Long-term stability of cortical ensembles

Abstract

Neuronal ensembles, coactive groups of neurons found in spontaneous and evoked cortical activity, are causally related to memories and perception, but it still unknown how stable or flexible they are over time. We used two-photon multiplane calcium imaging to track over weeks the activity of the same pyramidal neurons in layer 2/3 of the visual cortex from awake mice and recorded their spontaneous and visually evoked responses. Less than half of the neurons were commonly active across any two imaging sessions. These 'common neurons' formed stable ensembles lasting weeks, but some ensembles were also transient and appeared only in one single session. Stable ensembles preserved ~68 % of their neurons up to 46 days, our longest imaged period, and these 'core' cells had stronger functional connectivity. Our results demonstrate that neuronal ensembles can last for weeks and could, in principle, serve as a substrate for long-lasting representation of perceptual states or memories.

Intrinsic excitability mechanisms of neuronal ensemble formation

“…Our experimental procedures, similar to those that build (“imprint”) neuronal ensembles in vivo (Carrillo-Reid et al, 2016), lead to increases in correlations of spontaneous activity, a hallmark of a neuronal ensemble, just as it does in vivo . The cellular and/or synaptic mechanism induced by this optogenetic and electrical activation could contribute to neuronal ensemble development (Penn and Shatz, 1999; Zhang and Poo, 2001), flexibility and plasticity (Clopath et al, 2017; Kirkwood et al, 1996; Mrsic-Flogel et al, 2007; Pérez-Ortega et al, 2021; Wandell and Smirnakis, 2009; Wiesel and Hubel, 1963).…”

“…Intrinsic excitability could also implement temporary stimulus-response mappings and might play a role in rapid cognitive flexibility (Pang and Fairhall, 2019). Faster “online” plasticity via intrinsic excitability could have a broad impact on network dynamics and could serve as a critical information-storage mechanism that may contribute to memory formation (Lisman et al, 2018; Marder and Goaillard, 2006; Pérez-Ortega et al, 2021; Pignatelli et al, 2019; Xu, 2005) and to the generation of intrinsic circuit states, such as for ensembles and circuit attractors (Buzsáki, 2010; Carrillo-Reid et al, 2019, 2016; Hopfield, 1982; Miller et al, 2014).…”

Self-healing codes: How stable neural populations can track continually reconfiguring neural representations

“…These recent, widespread observations suggest that neural circuits can preserve learned associations at the population level while allowing the functional role of individual neurons to change ( 14 – 16 ). Such preservation is made possible by redundancy in population codes because a distributed readout allows changes in the tuning of individual neurons to be offset by changes in others.…”

Representational drift influences learning of a sensory cortical microstimulation task

“…Representational drift has been observed across primary sensory cortices (Clopath et al ., 2017; Deitch et al ., 2021; Marks and Goard, 2021; Perez-Ortega et al ., 2021; Peron et al ., 2015; Schoonover et al ., 2021), though it is likely that pre-cortical structures contribute to drift when stimuli engage the entire sensory pathway. We observe representational drift in the context of direct cortical stimulation, implying that such turnover is an intrinsic feature of cortex.…”

“…In other cases, sensory representations in cortex remain stable in size and population-scale tuning (Peron et al, 2015). However, even when representations are stable at the population scale, high levels of turnover are observed in individual neurons (Deitch et al, 2021; Marks and Goard, 2021; Perez-Ortega et al, 2021; Peron et al ., 2015; Schoonover et al, 2021). Understanding representational stability is crucial as it imposes important constraints on downstream readout and thereby limits perception (Clopath et al, 2017).…”