Population coupling predicts the plasticity of stimulus responses in cortical circuits

Abstract

Some neurons have stimulus responses that are stable over days, whereas other neurons have highly plastic stimulus responses. Using a recurrent network model, we explore whether this could be due to an underlying diversity in their synaptic plasticity. We find that, in a network with diverse learning rates, neurons with fast rates are more coupled to population activity than neurons with slow rates. This plasticity-coupling link predicts that neurons with high population coupling exhibit more long-term stimulus response variability than neurons with low population coupling. We substantiate this prediction using recordings from the Allen Brain Observatory, finding that a neuron’s population coupling is correlated with the plasticity of its orientation preference. Simulations of a simple perceptual learning task suggest a particular functional architecture: a stable ‘backbone’ of stimulus representation formed by neurons with low population coupling, on top of which lies a flexible substrate of neurons with high population coupling.

Primate neuronal connections are sparse in cortex as compared to mouse

“…Comparisons of how mouse and non-human primate brains differ and are similar have a long history in neuroscience, ranging from how neurons in similar brain regions respond to similar stimuli (Huberman and Niell, 2011;Sanzeni and Histed, 2020;Segev, 1992;Sweeney and Clopath, 2020;Van Hooser, 2007; to differences in the sizes of brain regions (Van Essen et al, 2018). The prior work perhaps most relevant is generally of two varieties: measurements of bulk synaptic density in neuropil (i.e., synapses/mm 3 ) in smaller volumes using EM (Ascoli et al, 2008;DeFelipe et al, 1999DeFelipe et al, , 2002Hsu et al, 2017;McGuire et al, 1991;Medalla and Luebke, 2015;Peters et al, 2008;Sherwood et al, 2020) and optical methods, particularly in primates, whereby neurons in living slices are filled and spines are counted as proxies of excitatory innervation (Gilman et al, 2017;Luebke et al, 2004;Luebke and Rosene, 2003;McGuire et al, 1991;Medalla and Luebke, 2015).…”

The brain in motion: How ensemble fluidity drives memory-updating and flexibility

“…Additionally, modeling work has suggested that repeated offline reactivation of specific ensembles could maintain potentiated synaptic weights ( Fauth and van Rossum, 2019 ). Thus, long-term memories may be supported by a ‘backbone’ of stable spines and neurons that store gross features while the remainder might continually undergo plasticity to encode more detailed representations ( Buzsáki and Mizuseki, 2014 ; Grosmark and Buzsaki, 2016 ; Sweeney and Clopath, 2020 ). A complementary ‘memory indexing’ theory has proposed that hippocampal neurons reinstate neocortical activity patterns for memory retrieval ( Goode et al, 2020 ; Tanaka et al, 2018 ; Teyler and DiScenna, 1986 ).…”

Primate neuronal connections are sparse as compared to mouse

“…neurons (Wang et al, 2016) for both visual stimuli and optogenetic manipulations (Sanzeni and Histed, 2020;Sweeney and Clopath, 2020). A simple potential explanation from our data suggests that feature extraction could be mediated by the pattern of inhibitory innervation, relatively similar across species, but that the magnitude of responses are encoded by the absolute number of connections, substantially different across species.…”