Recognition of Abstract Objects Via Neural Oscillators: Interaction Among Topological Organization, Associative Memory and Gamma Band Synchronization

Abstract

Synchronization of neural activity in the gamma band is assumed to play a significant role not only in perceptual processing, but also in higher cognitive functions. Here, we propose a neural network of Wilson-Cowan oscillators to simulate recognition of abstract objects, each represented as a collection of four features. Features are ordered in topological maps of oscillators connected via excitatory lateral synapses, to implement a similarity principle. Experience on previous objects is stored in long-range synapses connecting the different topological maps, and trained via timing dependent Hebbian learning (previous knowledge principle). Finally, a downstream decision network detects the presence of a reliable object representation, when all features are oscillating in synchrony. Simulations performed giving various simultaneous objects to the network (from 1 to 4), with some missing and/or modified properties suggest that the network can reconstruct objects, and segment them from the other simultaneously present objects, even in case of deteriorated information, noise, and moderate correlation among the inputs (one common feature). The balance between sensitivity and specificity depends on the strength of the Hebbian learning. Achieving a correct reconstruction in all cases, however, requires ad hoc selection of the oscillation frequency. The model represents an attempt to investigate the interactions among topological maps, autoassociative memory, and gamma-band synchronization, for recognition of abstract objects.

An integrated neural model of semantic memory, lexical retrieval and category formation, based on a distributed feature representation

“…We assume that this input is the result of an upstream processing stage, that extracts the main sensorymotor properties of the objects. In previous works each object was described by a fixed number of features (four features in Ursino et al 2009)). Conversely, we now consider that the number of features can vary from one object to the next.…”

“…Recent experimental data suggest that synaptic potentiation occurs if the pre-synaptic inputs precede postsynaptic activity by 10 ms or less (Abbott and Nelson 2000;Markram et al 1997). Accordingly, in previous works Ursino et al 2009) we used a time-dependent Hebbian rule, based on the correlation between the activity in the post-synaptic unit, and the activity in the pre-synaptic unit mediated over a previous 10 ms time-window. However, this rule produces a symmetric pattern of synapses.…”

“…This rule is then modified with a forgetting factor. Rule (1)-In previous works Ursino et al 2009), we used a Hebbian rule which depends on the present value of post-synaptic activity, x ij (t), and on the moving average of the pre-synaptic activity (say m hk (t)) computed during the previous 10 ms. We define a moving average signal, reflecting the average activity during the previous 10 ms, as follows…”

“…In order to address these two requirements, in previous works we implemented a complex ''decision network'' (see Ursino et al 2009)). Conversely, in the present model we adopted a more straightforward and physiologically realistic solution.…”

“…In recent years, we developed an original model, which aspires to explore several important issues of semantic memory, laying emphasis on the possible organization of the neural units involved, and on synapse learning mechanisms Ursino et al 2009). The model assumes that objects are represented via multimodal features, encoded through a distributed representation among different cortical areas.…”