No abstract

Integrating individual actions into coherent, organised behavioural units, a process called chunking, is a fundamental, evolutionarily conserved process that renders actions automatic. In vertebrates, evidence points to the basal ganglia – a complex network believed to be involved in action selection – as a key component of action sequence encoding, although the underlying mechanisms are only just beginning to be understood. Central pattern generators control many innate automatic behavioural sequences that form some of the most basic behaviours in an animal’s repertoire, and in vertebrates, brainstem and spinal pattern generators are under the control of higher order structures such as the basal ganglia. Evidence suggests that the basal ganglia play a crucial role in the concatenation of simpler behaviours into more complex chunks, in the context of innate behavioural sequences such as chain grooming in rats, as well as sequences in which innate capabilities and learning interact such as birdsong, and sequences that are learned from scratch, such as lever press sequences in operant behaviour. It has been proposed that the role of the striatum, the largest input structure of the basal ganglia, might lie in selecting and allowing the relevant central pattern generators to gain access to the motor system in the correct order, while inhibiting other behaviours. As behaviours become more complex and flexible, the pattern generators seem to become more dependent on descending signals. Indeed, during learning, the striatum itself may adopt the functional characteristics of a higher order pattern generator, facilitated at the microcircuit level by striatal neuropeptides.

Background: Although several brain regions and electrophysiological patterns have been related to sequence learning, less attention has been paid to the role that different neuromodulators play. Aims: Here we sought to investigate the role of substance P (SP) in sequence learning in an operant conditioning preparation, supported by a reinforcement learning model. Methods: Two experiments were performed to test the effects of an NK1 receptor (at which SP primarily acts) antagonist on learning and performing action sequences. In experiment 1, rats were trained to perform an action sequence until stable performance was achieved, and then, in phase 2, they were switched to perform the reverse sequence. In experiment 2, rats were trained to perform an action sequence, and in phase 2, they continued to do the same sequence. In both experiments in the first 3 days of phase 2, rats were injected with an NK1 receptor antagonist (L-733,060, i.p.) or with vehicle. Additionally, we developed a reinforcement learning model which allowed the in silico replication of our experimental tasks. Results: We found that administering an NK1 receptor antagonist weakened the stable retention of a well-learned sequence, allowing the faster acquisition of a new sequence, without impairing the continued performance of a crystallized sequence. Using our reinforcement learning model, we suggest that SP could be acting through the state value learning rate, modulating the effects of the reward prediction error. Conclusions: Our results suggest that SP could be involved in the consolidation of a sequence representation through a modulatory effect on the reward prediction error.

Applications of network theory to microbial ecology are an emerging and promising approach to understanding both global and local patterns in the structure and interplay of these microbial communities. In this paper, we present an open-source python toolbox which consists of two modules: on one hand, we introduce a visualization module that incorporates the use of UMAP, a dimensionality reduction technique that focuses on local patterns, and HDBSCAN, a clustering technique based on density; on the other hand, we have included a module that runs an enhanced version of the SparCC code, sustaining larger datasets than before, and we couple the resulting networks with network theory analyses to describe the resulting co-occurrence networks, including several novel analyses, such as structural balance metrics and a proposal to discover the underlying topology of a co-occurrence network. We validated the proposed toolbox on 1) a simple and well described biological network of kombucha, consisting of 48 ASVs, and 2) we validate the improvements of our new version of SparCC. Finally, we showcase the use of the MicNet toolbox on a large dataset from Archean Domes, consisting of more than 2,000 ASVs. Our toolbox is freely available as a github repository (https://github.com/Labevo/MicNetToolbox), and it is accompanied by a web dashboard (http://micnetapplb-1212130533.us-east-1.elb.amazonaws.com) that can be used in a simple and straightforward manner with relative abundance data. This easy-to-use implementation is aimed to microbial ecologists with little to no experience in programming, while the most experienced bioinformatics will also be able to manipulate the source code’s functions with ease.