Frontiers in Computational Neuroscience (Jan 2011)
A compositionality machine realized by a hierarchic architecture of synfire chains
Abstract
The composition of complex behavior is thought to rely on the concurrent and sequentialactivation of simpler action components, or primitives. Systems of synfire chains have previouslybeen proposed to account for either the simultaneous or the sequential aspects ofcompositionality; however, the compatibility of the two aspects has so far not been addressed.Moreover, the simultaneous activation of primitives has up until now only beeninvestigated in the context of reactive computations, i.e. the perception of stimuli. In thisstudy we demonstrate how a hierarchical organization of synfire chains is capable of generatingboth aspects of compositionality for proactive computations such as the generationof complex and ongoing action. To this end, we develop a network model consisting oftwo layers of synfire chains. Using simple drawing strokes as a visualization of abstractprimitives, we map the feed-forward activity of the upper level synfire chains to motion intwo-dimensional space. Our model is capable of producing drawing strokes that are combinationsof primitive strokes by binding together the corresponding chains. Moreover, whenthe lower layer of the network is constructed in a closed-loop fashion, drawing strokes aregenerated sequentially. The generated pattern can be random or deterministic, dependingon the connection pattern between the lower level chains. We propose quantitative measuresfor simultaneity and sequentiality, revealing a wide parameter range in which bothaspects are fulfilled. Finally, we investigate the spiking activity of our model to proposecandidate signatures of synfire chain computation in measurements of neural activity duringaction execution.
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