this work, the neuromodulatory plasticity is embedded with dynamic learning architectures. The network has an inbuilt modulatory unit that regulates learning depending on the context and the internal state of the system, thus rendering the networks with the ability to self modify their weights. In one of the proposed architectures, ModNet, a modulatory layer is introduced in a random projection framework. This layer modulates the weights of the output layer neurons in tandem with hebbian learning.
Moreover, to explore modulatory mechanisms in conjunction with backpropagation in deeper networks, a modulatory trace learning rule is introduced. The proposed learning rule, uses a time dependent trace to automatically modify the synaptic connections as a function of ongoing states and activations. The trace itself is updated via simple plasticity rules thus reducing the demand on resources. A digital architecture is proposed for ModNet, with on-device learning and resource sharing, to facilitate the efficacy of dynamic learning on the edge.
The proposed modulatory learning architecture and learning rules demonstrate the ability to learn from few samples, train quickly, and perform one shot image classification in a computationally efficient manner. The ModNet architecture achieves an accuracy of âŒ91% for image classification on the MNIST dataset while training for just 2 epochs. The deeper network with modulatory trace achieves an average accuracy of 98.8%±1.16 on the omniglot dataset for five-way one-shot image classification task. In general, incorporating neuromodulation in deep neural networks shows promise for energy and resource efficient lifelong learning systems
Harnessing function from form: towards bio-inspired artificial intelligence in neuronal substrates
Despite the recent success of deep learning, the mammalian brain is still unrivaled when it comes
to interpreting complex, high-dimensional data streams like visual, auditory and somatosensory stimuli.
However, the underlying computational principles allowing the brain to deal with unreliable, high-dimensional
and often incomplete data while having a power consumption on the order of a few watt are still mostly
unknown.
In this work, we investigate how specific functionalities emerge from simple structures observed in the
mammalian cortex, and how these might be utilized in non-von Neumann devices like âneuromorphic
hardwareâ. Firstly, we show that an ensemble of deterministic, spiking neural networks can be shaped by
a simple, local learning rule to perform sampling-based Bayesian inference. This suggests a coding scheme
where spikes (or âaction potentialsâ) represent samples of a posterior distribution, constrained by sensory
input, without the need for any source of stochasticity. Secondly, we introduce a top-down framework where
neuronal and synaptic dynamics are derived using a least action principle and gradient-based minimization.
Combined, neurosynaptic dynamics approximate real-time error backpropagation, mappable to mechanistic
components of cortical networks, whose dynamics can again be described within the proposed framework.
The presented models narrow the gap between well-defined, functional algorithms and their biophysical
implementation, improving our understanding of the computational principles the brain might employ.
Furthermore, such models are naturally translated to hardware mimicking the vastly parallel neural
structure of the brain, promising a strongly accelerated and energy-efficient implementation of powerful
learning and inference algorithms, which we demonstrate for the physical model system âBrainScaleSâ1â
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Computational models of the human visual cortex: on individual differences and ecologically valid input statistics
Perception relies on cortical processes in response to sensory stimuli. Visual input entering the
eyes ascends a cascade of processing steps from the retina to high-level regions of the cortex.
Vision science investigates these transformations that give rise to high-level processing of
visual objects, such as object recognition. In this thesis I investigate computational models
of the human visual cortex with regard to their ability to predict cortical responses to visual
objects. In particular, I describe two factors playing an important role in using deep neural
networks (DNNs) to better understand cortical functioning: the initial weight state and
ecologically more valid input statistics.
In Chapter 1 of this thesis I will introduce relevant literature pertaining to deep neural
networks as a modeling framework for the visual cortex. Next, I will lay out the motivation
for the research questions investigated in this thesis and described in detail in Chapters 2, 3,
and 4.
Chapter 2 focuses on the impact of the initial weight state of a model on its ability
to predict cortical representations. I describe work in which we demonstrate that two
DNN instances identical in every aspect but their initial weights, yield very dissimilar
representations. Relying on single network instances to predict cortical activation patterns
in response to sensory stimuli poses a problem for computational neuroscience: depending
on the initial set of weights the ability to mirror the cortical representations of these stimuli
might vary. Thus, results based on single (âoff-the-shelfâ) model instances - as commonly
used in computational neuroscience - may not generalize. In contrast, using multiple DNN
instances might alleviate this problem as they allow insights in the variability of a given
model architecture to predict cortical representations. These individual differences between
model instances suggest that to allow results to generalize more easily the model instances
should be treated similar to human experimental participants.
In Chapter 3 I focus on ecologically more valid input statistics (in the form of training
images) aiming to improve a modelâs ability to predict cortical representations. The most
successful models of the human visual cortex to date are DNNs trained on object recognition
tasks designed with machine learning goals in mind. However, the image sets used for training
these DNNs are often not ecologically realistic. For example, training on the most-widely used image set in computational neuroscience (ImageNet Large Scale Visual Recognition
Challenge (ILSVRC) 2012) requires the fine-grained distinction of 120 dog breeds, but does
not contain visual object categories encountered frequently in everyday human life (e.g.
woman, man, or child). This suggests that taking into account the human visual experience
when training models of the human visual cortex on a categorization task might help to
predict cortical representations. In this Chapter I describe the creation of a set of images
aimed at mimicking the human visual diet: ecoset. Ecoset contains more than 1.5 million
images from 565 basic level categories and is the largest image set specifically designed for
computational neuroscience to date. Ecoset is freely available to allow the community to test
their own hypotheses of models trained with input statistics matched to the human visual
environment.
In Chapter 4 we build on the results from the previous two Chapters. Using multiple
DNN instances I investigate whether a brain-inspired model architecture (vNet) trained on
ecologically more valid input statistics (ecoset) might improve its ability to predict cortical
representations. I first demonstrate that ecoset might improve an architectureâs ability to
mirror cortical representations. Furthermore, ecoset-trained vNet also outperforms state-ofthe-
art computer vision and computational neuroscience models in terms of mirroring cortical
representations in the human brain. Thus, incorporating biological and ecological aspects,
such as brain-inspired architectural features and ecologically more valid input statistics, into
computational models may yield better predictions of response patterns in the human visual
cortex.
Treating DNN instances similar to human experimental participants and considering
ecological and biological factors for building these DNNs may be an important step towards
better models of the human visual cortex. Such models might allow a better understanding of
the cortical processes underlying high-level vision in the human brain.Cambridge Trust - Vice Chancellor's Award 2015
Cambridge Philosophical Society
MRC Cognition and Brain Sciences Uni
Computational studies of genome evolution and regulation
This thesis takes on the challenge of extracting information from large volumes of biological data produced with newly established experimental techniques. The different types of information present in a particular dataset have been carefully identified to maximise the information gained from the data. This also precludes the attempts to infer the types of information that are not present in the data. In the first part of the thesis I examined the evolutionary origins of de novo taxonomically restricted genes (TRGs) in Drosophila subgenus. De novo TRGs are genes that have originated after the speciation of a particular clade from previously non-coding regions - functional ncRNA, within introns or alternative frames of older protein-coding genes, or from intergenic sequences. TRGs are clade-specific tool-kits that are likely to contain proteins with yet undocumented functions and new protein folds that are yet to be discovered. One of the main challenges in studying de novo TRGs is the trade-off between false positives (non-functional open reading frames) and false negatives (true TRGs that have properties distinct from well established genes). Here I identified two de novo TRG families in Drosophila subgenus that have not been previously reported as de novo originated genes, and to our knowledge they are the best candidates identified so far for experimental studies aimed at elucidating the properties of de novo genes. In the second part of the thesis I examined the information contained in single cell RNA sequencing (scRNA-seq) data and propose a method for extracting biological knowledge from this data using generative neural networks. The main challenge is the noisiness of scRNA-seq data - the number of transcripts sequenced is not proportional to the number of mRNAs present in the cell. I used an autoencoder to reduce the dimensionality of the data without making untestable assumptions about the data. This embedding into lower dimensional space alongside the features learned by an autoencoder contains information about the cell populations, differentiation trajectories and the regulatory relationships between the genes. Unlike most methods currently used, an autoencoder does not assume that these regulatory relationships are the same in all cells in the data set. The main advantages of our approach is that it makes minimal assumptions about the data, it is robust to noise and it is possible to assess its performance. In the final part of the thesis I summarise lessons learnt from analysing various types of biological data and make suggestions for the future direction of similar computational studies
Scalable Probabilistic Model Selection for Network Representation Learning in Biological Network Inference
A biological system is a complex network of heterogeneous molecular entities and their interactions contributing to various biological characteristics of the system. Although the biological networks not only provide an elegant theoretical framework but also offer a mathematical foundation to analyze, understand, and learn from complex biological systems, the reconstruction of biological networks is an important and unsolved problem. Current biological networks are noisy, sparse and incomplete, limiting the ability to create a holistic view of the biological reconstructions and thus fail to provide a system-level understanding of the biological phenomena. Experimental identification of missing interactions is both time-consuming and expensive. Recent advancements in high-throughput data generation and significant improvement in computational power have led to novel computational methods to predict missing interactions. However, these methods still suffer from several unresolved challenges. It is challenging to extract information about interactions and incorporate that information into the computational model. Furthermore, the biological data are not only heterogeneous but also high-dimensional and sparse presenting the difficulty of modeling from indirect measurements. The heterogeneous nature and sparsity of biological data pose significant challenges to the design of deep neural network structures which use essentially either empirical or heuristic model selection methods. These unscalable methods heavily rely on expertise and experimentation, which is a time-consuming and error-prone process and are prone to overfitting. Furthermore, the complex deep networks tend to be poorly calibrated with high confidence on incorrect predictions. In this dissertation, we describe novel algorithms that address these challenges. In Part I, we design novel neural network structures to learn representation for biological entities and further expand the model to integrate heterogeneous biological data for biological interaction prediction. In part II, we develop a novel Bayesian model selection method to infer the most plausible network structures warranted by data. We demonstrate that our methods achieve the state-of-the-art performance on the tasks across various domains including interaction prediction. Experimental studies on various interaction networks show that our method makes accurate and calibrated predictions. Our novel probabilistic model selection approach enables the network structures to dynamically evolve to accommodate incrementally available data. In conclusion, we discuss the limitations and future directions for proposed works
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