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Reyes, M. B.

Publications and source records attributed to Reyes, M. B..

8 recordsLinked to original sources

Evidence for timing in the midsession reversal task with rats in operant conditioning boxes

The midsession reversal (MSR) task is frequently used to study behavioral flexibility and decision strategies in animals. In a typical version of the task, subjects complete 80 trials in which they choose between two simultaneously presented stimuli, S1 and S2. During the first 40 trials, responses to S1 are reinforced, whereas responses to S2 are not. The contingencies then reverse without warning: From trial 41 to 80, only responses to S2 are reinforced. In birds, performance in this task is often characterized by anticipatory and perseverative errors around the reversal point, suggesting a reliance on elapsed time since the session began. In contrast, rats tested in operant conditioning chambers typically show near-optimal performance with few errors, a pattern often interpreted as evidence that rats rely primarily on local reinforcement cues rather than temporal information. The present study investigated whether rats exclusively rely on local cues in the MSR task or whether temporal information also contributes to the decision process. Two groups of rats were trained with different intertrial intervals (ITIs; 5 s or 10 s) while the reversal point remained fixed at Trial 41. During acquisition, both groups diplayed similar learning rates and near-optimal steady-state performance with minimal anticipatory or perseverative errors. However, when the ITI was manipulated in probe sessions, systematic shifts in switching behavior emerged. Rats adjusted their choices according to the temporal midpoint experienced during training rather than the nominal trial number of the reversal. These results suggest that rats rely on a mixed strategy that integrates local reinforcement cues with global timing information. Temporal control may therefore be present even when it is not expressed during standard training conditions.

animal behavior and cognition↗

Evidence in favor of abrupt over gradual learning in the differential reinforcement of response duration (DRRD) task

Learning can occur in markedly different ways: in some cases, it unfolds as a gradual process, with behavior improving slowly toward an asymptotic level of performance; in others, it appears as an abrupt process that sharply separates behavior before and after a change point. Understanding the behavioral and neural processes underlying these distinct acquisition patterns may be critical for elucidating the basic principles of learning. We investigated this question experimentally using naive rats performing a differential reinforcement of response duration (DRRD) task, in which animals were required to remain inside a nosepoke for a minimum duration of 1.5 seconds to get a sugar pellet as a reward. All rats learned to wait longer in the nosepoke when comparing behavior at the beginning and at the end of the experiment. We tested several continuous models against a single change point (CP) model, in which behavior changes at a specific moment and remains stable thereafter. Instead of the traditional approach based on trial-segmented behavior, we used the real time elapsed since the beginning of the experiment as a continuous, uncontrolled variable. We fitted all models to data from individual rats and compared model fit quality across alternatives. Our results provide strong evidence in favor of an abrupt change, as captured by the CP model, over all other models. Moreover, the residuals of the CP model exhibited a Gaussian distribution, suggesting that no additional systematic dynamics remained unexplained and that the behavioral dynamics were fully captured by a single change point.

animal behavior and cognition↗

Intrinsic noise reveals the stability of a neuronal network

The stability of rhythmic activity in neural networks is an important aspect in the study of central pattern generators (CPGs). Different from other physiological rhythms, the activity of CPGs has not been fully characterized in terms of its stability, especially using quantitative methods. We propose a method that takes advantage of the natural noise present in CPGs to quantify the stability of the rhythmic activity. Furthermore, we used the stationary bootstrap method to define confidence intervals of the results. We applied this method to study the influence of a synaptic modification on the pyloric CPG circuit, using artificial synapses implemented in dynamic clamp software. We show that even after removing one of its strongest synapses, the CPG stability remains unaltered. This analysis suggests that CPGs are designed to be strongly stable regardless of the parameter perturbations they undergo.

neuroscience↗

Inferring large-scale structural connectivity from local activity in a model of the mouse large-scale cortical network

AO_SCPLOWBSTRACTC_SCPLOWInferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the Generalized Partial Directed Coherence (GPDC), provide estimates of the causal influence between areas. However, the relation between causality estimates and structural connectivity is still not clear. We analyzed this problem by evaluating the effectiveness of GPDC to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale network model of the mouse cortex. The model contains 19 cortical areas comprised of spiking neurons, with areas connected by long-range projections with weights obtained from a tract-tracing cortical connectome. We show that GPDC values provide a reasonable estimate of structural connectivity, with an average Pearson correlation over simulations of 0.74. Moreover, even in a typical electrophysiological recording scenario containing five areas, the mean correlation was above 0.6. These results suggest that it may be possible to empirically estimate structural connectivity from functional connectivity even when detailed whole-brain recordings are not achievable.

neuroscience↗

Time-Encoding Migrates from Prefrontal Cortex to Dorsal Striatum During Learning of a Self-Timed Response Duration Task

Although time is a fundamental dimension of life, we do not know how the brain encodes the temporal information. Several brain areas underlie the temporal information, such as the hippocampus, prefrontal cortex, and striatum, but evidence of how they cooperate to process temporal information is scarce. Notably, the analysis of neural activity during learning are rare, mainly because timing tasks usually take a long time to train. Here we investigated how the time encoding evolves when animals learn to time a 1.5 s interval. We designed a novel training protocol where rats go from naive- to proficient-level timing performance within a single session, allowing us to investigate neuronal activity from very early learning stages. We used pharmacological experiments and machine-learning algorithms to evaluate the level of time encoding in the medial prefrontal cortex and the dorsal striatum. Our results show a double dissociation between the roles of the medial prefrontal cortex and the dorsal striatum during temporal learning, where the former commits to early learning stages while the latter become more engaged as animals become more proficient in the task.

neuroscience↗

Open hardware low-cost system for behavioral experiments simultaneously with electrophysiological recordings.

A major frontier in neuroscience is to find neural correlates of perception, learning, decision making, and a variety of other types of behavior. In the last decades, modern devices allow simultaneous recordings of different operant responses and the electrical activity of large neuronal populations. However, the commercially available instruments for studying operant conditioning are expensive, and the design of low-cost chambers has emerged as an appealing alternative to resource-limited laboratories engaged in animal behavior. In this article, we provide a full description of a platform that records the operant behavior and synchronizes it with the electrophysiological activity. The programming of this platform is open source, flexible and adaptable to a wide range of operant conditioning tasks. We also show results of operant conditioning experiments with freely moving rats with simultaneous electrophysiological recordings. Specifications table O_TBL View this table: org.highwire.dtl.DTLVardef@114843corg.highwire.dtl.DTLVardef@1f59122org.highwire.dtl.DTLVardef@10b5b77org.highwire.dtl.DTLVardef@a220bdorg.highwire.dtl.DTLVardef@136c1b_HPS_FORMAT_FIGEXP M_TBL C_TBL

animal behavior and cognition↗

Temporal regularity and stimulus control in multiple fixed interval schedules

In multiple fixed interval schedules of reinforcement, different time intervals are signaled by different environmental stimuli which acquire control over behavior. Previous work has shown that temporal performance is controlled not only by external stimuli but also by temporal aspects of the task, depending on the order in which the different intervals are trained - intermixed across trials or in blocks of several trials. The aim of this study was to further describe the training conditions under which the stimuli acquire control over temporal performance. We manipulated the number of consecutive trials of each fixed interval (FI) per training block (Experiment I) and the number of FIs trained (Experiment II). The results suggest that when trained in blocks of several consecutive trials of the same FI, temporal performance is controlled by temporal regularities across trials and not by the visual stimuli that signal the FIs. One possible account for those data is that the temporal cues overshadow the visual stimuli for the control of temporal performance. Similar results have also been observed with humans, which suggest that temporal regularity overcomes the stimuli in the control of behavior in temporal tasks across species.

animal behavior and cognition↗

A Model for the Peak-Interval Task Based on Neural Oscillation Delimited States

Timing mechanisms in the brain are still an open issue. Several existing computational models for timing can reproduce properties of experimental psychophysical responses. Still, only a few consider the underlying biological mechanisms, such as the synchronized neural activity that occurs in several brain areas. In this paper, we introduce a model for the peak-interval task based on neuronal network properties. We consider that Local Field Potential (LFP) oscillation cycles specify a sequence of states, represented as neuronal ensembles. Repeated presentation of time intervals during training reinforces the connections of specific ensembles to downstream networks. Later, during the peak-interval procedure, these downstream networks are reactivated by previously experienced neuronal ensembles, triggering actions at the learned time intervals. The model reproduces experimental response patterns from individual rats in the peak-interval procedure, satisfying relevant properties such as the Weber law. Finally, the model provides a biological interpretation of its parameters.

animal behavior and cognition↗