1,168 research outputs found
Evolution of network structure by temporal learning
We study the effect of learning dynamics on network topology. A network of
discrete dynamical systems is considered for this purpose and the coupling
strengths are made to evolve according to a temporal learning rule that is
based on the paradigm of spike-time-dependent plasticity. This incorporates
necessary competition between different edges. The final network we obtain is
robust and has a broad degree distribution.Comment: revised manuscript in communicatio
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Neurons and symbols: a manifesto
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of
neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty
NMDA-based pattern discrimination in a modeled cortical neuron
Compartmental simulations of an anatomically characterized cortical pyramidal cell were carried out to study the integrative behavior of a complex dendritic tree. Previous theoretical (Feldman and Ballard 1982; Durbin and Rumelhart 1989; Mel 1990; Mel and Koch 1990; Poggio and Girosi 1990) and compartmental modeling (Koch et al. 1983; Shepherd et al. 1985; Koch and Poggio 1987; Rall and Segev 1987; Shepherd and Brayton 1987; Shepherd et al. 1989; Brown et al. 1991) work had suggested that multiplicative interactions among groups of neighboring synapses could greatly enhance the processing power of a neuron relative to a unit with only a single global firing threshold. This issue was investigated here, with a particular focus on the role of voltage-dependent N-methyl-D-asparate (NMDA) channels in the generation of cell responses. First, it was found that when a large proportion of the excitatory synaptic input to dendritic spines is carried by NMDA channels, the pyramidal cell responds preferentially to spatially clustered, rather than random, distributions of activated synapses. Second, based on this mechanism, the NMDA-rich neuron is shown to be capable of solving a nonlinear pattern discrimination task. We propose that manipulation of the spatial ordering of afferent synaptic connections onto the dendritic arbor is a possible biological strategy for pattern information storage during learning
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Value-based argumentation frameworks as neural-symbolic learning systems
While neural networks have been successfully used in a number of machine learning applications, logical languages have been the standard for the representation of argumentative reasoning. In this paper, we establish a relationship between neural networks and argumentation networks, combining reasoning and learning in the same argumentation framework. We do so by presenting a new neural argumentation algorithm, responsible for translating argumentation networks into standard neural networks. We then show a correspondence between the two networks. The algorithm works not only for acyclic argumentation networks, but also for circular networks, and it enables the accrual of arguments through learning as well as the parallel computation of arguments
Robust short-term memory without synaptic learning
Short-term memory in the brain cannot in general be explained the way
long-term memory can -- as a gradual modification of synaptic weights -- since
it takes place too quickly. Theories based on some form of cellular
bistability, however, do not seem able to account for the fact that noisy
neurons can collectively store information in a robust manner. We show how a
sufficiently clustered network of simple model neurons can be instantly induced
into metastable states capable of retaining information for a short time (a few
seconds). The mechanism is robust to different network topologies and kinds of
neural model. This could constitute a viable means available to the brain for
sensory and/or short-term memory with no need of synaptic learning. Relevant
phenomena described by neurobiology and psychology, such as local
synchronization of synaptic inputs and power-law statistics of forgetting
avalanches, emerge naturally from this mechanism, and we suggest possible
experiments to test its viability in more biological settings.Comment: 20 pages, 9 figures. Amended to include section on spiking neurons,
with general rewrit
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