2,574 research outputs found
Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems
Neuromorphic chips embody computational principles operating in the nervous
system, into microelectronic devices. In this domain it is important to
identify computational primitives that theory and experiments suggest as
generic and reusable cognitive elements. One such element is provided by
attractor dynamics in recurrent networks. Point attractors are equilibrium
states of the dynamics (up to fluctuations), determined by the synaptic
structure of the network; a `basin' of attraction comprises all initial states
leading to a given attractor upon relaxation, hence making attractor dynamics
suitable to implement robust associative memory. The initial network state is
dictated by the stimulus, and relaxation to the attractor state implements the
retrieval of the corresponding memorized prototypical pattern. In a previous
work we demonstrated that a neuromorphic recurrent network of spiking neurons
and suitably chosen, fixed synapses supports attractor dynamics. Here we focus
on learning: activating on-chip synaptic plasticity and using a theory-driven
strategy for choosing network parameters, we show that autonomous learning,
following repeated presentation of simple visual stimuli, shapes a synaptic
connectivity supporting stimulus-selective attractors. Associative memory
develops on chip as the result of the coupled stimulus-driven neural activity
and ensuing synaptic dynamics, with no artificial separation between learning
and retrieval phases.Comment: submitted to Scientific Repor
Modeling and Simulation of Elementary Robot Behaviors using Associative Memories
International audienceToday, there are several drawbacks that impede the necessary and much needed use of robot learning techniques in real applications. First, the time needed to achieve the synthesis of any behavior is prohibitive. Second, the robot behavior during the learning phase is – by definition – bad, it may even be dangerous. Third, except within the lazy learning approach, a new behavior implies a new learning phase. We propose in this paper to use associative memories (self-organizing maps) to encode the non explicit model of the robot-world interaction sampled by the lazy memory, and then generate a robot behavior by means of situations to be achieved, i.e., points on the self-organizing maps. Any behavior can instantaneously be synthesized by the definition of a goal situation. Its performance will be minimal (not necessarily bad) and will improve by the mere repetition of the behavior
Cerebellar models of associative memory: Three papers from IEEE COMPCON spring 1989
Three papers are presented on the following topics: (1) a cerebellar-model associative memory as a generalized random-access memory; (2) theories of the cerebellum - two early models of associative memory; and (3) intelligent network management and functional cerebellum synthesis
Making Neural QA as Simple as Possible but not Simpler
Recent development of large-scale question answering (QA) datasets triggered
a substantial amount of research into end-to-end neural architectures for QA.
Increasingly complex systems have been conceived without comparison to simpler
neural baseline systems that would justify their complexity. In this work, we
propose a simple heuristic that guides the development of neural baseline
systems for the extractive QA task. We find that there are two ingredients
necessary for building a high-performing neural QA system: first, the awareness
of question words while processing the context and second, a composition
function that goes beyond simple bag-of-words modeling, such as recurrent
neural networks. Our results show that FastQA, a system that meets these two
requirements, can achieve very competitive performance compared with existing
models. We argue that this surprising finding puts results of previous systems
and the complexity of recent QA datasets into perspective
Learning the Pseudoinverse Solution to Network Weights
The last decade has seen the parallel emergence in computational neuroscience
and machine learning of neural network structures which spread the input signal
randomly to a higher dimensional space; perform a nonlinear activation; and
then solve for a regression or classification output by means of a mathematical
pseudoinverse operation. In the field of neuromorphic engineering, these
methods are increasingly popular for synthesizing biologically plausible neural
networks, but the "learning method" - computation of the pseudoinverse by
singular value decomposition - is problematic both for biological plausibility
and because it is not an online or an adaptive method. We present an online or
incremental method of computing the pseudoinverse, which we argue is
biologically plausible as a learning method, and which can be made adaptable
for non-stationary data streams. The method is significantly more
memory-efficient than the conventional computation of pseudoinverses by
singular value decomposition.Comment: 13 pages, 3 figures; in submission to Neural Network
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