Variance and invariance of neuronal long-term representations

Abstract

The brain extracts behaviourally relevant sensory input to produce appropriate motor output. On the one hand, our constantly changing environment requires this transformation to be plastic. On the other hand, plasticity is thought to be balanced by mechanisms ensuring constancy of neuronal representations in order to achieve stable behavioural performance. Yet, prominent changes in synaptic strength and connectivity also occur during normal sensory experience, indicating a certain degree of constitutive plasticity. This raises the question of how stable neuronal representations are on the population level and also on the single neuron level. Here, we review recent data from longitudinal electrophysiological and optical recordings of single-cell activity that assess the long-term stability of neuronal stimulus selectivities under conditions of constant sensory experience, during learning, and after reversible modification of sensory input. The emerging picture is that neuronal representations are stabilized by behavioural relevance and that the degree of long-term tuning stability and perturbation resistance directly relates to the functional role of the respective neurons, cell types and circuits. Using a ‘toy’ model, we show that stable baseline representations and precise recovery from perturbations in visual cortex could arise from a ‘backbone’ of strong recurrent connectivity between similarly tuned cells together with a small number of ‘anchor’ neurons exempt from plastic changes.This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

Cortical circuit alterations precede disease onset in Huntington’s disease mice

“…the fraction of cells that were active at the given imaging time point and remained active in each of the following imaging sessions. In WT mice, the reoccurrence rates of active cells steadily declined throughout the imaging period, consistent with previous studies describing a high variability of neuronal activity patterns in the motor cortex (Rokni et al, 2007;Peters et al, 2014;Clopath et al, 2017). In R6/2 animals this decline also occurred, but slowed down from 8.5 weeks onwards, resulting in a higher reoccurrence rate compared to WT controls (WT, 7 FOVs from 6 mice; R6/2, 10 FOVs from 5 mice; Repeated measures ANOVA, 6.5 to 9.5 weeks:…”

Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions

“…Similarly, mechanisms involved in the recovery of individual neurons tuning following sensory deprivation in vivo [11,12,[14][15][16]29,60,67,95] could be explained via theoretical work. Theoretical models using attractor dynamics or hidden states [96,97] could be implemented to better understand how interactions between individual cells and the network of cells facilitate the recovery of activity following deprivation and maintain the same properties of individual cells from prior to deprivation [95,98]. Overall, better interaction between molecular/cellular and systems level experiments and theory will be critical to understand the underlying details of the mechanisms of plasticity and how they are implemented in vivo.…”

Population coupling predicts the plasticity of stimulus responses in cortical circuits

“…Advances in neural imaging techniques allow us to interrogate such changes by tracking stimulus responses of hundreds of neurons over many days in vivo (Andermann, 2010;Mank et al, 2008). These recordings reveal a substantial, and puzzling, variability in the long-term stability of responses in sensory cortex: some neurons retain highly stable preferences to specific stimuli, whereas the stimulus preference of other neurons change from day to day (Ranson, 2017;Clopath and Rose, 2017;Rose et al, 2016;Poort et al, 2015;Lütcke et al, 2013). The degree of stimulus response stability typically depends on brain region; whisking responses in mouse barrel cortex are highly plastic, whereas visual responses in mouse V1 are more stable but still exhibit fluctuations (Clopath and Rose, 2017;Lütcke et al, 2013).…”