199,484 research outputs found
Emergence and reconfiguration of modular structure for synaptic neural networks during continual familiarity detection
While advances in artificial intelligence and neuroscience have enabled the
emergence of neural networks capable of learning a wide variety of tasks, our
understanding of the temporal dynamics of these networks remains limited. Here,
we study the temporal dynamics during learning of Hebbian Feedforward (HebbFF)
neural networks in tasks of continual familiarity detection. Drawing
inspiration from the field of network neuroscience, we examine the network's
dynamic reconfiguration, focusing on how network modules evolve throughout
learning. Through a comprehensive assessment involving metrics like network
accuracy, modular flexibility, and distribution entropy across diverse learning
modes, our approach reveals various previously unknown patterns of network
reconfiguration. In particular, we find that the emergence of network
modularity is a salient predictor of performance, and that modularization
strengthens with increasing flexibility throughout learning. These insights not
only elucidate the nuanced interplay of network modularity, accuracy, and
learning dynamics but also bridge our understanding of learning in artificial
and biological realms
Dreaming neural networks: forgetting spurious memories and reinforcing pure ones
The standard Hopfield model for associative neural networks accounts for
biological Hebbian learning and acts as the harmonic oscillator for pattern
recognition, however its maximal storage capacity is , far
from the theoretical bound for symmetric networks, i.e. . Inspired
by sleeping and dreaming mechanisms in mammal brains, we propose an extension
of this model displaying the standard on-line (awake) learning mechanism (that
allows the storage of external information in terms of patterns) and an
off-line (sleep) unlearningconsolidating mechanism (that allows
spurious-pattern removal and pure-pattern reinforcement): this obtained daily
prescription is able to saturate the theoretical bound , remaining
also extremely robust against thermal noise. Both neural and synaptic features
are analyzed both analytically and numerically. In particular, beyond obtaining
a phase diagram for neural dynamics, we focus on synaptic plasticity and we
give explicit prescriptions on the temporal evolution of the synaptic matrix.
We analytically prove that our algorithm makes the Hebbian kernel converge with
high probability to the projection matrix built over the pure stored patterns.
Furthermore, we obtain a sharp and explicit estimate for the "sleep rate" in
order to ensure such a convergence. Finally, we run extensive numerical
simulations (mainly Monte Carlo sampling) to check the approximations
underlying the analytical investigations (e.g., we developed the whole theory
at the so called replica-symmetric level, as standard in the
Amit-Gutfreund-Sompolinsky reference framework) and possible finite-size
effects, finding overall full agreement with the theory.Comment: 31 pages, 12 figure
SORN: A Self-Organizing Recurrent Neural Network
Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing circuits that learn appropriate representations for time-varying sensory stimuli. However, it has been difficult to mimic these abilities in artificial neural network models. Here we introduce SORN, a self-organizing recurrent network. It combines three distinct forms of local plasticity to learn spatio-temporal patterns in its input while maintaining its dynamics in a healthy regime suitable for learning. The SORN learns to encode information in the form of trajectories through its high-dimensional state space reminiscent of recent biological findings on cortical coding. All three forms of plasticity are shown to be essential for the network's success
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Learning Algorithms For Biologically Plausible Recurrent Neural Networks
Neural networks underlie a wide range of ongoing research in the fields of Applied Mathe- matics, Statistics, and Computer Science. Originally inspired by early observations of biological neurons, these structures are capable of performing complex tasks despite having fairly simple in- dividual components. Since their original conception, the study of neural networks has revealed many categories of relevant network structures for modeling and solving statistical problems, some of which have been found to excel at particular tasks like classification and prediction once appropri- ate weights have been learned. Accordingly, determining ideal network architecture and methods by which weights (network connections) can be trained are fundamental problems in this field. Many have observed that the similarities between artificial neural networks and biological neural networks essentially end with biology inspiring the artificial, but this need not be the case. By considering artificial neural networks that share topological network features with their biological counterparts, we work incrementally towards development of models of how biological learning might occur. In this work, we reproduce several prior results of novel learning techniques applied to recurrent neural networks, demonstrating the ability of FORCE learning to reproduce patterns in both input driven and input-free environments and the EMPJ method of translating low dimensional dynamics into a higher dimensional recurrent neural network. We then extend those results, exploring whether FORCE can learn to perform a simple working memory task, if EMPJ works for one dimensional dynamics, and consider how these structures and methods fit into the overall goal of achieving biologically plausible recurrent neural networks, with features that are entirely in agreement with biological neural networks. That is to say, networks that respect Dale’s law, are highly recurrent, do not rely on global knowledge, and are able to develop coherent patterns amidst the time delayed chaotic instabilities previously observed in highly recurrently connected networks.</p
Learning
Learning and evolution are adaptive or “backward-looking” models of social and biological systems. Learning changes the probability distribution of traits within an individual through direct and vicarious reinforcement, while evolution changes the probability distribution of traits within a population through reproduction and selection. Compared to forward-looking models of rational calculation that identify equilibrium outcomes, adaptive models pose fewer cognitive requirements and reveal both equilibrium and out-of-equilibrium dynamics. However, they are also less general than analytical models and require relatively stable environments. In this chapter, we review the conceptual and practical foundations of several approaches to models of learning that offer powerful tools for modeling social processes. These include the Bush-Mosteller stochastic learning model, the Roth-Erev matching model, feed-forward and attractor neural networks, and belief learning. Evolutionary approaches include replicator dynamics and genetic algorithms. A unifying theme is showing how complex patterns can arise from relatively simple adaptive rules.</p
In search of dispersed memories: Generative diffusion models are associative memory networks
Uncovering the mechanisms behind long-term memory is one of the most
fascinating open problems in neuroscience and artificial intelligence.
Artificial associative memory networks have been used to formalize important
aspects of biological memory. Generative diffusion models are a type of
generative machine learning techniques that have shown great performance in
many tasks. Like associative memory systems, these networks define a dynamical
system that converges to a set of target states. In this work we show that
generative diffusion models can be interpreted as energy-based models and that,
when trained on discrete patterns, their energy function is (asymptotically)
identical to that of modern Hopfield networks. This equivalence allows us to
interpret the supervised training of diffusion models as a synaptic learning
process that encodes the associative dynamics of a modern Hopfield network in
the weight structure of a deep neural network. Leveraging this connection, we
formulate a generalized framework for understanding the formation of long-term
memory, where creative generation and memory recall can be seen as parts of a
unified continuum
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