Interpyramid Spike Transmission Stabilizes the Sparseness of Recurrent Network Activity

Abstract

Cortical synaptic strengths vary substantially from synapse to synapse and exhibit a skewed distribution with a small fraction of synapses generating extremely large depolarizations. Using multiple whole-cell recordings from rat hippocampal CA3 pyramidal cells, we found that the amplitude of unitary excitatory postsynaptic conductances approximates a lognormal distribution and that in the presence of synaptic background noise, the strongest fraction of synapses could trigger action potentials in postsynaptic neurons even with single presynaptic action potentials, a phenomenon termed interpyramid spike transmission (IpST). The IpST probability reached 80%, depending on the network state. To examine how IpST impacts network dynamics, we simulated a recurrent neural network embedded with a few potent synapses. This network, unlike many classical neural networks, exhibited distinctive behaviors resembling cortical network activity in vivo. These behaviors included the following: 1) infrequent ongoing activity, 2) firing rates of individual neurons approximating a lognormal distribution, 3) asynchronous spikes among neurons, 4) net balance between excitation and inhibition, 5) network activity patterns that was robust against external perturbation, 6) responsiveness even to a single spike of a single excitatory neuron, and 7) precise firing sequences. Thus, IpST captures a surprising number of recent experimental findings in vivo. We propose that an unequally biased distribution with a few select strong synapses helps stabilize sparse neuronal activity, thereby reducing the total spiking cost, enhancing the circuit responsiveness, and ensuring reliable information transfer.

Astrocytes regulate heterogeneity of presynaptic strengths in hippocampal networks

“…Therefore, the mechanism that promotes the presynaptic strength heterogeneity is likely to function in concert with the various rules of synaptic plasticity that operates in the adult hippocampal circuit. For example, decorrelated presynaptic strengths may boost the circuit responsiveness under sparse activity conditions (61) or facilitate the reorganization of correlated networks that is associated with learning (62). Moreover, its dysregulation might culminate in diseased states; for instance, unrestrained correlation may nucleate synchronization of activity across synaptic networks, which is a hallmark feature of epileptic disorders (63).…”

Extraction of Synaptic Input Properties in Vivo

“…This means that the probability for large events is larger than for a Gaussian with same mean and variance. Such heavy-tailed distributions have been observed in a number of systems (Sayer, Friedlander, and Redman, 1990;Song et al, 2005;Barbour et al, 2007;Ikegaya et al, 2013) and are believed to be an important characteristic of neural processing (Koulakov, Hromádka, and Zador, 2009;Roxin et al, 2011;Teramae, Tsubo, and Fukai, 2012). While any distribution can be tested (although for efficiency reasons the moments should ideally be available analytically), a future goal is to reconstruct the amplitude distribution directly, for instance by reconstructing it from it moments.…”

A neural network model of reliably optimized spike transmission

“…Log-normal distribution of synaptic strength is observed in neocortical slices (Song et al 2005;Sarid et al 2007;Lefort et al 2009) and in the hippocampal network (Ikegaya et al 2013). In general, the lognormal distribution with mean l and standard deviation r is defined as follows (Song et al 2005):…”