Constructing neural networks with pre-specified dynamics.

A main goal in neuroscience is to understand the computations carried out by neural populations that give animals their cognitive skills. Neural network models allow to formulate explicit hypotheses regarding the algorithms instantiated in the dynamics of a neural population, its firing statistics, and the underlying connectivity. Neural networks can be defined by a small set of parameters, carefully chosen to procure specific capabilities, or by a large set of free parameters, fitted with optimization algorithms that minimize a given loss function.

Information capacity and robustness of encoding in the medial prefrontal cortex are modulated by the bioavailability of serotonin and the time elapsed from the cue during a reward-driven task.

Serotonin (5-HT) is a key neuromodulator of medial prefrontal cortex (mPFC) functions. Pharmacological manipulation of systemic 5-HT bioavailability alters the electrical activity of mPFC neurons. However, 5-HT modulation at the population level is not well characterized.

Probing the structure-function relationship with neural networks constructed by solving a system of linear equations.

Neural network models are an invaluable tool to understand brain function since they allow us to connect the cellular and circuit levels with behaviour. Neural networks usually comprise a huge number of parameters, which must be chosen carefully such that networks reproduce anatomical, behavioural, and neurophysiological data. These parameters are usually fitted with off-the-shelf optimization algorithms that iteratively change network parameters and simulate the network to evaluate its performance and improve fitting.

Serotonergic modulation of basolateral amygdala nucleus in the extinction of reward-driven learning: The role of 5-HT bioavailability and 5-HT receptor.

Serotonin (5-HT) neurotransmission has been associated with reward-related behaviour. Moreover, the serotonergic system modulates the basolateral amygdala (BLA), a structure involved in reward encoding, and reward prediction error. However, the role played by 5-HT on BLA during a reward-driven task has not been fully elucidated.

Seizure localization using pre ictal phase-amplitude coupling in intracranial electroencephalography.

Understanding changes in brain rhythms provides useful information to predict the onset of a seizure and to localize its onset zone in epileptic patients. Brain rhythms dynamics in general, and phase-amplitude coupling in particular, are known to be drastically altered during epileptic seizures. However, the neural processes that take place before a seizure are not well understood.

Putative dopamine neurons in the ventral tegmental area enhance information coding in the prefrontal cortex.

It has been proposed that neuronal populations in the prefrontal cortex (PFC) robustly encode task-relevant information through an interplay with the ventral tegmental area (VTA). Yet, the precise computation underlying such functional interaction remains elusive. Here, we conducted simultaneous recordings of single-unit activity in PFC and VTA of rats performing a GO/NoGO task.

A spherical treadmill system to train head-fixed adult rats.

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.

Efficient enhancement of information in the prefrontal cortex during the presence of reward predicting stimuli.

The prefrontal cortex (PFC) is a key brain structure for decision making, behavioural flexibility and working memory. Neurons in PFC encode relevant stimuli through changes in their firing rate, although the metabolic cost of spiking activity puts strong constrains to neural codes based on firing rate modulation. Thus, how PFC neural populations code relevant information in an efficient way is not clearly understood.

Exploring the limits of learning: Segregation of information integration and response selection is required for learning a serial reversal task.

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.