Abstract
The pattern of connections among cortical excitatory cells with overlapping arbors is non-random. In particular, correlations among connections produce clustering – cells in cliques connect to each other with high probability, but with lower probability to cells in other spatially intertwined cliques. In this study, we model initially randomly connected sparse recurrent networks of spiking neurons with random, overlapping inputs, to investigate what functional and structural synaptic plasticity mechanisms sculpt network connections into the patterns measured in vitro. Our Hebbian implementation of structural plasticity causes a removal of connections between uncorrelated excitatory cells, followed by their random replacement. To model a biconditional discrimination task, we stimulate the network via pairs (A + B, C + D, A + D, and C + B) of four inputs (A, B, C, and D). We find networks that produce neurons most responsive to specific paired inputs – a building block of computation and essential role for cortex – contain the excessive clustering of excitatory synaptic connections observed in cortical slices. The same networks produce the best performance in a behavioral readout of the networks’ ability to complete the task. A plasticity mechanism operating on inhibitory connections, long-term potentiation of inhibition, when combined with structural plasticity, indirectly enhances clustering of excitatory cells via excitatory connections. A rate-dependent (triplet) form of spike-timing-dependent plasticity (STDP) between excitatory cells is less effective and basic STDP is detrimental. Clustering also arises in networks stimulated with single stimuli and in networks undergoing raised levels of spontaneous activity when structural plasticity is combined with functional plasticity. In conclusion, spatially intertwined clusters or cliques of connected excitatory cells can arise via a Hebbian form of structural plasticity operating in initially randomly connected networks.
Highlights
Connections between neurons are not randomly distributed, but contain correlations indicative of clustering (Song et al, 2005; Lefort et al, 2009; Perin et al, 2011)
Clustered connectivity correlates with task performance In a prior publication (Bourjaily and Miller, 2011) we showed the necessity of high stimulus-pair selectivity among cells in the associative network (Figure 1B) to produce reliably correct behavior as determined by the trained output of a biologically inspired decision-making network (Wang, 2002)
We find that in networks without triplet-spike-timing-dependent plasticity (STDP)
Summary
Connections between neurons are not randomly distributed, but contain correlations indicative of clustering (Song et al, 2005; Lefort et al, 2009; Perin et al, 2011). An in vitro study of connections among cells in a small region of visual cortex in rats demonstrated that bidirectional connections between pairs of neurons are much greater than expected by chance, given the measured probability of individual connections (Song et al, 2005). A more recent study (Perin et al, 2011) extended these results by simultaneously recording from groups of up to 12 cells in rat somatosensory cortex, finding spatially interlocking but distinctly connected clusters of dozens of cells. The authors suggest these latter findings place constraints on any experiencedependent structural reorganization of synaptic connections
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
