Summary Neuronal representations change as associations are learned between sensory stimuli and behavioral actions. However, it is poorly understood whether representations for learned associations stabilize in cortical association areas or continue to change following learning. We tracked the activity of posterior parietal cortex neurons for a month as mice stably performed a virtual-navigation task. The relationship between cells’ activity and task features was mostly stable on single days but underwent major reorganization over weeks. The neurons informative about task features (trial type and maze locations) changed across days. Despite changes in individual cells, the population activity had statistically similar properties each day and stable information for over a week. As mice learned additional associations, new activity patterns emerged in the neurons used for existing representations without greatly affecting the rate of change of these representations. We propose that dynamic neuronal activity patterns could balance plasticity for learning and stability for memory.

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Doubt persists about ecotourism's ability to make tangible contributions to conservation and deliver benefits for host communities. This work in Costa Rica's Osa Peninsula tests the hypothesis that ecotourism in this region is more effective at improving well-being for local residents, at enhancing their access to key resources and information, and at supporting biodiversity conservation than other locally available economic sectors. Data from 128 semi-structured interviews with local workers, both in ecotourism and in other occupations, together with associated research, indicate that ecotourism offers the best currently available employment opportunities, double the earnings of other livelihoods, and other linked benefits. Locally, ecotourism is viewed as the activity contributing most to improvements in residents' quality of life in the Osa Peninsula and to increased levels of financial and attitudinal support for parks and environmental conservation. Ecolodge ownership by local people is substantial, and many local ecotourism workers plan to launch their own businesses. The data offer a convincing rebuttal to arguments that ecotourism does little to address poverty or disparities in access to resources and equally rebuts claims that ecotourism is simply a part of the "neoliberal conservation toolkit" that cannot help but exacerbate the very inequalities it purports to address.

Over days and weeks, neurons in mammalian sensorimotor cortex have been found to continually change their activity patterns during performance of a learned sensorimotor task, with no detectable change in behaviour. This challenges classical theories of neural circuit function and memory, which posit that stable engrams underlie stable learned behavior [1,2]. Using existing experimental data we show that a simple linear readout can accurately recover behavioural variables, and that fixed linear weights can approximately decode behaviour over many days, despite significant changes in neural tuning. This implies that an appreciable fraction of ongoing activity reconfiguration occurs in an approximately linear subspace of population activity. We quantify the amount of additional plasticity that would be required to compensate for reconfiguration, and show that a biologically plausible local learning rule can achieve good decoding accuracy with physiologically achievable rates of synaptic plasticity.

Over days and weeks, neural activity representing an animal's position and movement in sensorimotor cortex has been found to continually reconfigure or 'drift' during repeated trials of learned tasks, with no obvious change in behavior. This challenges classical theories which assume stable engrams underlie stable behavior. However, it is not known whether this drift occurs systematically, allowing downstream circuits to extract consistent information. Analyzing long-term calcium imaging recordings from posterior parietal cortex in mice (Mus musculus), we show that drift is systematically constrained far above chance, facilitating a linear weighted readout of behavioural variables. However, a significant component of drift continually degrades a fixed readout, implying that drift is not confined to a null coding space. We calculate the amount of plasticity required to compensate drift independently of any learning rule, and find that this is within physiologically achievable bounds. We demonstrate that a simple, biologically plausible local learning rule can achieve these bounds, accurately decoding behavior over many days.

Flexible computation is a hallmark of intelligent behavior. Yet, little is known about how neural networks contextually reconfigure for different computations. Humans are able to perform a new task without extensive training, presumably through the composition of elementary processes that were previously learned. Cognitive scientists have long hypothesized the possibility of a compositional neural code, where complex neural computations are made up of constituent components; however, the neural substrate underlying this structure remains elusive in biological and artificial neural networks. Here we identified an algorithmic neural substrate for compositional computation through the study of multitasking artificial recurrent neural networks. Dynamical systems analyses of networks revealed learned computational strategies that mirrored the modular subtask structure of the task-set used for training. Dynamical motifs such as attractors, decision boundaries and rotations were reused across different task computations. For example, tasks that required memory of a continuous circular variable repurposed the same ring attractor. We show that dynamical motifs are implemented by clusters of units and are reused across different contexts, allowing for flexibility and generalization of previously learned computation. Lesioning these clusters resulted in modular effects on network performance: a lesion that destroyed one dynamical motif only minimally perturbed the structure of other dynamical motifs. Finally, modular dynamical motifs could be reconfigured for fast transfer learning. After slow initial learning of dynamical motifs, a subsequent faster stage of learning reconfigured motifs to perform novel tasks. This work contributes to a more fundamental understanding of compositional computation underlying flexible general intelligence in neural systems. We present a conceptual framework that establishes dynamical motifs as a fundamental unit of computation, intermediate between the neuron and the network. As more whole brain imaging studies record neural activity from multiple specialized systems simultaneously, the framework of dynamical motifs will guide questions about specialization and generalization across brain regions.

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