Elsevier

Current Opinion in Neurobiology

Volume 53, December 2018, Pages 50-56
Current Opinion in Neurobiology

Plasticity of population coding in primary sensory cortex

https://doi.org/10.1016/j.conb.2018.04.029Get rights and content

That experience shapes sensory tuning in primary sensory cortex is well understood. But effective neural population codes depend on more than just sensory tuning. Recent population imaging and recording studies have characterized population codes in sensory cortex, and tracked how they change with sensory manipulations and training on perceptual learning tasks. These studies confirm sensory tuning changes, but also reveal other features of plasticity, including sensory gain modulation, restructuring of firing correlations, and differential routing of information to output pathways. Unexpectedly strong day-to-day variation exists in single-neuron sensory tuning, which stabilizes during learning. These are novel dimensions of plasticity in sensory cortex, which refine population codes during learning, but whose mechanisms are unknown.

Introduction

Sensory experience drives robust plasticity of sensory tuning and maps in sensory cortex. This well-studied process drives map development to match sensory statistics, and contributes to sensory perceptual learning [1, 2, 3]. But is there more to sensory cortex plasticity than changes in sensory tuning? Sensory areas use population codes that are based on coordinated spiking across many neurons. Large-scale population imaging and recording enable comprehensive analysis of population coding. Chronic longitudinal imaging allows plasticity to be directly observed, with cellular resolution, as it unfolds [4, 5, 6]. These methods provide new insight into neural coding and how it changes during plasticity. Recent studies confirm changes in sensory tuning, but also reveal plasticity in other aspects of population coding, including response gain and variability, firing correlations, and top-down modulation by task context. Here we review some key findings, which suggest novel sites and mechanisms for sensory cortex plasticity.

Section snippets

Plasticity of sensory tuning

In classical map plasticity, neurons adjust their sensory tuning to represent common or behaviorally relevant (i.e. reinforced) sensory features. This is confirmed by population imaging. In mouse V1, filtering out all but one visual orientation in juveniles increases the proportion of neurons tuned to that orientation [7]. In adults, monocular deprivation causes 60% of active neurons to shift ocular dominance toward the open eye, though a minority shift paradoxically to favor the closed eye [8••

Principles of population coding in sensory cortex

Sensation occurs on single trials, despite noisy spike data. Population codes are robust on single trials because they utilize statistical patterns of activity across large numbers of neurons. Both population spike recording and population imaging have been used to characterize population coding in sensory cortex. Here we focus on population imaging, which typically samples more neurons, often with cell type specificity, and has revealed several key features of population coding in sensory

Learning by changes in response gain and reliability

Sensory training can improve population coding by modulating sensory response gain and reliability (Figure 1b). Poort et al. [15••] trained mice to discriminate vertical from angled gratings in a virtual corridor to guide the decision whether to lick for a reward. Calcium imaging during behavior showed that with training, V1 neurons became more selective for grating orientation, largely driven by increased single-trial reliability and amplitude (gain) to the preferred orientation. This improved

Learning by changes in firing correlations between neurons

Population coding is strongly impacted by noise correlations, which are co-variations in firing rate between neurons that are not due to shared sensory tuning. Correlated noise in similarly tuned neurons impairs population coding because it mimics sensory-evoked signals. But when noise correlations are inversely related to tuning similarity, they can increase stimulus information at the population level [32, 33]. Several studies in high-level sensory areas demonstrate that learning can improve

Learning by reduction in daily tuning variation

In the motor system, variability in movement-related activity is robust, and drives variable motor output that explores the space of useful movements. During motor learning, circuit activity that commands successful movements is reinforced, thus selecting an optimal movement for completing the task. As learning occurs, neural variability decreases [29, 30, 38]. Does the day-to-day variability in single-neuron sensory tuning play a similar role in sensory learning?

Several studies show that

Learning by changing routing of information down sensorimotor pathways

Learning of sensorimotor associations requires plasticity downstream of sensory cortex to transform sensory signals to appropriate behavioral responses. Recently, Le Merre et al. [42••] tracked macroscopic changes in sensory-evoked population activity along cortical pathways while animals learned to lick in response to whisker deflection. Recording chronically in an array of sensory, associative, and motor areas, they found that sensory-evoked potentials were initially present in S1 and S2, but

Sites and mechanisms for changing population codes

These changes in population coding are likely to reflect a mix of classical and novel circuit plasticity mechanisms. Hebbian plasticity in local circuits will form and strengthen ensembles of coactive neurons, driven either by bottom-up sensory statistics, top-down inputs, or their interaction. This likely explains shifts and narrowing of sensory tuning for task-relevant sensory features. Pattern completion within Hebbian ensembles could explain increased response reliability. As demonstrated

Conflict of interest statement

Nothing declared.

Acknowledgements

This work was supported by NIH R37 NS092367 and NIH R01 NS105333.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

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