662 research outputs found
Emergence of associative learning in a neuromorphic inference network
OBJECTIVE: In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of prediction errors. While this theory has been successfully applied to cognitive processes - by modelling the activity of functional neural networks at a mesoscopic scale - the validity of the approach when modelling neurons as an ensemble of inferring agents, in a biologically plausible architecture, remained to be explored. APPROACH: We modelled a simplified cerebellar circuit with individual neurons acting as Bayesian agents to simulate the classical delayed eyeblink conditioning protocol. Neurons and synapses adjusted their activity to minimize their prediction error, which was used as the network cost function. This cerebellar network was then implemented in hardware by replicating digital neuronal elements via a low-power microcontroller. MAIN RESULTS: Persistent changes of synaptic strength - that mirrored neurophysiological observations - emerged via local (neurocentric) prediction error minimization, leading to the expression of associative learning. The same paradigm was effectively emulated in low-power hardware showing remarkably efficient performance compared to conventional neuromorphic architectures. SIGNIFICANCE: These findings show that: i) an ensemble of free energy minimizing neurons - organized in a biological plausible architecture - can recapitulate functional self-organization observed in nature, such as associative plasticity, and ii) a neuromorphic network of inference units can learn unsupervised tasks without embedding predefined learning rules in the circuit, thus providing a potential avenue to a novel form of brain-inspired artificial intelligence
Emergence of associative learning in a neuromorphic inference network
Objective. In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of prediction errors. While this theory has been successfully applied to cognitive processes-by modelling the activity of functional neural networks at a mesoscopic scale-the validity of the approach when modelling neurons as an ensemble of inferring agents, in a biologically plausible architecture, remained to be explored.Approach.We modelled a simplified cerebellar circuit with individual neurons acting as Bayesian agents to simulate the classical delayed eyeblink conditioning protocol. Neurons and synapses adjusted their activity to minimize their prediction error, which was used as the network cost function. This cerebellar network was then implemented in hardware by replicating digital neuronal elements via a low-power microcontroller.Main results. Persistent changes of synaptic strength-that mirrored neurophysiological observations-emerged via local (neurocentric) prediction error minimization, leading to the expression of associative learning. The same paradigm was effectively emulated in low-power hardware showing remarkably efficient performance compared to conventional neuromorphic architectures.Significance. These findings show that: (a) an ensemble of free energy minimizing neurons-organized in a biological plausible architecture-can recapitulate functional self-organization observed in nature, such as associative plasticity, and (b) a neuromorphic network of inference units can learn unsupervised tasks without embedding predefined learning rules in the circuit, thus providing a potential avenue to a novel form of brain-inspired artificial intelligence
Deterministic Bayesian Information Fusion and the Analysis of its Performance
This paper develops a mathematical and computational framework for analyzing
the expected performance of Bayesian data fusion, or joint statistical
inference, within a sensor network. We use variational techniques to obtain the
posterior expectation as the optimal fusion rule under a deterministic
constraint and a quadratic cost, and study the smoothness and other properties
of its classification performance. For a certain class of fusion problems, we
prove that this fusion rule is also optimal in a much wider sense and satisfies
strong asymptotic convergence results. We show how these results apply to a
variety of examples with Gaussian, exponential and other statistics, and
discuss computational methods for determining the fusion system's performance
in more general, large-scale problems. These results are motivated by studying
the performance of fusing multi-modal radar and acoustic sensors for detecting
explosive substances, but have broad applicability to other Bayesian decision
problems
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Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition
During the last decade, Bayesian probability theory has emerged as a framework in cognitive science and neuroscience for describing perception, reasoning and learning of mammals. However, our understanding of how probabilistic computations could be organized in the brain, and how the observed connectivity structure of cortical microcircuits supports these calculations, is rudimentary at best. In this study, we investigate statistical inference and self-organized learning in a spatially extended spiking network model, that accommodates both local competitive and large-scale associative aspects of neural information processing, under a unified Bayesian account. Specifically, we show how the spiking dynamics of a recurrent network with lateral excitation and local inhibition in response to distributed spiking input, can be understood as sampling from a variational posterior distribution of a well-defined implicit probabilistic model. This interpretation further permits a rigorous analytical treatment of experience-dependent plasticity on the network level. Using machine learning theory, we derive update rules for neuron and synapse parameters which equate with Hebbian synaptic and homeostatic intrinsic plasticity rules in a neural implementation. In computer simulations, we demonstrate that the interplay of these plasticity rules leads to the emergence of probabilistic local experts that form distributed assemblies of similarly tuned cells communicating through lateral excitatory connections. The resulting sparse distributed spike code of a well-adapted network carries compressed information on salient input features combined with prior experience on correlations among them. Our theory predicts that the emergence of such efficient representations benefits from network architectures in which the range of local inhibition matches the spatial extent of pyramidal cells that share common afferent input
Probabilistic analysis of the human transcriptome with side information
Understanding functional organization of genetic information is a major
challenge in modern biology. Following the initial publication of the human
genome sequence in 2001, advances in high-throughput measurement technologies
and efficient sharing of research material through community databases have
opened up new views to the study of living organisms and the structure of life.
In this thesis, novel computational strategies have been developed to
investigate a key functional layer of genetic information, the human
transcriptome, which regulates the function of living cells through protein
synthesis. The key contributions of the thesis are general exploratory tools
for high-throughput data analysis that have provided new insights to
cell-biological networks, cancer mechanisms and other aspects of genome
function.
A central challenge in functional genomics is that high-dimensional genomic
observations are associated with high levels of complex and largely unknown
sources of variation. By combining statistical evidence across multiple
measurement sources and the wealth of background information in genomic data
repositories it has been possible to solve some the uncertainties associated
with individual observations and to identify functional mechanisms that could
not be detected based on individual measurement sources. Statistical learning
and probabilistic models provide a natural framework for such modeling tasks.
Open source implementations of the key methodological contributions have been
released to facilitate further adoption of the developed methods by the
research community.Comment: Doctoral thesis. 103 pages, 11 figure
Investigation of the Sense of Agency in Social Cognition, based on frameworks of Predictive Coding and Active Inference: A simulation study on multimodal imitative interaction
When agents interact socially with different intentions, conflicts are
difficult to avoid. Although how agents can resolve such problems autonomously
has not been determined, dynamic characteristics of agency may shed light on
underlying mechanisms. The current study focused on the sense of agency (SoA),
a specific aspect of agency referring to congruence between the agent's
intention in acting and the outcome. Employing predictive coding and active
inference as theoretical frameworks of perception and action generation, we
hypothesize that regulation of complexity in the evidence lower bound of an
agent's model should affect the strength of the agent's SoA and should have a
critical impact on social interactions. We built a computational model of
imitative interaction between a robot and a human via visuo-proprioceptive
sensation with a variational Bayes recurrent neural network, and simulated the
model in the form of pseudo-imitative interaction using recorded human body
movement data. A key feature of the model is that each modality's complexity
can be regulated differently with a hyperparameter assigned to each module. We
first searched for an optimal setting that endows the model with appropriate
coordination of multimodal sensation. This revealed that the vision module's
complexity should be more tightly regulated than that of the proprioception
module. Using the optimally trained model, we examined how changing the
tightness of complexity regulation after training affects the strength of the
SoA during interactions. The results showed that with looser regulation, an
agent tends to act more egocentrically, without adapting to the other. In
contrast, with tighter regulation, the agent tends to follow the other by
adjusting its intention. We conclude that the tightness of complexity
regulation crucially affects the strength of the SoA and the dynamics of
interactions between agents.Comment: 23 pages, 8 figure
Kanerva++: extending The Kanerva Machine with differentiable, locally block allocated latent memory
Episodic and semantic memory are critical components of the human memory
model. The theory of complementary learning systems (McClelland et al., 1995)
suggests that the compressed representation produced by a serial event
(episodic memory) is later restructured to build a more generalized form of
reusable knowledge (semantic memory). In this work we develop a new principled
Bayesian memory allocation scheme that bridges the gap between episodic and
semantic memory via a hierarchical latent variable model. We take inspiration
from traditional heap allocation and extend the idea of locally contiguous
memory to the Kanerva Machine, enabling a novel differentiable block allocated
latent memory. In contrast to the Kanerva Machine, we simplify the process of
memory writing by treating it as a fully feed forward deterministic process,
relying on the stochasticity of the read key distribution to disperse
information within the memory. We demonstrate that this allocation scheme
improves performance in memory conditional image generation, resulting in new
state-of-the-art conditional likelihood values on binarized MNIST (<=41.58
nats/image) , binarized Omniglot (<=66.24 nats/image), as well as presenting
competitive performance on CIFAR10, DMLab Mazes, Celeb-A and ImageNet32x32
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