Search

Background: While spherical treadmills are widely used in mouse models, there are only a few experimental setups suitable for adult rats, and none of them include head-fixation., New Method: We introduce a novel spherical treadmill apparatus for head-fixed rats that allows a wide repertory of natural responses. The rat is secured to a frame and placed on a freely rotating sphere. While being head-fixed, it can walk in any direction and perform different motor tasks., Comparison With Existing Methods: Instead of being air-lifted, which is acceptable for light animals, the treadmill is sustained by three spherical bearings ensuring a smooth rotation in any direction. Movement detection is accomplished using a video camera that registers a dot pattern plotted on the sphere., Results: Long Evans rats were trained to perform an auditory discrimination task in a Go/No-Go (walking/not-walking) paradigm. Animals were able to successfully discriminate between a 1 kHz and a 8 kHz auditory stimulus and execute the correct response, reaching the learning criterion (80% of correct responses) in approximately 20 training sessions., Conclusions: Our system broadens the possibilities of head-fixation experiments in adult rats making them compatible with spatial navigation on a spherical treadmill., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Animals are proposed to learn the latent rules governing their environment in order to maximize their chances of survival. However, rules may change without notice, forcing animals to keep a memory of which one is currently at work. Rule switching can lead to situations in which the same stimulus/response pairing is positively and negatively rewarded in the long run, depending on variables that are not accessible to the animal. This fact raises questions on how neural systems are capable of reinforcement learning in environments where the reinforcement is inconsistent. Here we address this issue by asking about which aspects of connectivity, neural excitability and synaptic plasticity are key for a very general, stochastic spiking neural network model to solve a task in which rules change without being cued, taking the serial reversal task (SRT) as paradigm. Contrary to what could be expected, we found strong limitations for biologically plausible networks to solve the SRT. Especially, we proved that no network of neurons can learn a SRT if it is a single neural population that integrates stimuli information and at the same time is responsible of choosing the behavioural response. This limitation is independent of the number of neurons, neuronal dynamics or plasticity rules, and arises from the fact that plasticity is locally computed at each synapse, and that synaptic changes and neuronal activity are mutually dependent processes. We propose and characterize a spiking neural network model that solves the SRT, which relies on separating the functions of stimuli integration and response selection. The model suggests that experimental efforts to understand neural function should focus on the characterization of neural circuits according to their connectivity, neural dynamics, and the degree of modulation of synaptic plasticity with reward.

Circuit modification associated with learning and memory involves multiple events, including the addition and remotion of newborn cells trough adulthood. Adult neurogenesis and gliogenesis were mainly described in models of voluntary exercise, enriched environments, spatial learning and memory task; nevertheless, it is unknown whether it is a common mechanism among different learning paradigms, like reward dependent tasks. Therefore, we evaluated cell proliferation, neurogenesis, astrogliogenesis, survival and neuronal maturation in the medial prefrontal cortex (mPFC) and the hippocampus (HIPP) during learning an operant conditioning task. This was performed by using endogenous markers of cell proliferation, and a bromodeoxiuridine (BrdU) injection schedule in two different phases of learning. Learning an operant conditioning is divided in two phases: a first phase when animals were considered incompletely trained (IT, animals that were learning the task) when they performed between 50% and 65% of the responses, and a second phase when animals were considered trained (Tr, animals that completely learned the task) when they reached 100% of the responses with a latency time lower than 5 seconds. We found that learning an operant conditioning task promoted cell proliferation in both phases of learning in the mPFC and HIPP. Additionally, the results presented showed that astrogliogenesis was induced in the medial prefrontal cortex (mPFC) in both phases, however, the first phase promoted survival of these new born astrocytes. On the other hand, an increased number of new born immature neurons was observed in the HIPP only in the first phase of learning, whereas, decreased values were observed in the second phase. Finally, we found that neuronal maturation was induced only during the first phase. This study shows for the first time that learning a reward-dependent task, like the operant conditioning, promotes neurogenesis, astrogliogenesis, survival and neuronal maturation depending on the learning phase in the mPFC-HIPP circuit.

Nitric oxide (NO) promotes plasticity and it is essential for learning, the NO synthases involved in these events are the endothelial NO synthase (eNOS) and neuronal NO synthase (nNOS) isoforms. The aim of this study was to study transcription, protein expression and enzymatic activity of eNOS and nNOS in the prefrontal cortex and the hippocampus during learning an operant conditioning task. Animals were considered incompletely trained (IT) when performed between 50% and 65% of responses, whereas animals were considered trained when reached 100% of responses with a latency time lower than 5 s. Following training session animals were killed and we quantified mRNA levels by Real Time RT-PCR, protein expression by western blot and enzymatic activity. eNOS and nNOS mRNA levels were only incremented in IT group. On the contrary, protein expression of both isoforms were augmented during all learning process. Moreover, we also found that eNOS and nNOS synthase activity were incremented in IT group and in trained group. Here, we showed that during learning there is a differential regulation of eNOS and nNOS in the prefrontal cortex and hippocampus and that NO could be acting as a promoter of plasticity.

The plasticity in the medial Prefrontal Cortex (mPFC) of rodents or lateral prefrontal cortex in non human primates (lPFC), plays a key role neural circuits involved in learning and memory. Several genes, like brain-derived neurotrophic factor (BDNF), cAMP response element binding (CREB), Synapsin I, Calcium/calmodulin-dependent protein kinase II (CamKII), activity-regulated cytoskeleton-associated protein (Arc), c-jun and c-fos have been related to plasticity processes. We analysed differential expression of related plasticity genes and immediate early genes in the mPFC of rats during learning an operant conditioning task. Incompletely and completely trained animals were studied because of the distinct events predicted by our computational model at different learning stages. During learning an operant conditioning task, we measured changes in the mRNA levels by Real-Time RT-PCR during learning; expression of these markers associated to plasticity was incremented while learning and such increments began to decline when the task was learned. The plasticity changes in the lPFC during learning predicted by the model matched up with those of the representative gene BDNF. Herein, we showed for the first time that plasticity in the mPFC in rats during learning of an operant conditioning is higher while learning than when the task is learned, using an integrative approach of a computational model and gene expression.

Changes in transcription levels of brain-derived neurotrophic factor (BDNF), cyclic adenosine monophosphate (cAMP) response element binding (CREB), Synapsin I, Ca(2+)/calmodulin-dependent protein kinase II (CamKII), activity-regulated cytoskeleton-associated protein (Arc), c-jun and c-fos have been associated to several learning paradigms in different brain areas. In this study, we measured mRNA expression in the hippocampus by real time (RT)-PCR mRNA levels of BDNF, CREB, Synapsin I, CamKII, Arc, c-jun and c-fos, during learning and operant conditioning task. Experimental groups were as follows: control (C, the animals never left the bioterium), when the animals reached 50-65% of the expected response (Incompletely Trained, IT), when animals reached 100% of the expected response with a latency time lower than 5 s (Trained, Tr), Box Control of Incompletely Trained (BCIT), animals spent the same time as the IT in the operant conditioning box and Box Control of Trained (BCTr) animals spent the same time as the Tr in the operant conditioning box. All rats were killed at the same time by cervical dislocation 15 min after training and hippocampi were removed and processed. We found increments of mRNA levels of most genes (BDNF, CREB, Synapsin I, Arc, c-jun and c-fos) in IT and Tr groups compared to their box controls, but increments in Tr were smaller compared with IT. These results describe a differential gene expression in the rat hippocampus when the animals are learning and when animals have already learned. Taking together the results presented herein with the known functions of these genes, we propose a link between changes in gene expression in the hippocampus and different degrees of cellular activation and plasticity during learning of an operant conditioning task.