1,309 research outputs found
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
Artificial autonomous agents and robots interacting in complex environments
are required to continually acquire and fine-tune knowledge over sustained
periods of time. The ability to learn from continuous streams of information is
referred to as lifelong learning and represents a long-standing challenge for
neural network models due to catastrophic forgetting. Computational models of
lifelong learning typically alleviate catastrophic forgetting in experimental
scenarios with given datasets of static images and limited complexity, thereby
differing significantly from the conditions artificial agents are exposed to.
In more natural settings, sequential information may become progressively
available over time and access to previous experience may be restricted. In
this paper, we propose a dual-memory self-organizing architecture for lifelong
learning scenarios. The architecture comprises two growing recurrent networks
with the complementary tasks of learning object instances (episodic memory) and
categories (semantic memory). Both growing networks can expand in response to
novel sensory experience: the episodic memory learns fine-grained
spatiotemporal representations of object instances in an unsupervised fashion
while the semantic memory uses task-relevant signals to regulate structural
plasticity levels and develop more compact representations from episodic
experience. For the consolidation of knowledge in the absence of external
sensory input, the episodic memory periodically replays trajectories of neural
reactivations. We evaluate the proposed model on the CORe50 benchmark dataset
for continuous object recognition, showing that we significantly outperform
current methods of lifelong learning in three different incremental learning
scenario
Birth of a Learning Law
Defense Advanced Research Projects Agency; Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657, N00014-92-J-1309
Reading as Active Sensing: A Computational Model of Gaze Planning in Word Recognition
We offer a computational model of gaze planning during reading that consists of two main components: a lexical representation network, acquiring lexical representations from input texts (a subset of the Italian CHILDES database), and a gaze planner, designed to recognize written words by mapping strings of characters onto lexical representations. The model implements an active sensing strategy that selects which characters of the input string are to be fixated, depending on the predictions dynamically made by the lexical representation network. We analyze the developmental trajectory of the system in performing the word recognition task as a function of both increasing lexical competence, and correspondingly increasing lexical prediction ability. We conclude by discussing how our approach can be scaled up in the context of an active sensing strategy applied to a robotic setting
Self-Organization of Spiking Neural Networks for Visual Object Recognition
On one hand, the visual system has the ability to differentiate between very similar
objects. On the other hand, we can also recognize the same object in images that vary
drastically, due to different viewing angle, distance, or illumination. The ability to
recognize the same object under different viewing conditions is called invariant object
recognition. Such object recognition capabilities are not immediately available after
birth, but are acquired through learning by experience in the visual world.
In many viewing situations different views of the same object are seen in a tem-
poral sequence, e.g. when we are moving an object in our hands while watching it.
This creates temporal correlations between successive retinal projections that can be
used to associate different views of the same object. Theorists have therefore pro-
posed a synaptic plasticity rule with a built-in memory trace (trace rule).
In this dissertation I present spiking neural network models that offer possible
explanations for learning of invariant object representations. These models are based
on the following hypotheses:
1. Instead of a synaptic trace rule, persistent firing of recurrently connected groups
of neurons can serve as a memory trace for invariance learning.
2. Short-range excitatory lateral connections enable learning of self-organizing
topographic maps that represent temporal as well as spatial correlations.
3. When trained with sequences of object views, such a network can learn repre-
sentations that enable invariant object recognition by clustering different views
of the same object within a local neighborhood.
4. Learning of representations for very similar stimuli can be enabled by adaptive
inhibitory feedback connections.
The study presented in chapter 3.1 details an implementation of a spiking neural
network to test the first three hypotheses. This network was tested with stimulus
sets that were designed in two feature dimensions to separate the impact of tempo-
ral and spatial correlations on learned topographic maps. The emerging topographic
maps showed patterns that were dependent on the temporal order of object views
during training. Our results show that pooling over local neighborhoods of the to-
pographic map enables invariant recognition.
Chapter 3.2 focuses on the fourth hypothesis. There we examine how the adaptive
feedback inhibition (AFI) can improve the ability of a network to discriminate between
very similar patterns. The results show that with AFI learning is faster, and the
network learns selective representations for stimuli with higher levels of overlap
than without AFI.
Results of chapter 3.1 suggest a functional role for topographic object representa-
tions that are known to exist in the inferotemporal cortex, and suggests a mechanism
for the development of such representations. The AFI model implements one aspect
of predictive coding: subtraction of a prediction from the actual input of a system. The
successful implementation in a biologically plausible network of spiking neurons
shows that predictive coding can play a role in cortical circuits
Extracting finite structure from infinite language
This paper presents a novel connectionist memory-rule based model capable of learning the finite-state properties of an input language from a set of positive examples. The model is based upon an unsupervised recurrent self-organizing map [T. McQueen, A. Hopgood, J. Tepper, T. Allen, A recurrent self-organizing map for temporal sequence processing, in: Proceedings of Fourth International Conference in Recent Advances in Soft Computing (RASC2002), Nottingham, 2002] with laterally interconnected neurons. A derivation of functionalequivalence theory [J. Hopcroft, J. Ullman, Introduction to Automata Theory, Languages and Computation, vol. 1, Addison-Wesley, Reading, MA, 1979] is used that allows the model to exploit similarities between the future context of previously memorized sequences and the future context of the current input sequence. This bottom-up learning algorithm binds functionally related neurons together to form states. Results show that the model is able to learn the Reber grammar [A. Cleeremans, D. Schreiber, J. McClelland, Finite state automata and simple recurrent networks, Neural Computation, 1 (1989) 372–381] perfectly from a randomly generated training set and to generalize to sequences beyond the length of those found in the training set
Evolutionary Neural Gas (ENG): A Model of Self Organizing Network from Input Categorization
Despite their claimed biological plausibility, most self organizing networks
have strict topological constraints and consequently they cannot take into
account a wide range of external stimuli. Furthermore their evolution is
conditioned by deterministic laws which often are not correlated with the
structural parameters and the global status of the network, as it should happen
in a real biological system. In nature the environmental inputs are noise
affected and fuzzy. Which thing sets the problem to investigate the possibility
of emergent behaviour in a not strictly constrained net and subjected to
different inputs. It is here presented a new model of Evolutionary Neural Gas
(ENG) with any topological constraints, trained by probabilistic laws depending
on the local distortion errors and the network dimension. The network is
considered as a population of nodes that coexist in an ecosystem sharing local
and global resources. Those particular features allow the network to quickly
adapt to the environment, according to its dimensions. The ENG model analysis
shows that the net evolves as a scale-free graph, and justifies in a deeply
physical sense- the term gas here used.Comment: 16 pages, 8 figure
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