A Compositionality Machine Realized by a Hierarchic Architecture of Synfire Chains

The composition of complex behavior is thought to rely on the concurrent and sequential activation of simpler action components, or primitives. Systems of synfire chains have previously been proposed to account for either the simultaneous or the sequential aspects of compositionality; however, the compatibility of the two aspects has so far not been addressed. Moreover, the simultaneous activation of primitives has up until now only been investigated in the context of reactive computations, i.e., the perception of stimuli. In this study we demonstrate how a hierarchical organization of synfire chains is capable of generating both aspects of compositionality for proactive computations such as the generation of complex and ongoing action. To this end, we develop a network model consisting of two layers of synfire chains. Using simple drawing strokes as a visualization of abstract primitives, we map the feed-forward activity of the upper level synfire chains to motion in two-dimensional space. Our model is capable of producing drawing strokes that are combinations of primitive strokes by binding together the corresponding chains. Moreover, when the lower layer of the network is constructed in a closed-loop fashion, drawing strokes are generated sequentially. The generated pattern can be random or deterministic, depending on the connection pattern between the lower level chains. We propose quantitative measures for simultaneity and sequentiality, revealing a wide parameter range in which both aspects are fulfilled. Finally, we investigate the spiking activity of our model to propose candidate signatures of synfire chain computation in measurements of neural activity during action execution

Is a 4-bit synaptic weight resolution enough? - Constraints on enabling spike-timing dependent plasticity in neuromorphic hardware

Large-scale neuromorphic hardware systems typically bear the trade-off between detail level and required chip resources. Especially when implementing spike-timing-dependent plasticity, reduction in resources leads to limitations as compared to floating point precision. By design, a natural modification that saves resources would be reducing synaptic weight resolution. In this study, we give an estimate for the impact of synaptic weight discretization on different levels, ranging from random walks of individual weights to computer simulations of spiking neural networks. The FACETS wafer-scale hardware system offers a 4-bit resolution of synaptic weights, which is shown to be sufficient within the scope of our network benchmark. Our findings indicate that increasing the resolution may not even be useful in light of further restrictions of customized mixed-signal synapses. In addition, variations due to production imperfections are investigated and shown to be uncritical in the context of the presented study. Our results represent a general framework for setting up and configuring hardware-constrained synapses. We suggest how weight discretization could be considered for other backends dedicated to large-scale simulations. Thus, our proposition of a good hardware verification practice may rise synergy effects between hardware developers and neuroscientists