Nucleus accumbens neurons dynamically respond to appetitive and aversive associative learning

To survive, individuals must learn to associate cues in the environment with emotionally relevant outcomes. This association is partially mediated by the nucleus accumbens (NAc), a key brain region of the reward circuit that is mainly composed by GABAergic medium spiny neurons (MSNs), that express either dopamine receptor D1 or D2. Recent studies showed that both populations can drive reward and aversion, however, the activity of these neurons during appetitive and aversive Pavlovian conditioning remains to be determined. Here, we investigated the relevance of D1- and D2-neurons in associative learning, by measuring calcium transients with fiber photometry during appetitive and aversive Pavlovian tasks in mice. Sucrose was used as a positive valence unconditioned stimulus (US) and foot shock was used as a negative valence US. We show that during appetitive Pavlovian conditioning, D1- and D2-neurons exhibit a general increase in activity in response to the conditioned stimuli (CS). Interestingly, D1- and D2-neurons present distinct changes in activity after sucrose consumption that dynamically evolve throughout learning. During the aversive Pavlovian conditioning, D1- and D2-neurons present an increase in the activity in response to the CS and to the US (shock). Our data support a model in which D1- and D2-neurons are concurrently activated during appetitive and aversive conditioning.

Statistical complexity is maximized close to criticality in cortical dynamics

Complex systems are typically characterized as an intermediate situation between a complete regular structure and a random system. Brain signals can be studied as a striking example of such systems: cortical states can range from highly synchronous and ordered neuronal activity (with higher spiking variability) to desynchronized and disordered regimes (with lower spiking variability). It has been recently shown, by testing independent signatures of criticality, that a phase transition occurs in a cortical state of intermediate spiking variability. Here we use a symbolic information approach to show that, despite the monotonical increase of the Shannon entropy between ordered and disordered regimes, we can determine an intermediate state of maximum complexity based on the Jensen disequilibrium measure. More specifically, we show that statistical complexity is maximized close to criticality for cortical spiking data of urethane-anesthetized rats, as well as for a network model of excitable elements that presents a critical point of a nonequilibrium phase transition.

Subsampled Directed-Percolation Models Explain Scaling Relations Experimentally Observed in the Brain

Recent experimental results on spike avalanches measured in the urethane-anesthetized rat cortex have revealed scaling relations that indicate a phase transition at a specific level of cortical firing rate variability. The scaling relations point to critical exponents whose values differ from those of a branching process, which has been the canonical model employed to understand brain criticality. This suggested that a different model, with a different phase transition, might be required to explain the data. Here we show that this is not necessarily the case. By employing two different models belonging to the same universality class as the branching process (mean-field directed percolation) and treating the simulation data exactly like experimental data, we reproduce most of the experimental results. We find that subsampling the model and adjusting the time bin used to define avalanches (as done with experimental data) are sufficient ingredients to change the apparent exponents of the critical point. Moreover, experimental data is only reproduced within a very narrow range in parameter space around the phase transition.