Strasbourg Computational and Systems Neuroscience seminar

During learning process, it is commonly believed that the goal directed behaviour, is sustained by the activity of the dorsomedial portion of the striatum – DMS, whereas the activation of the dorsolateral striatum – DLS – sustains inflexible motor routines, stimulus-response learning and more generally habit behaviour. However, this DMS-DLS functional dissociation is mainly observed in instrumental conditioning, but it is less straightforward in non-instrumental learning. In this study we aimed at investigating this issue using a spontaneous alternation task with no cue-guided instruction, thus limiting experimentally-defined discrete actions that possibly influence ‘task bracketing’ activity. DMS and DLS single neuron activity was recorded from the first day rats 81 were exposed to the task until they reached and maintained stable performance. To decode animal behaviour from neural activity we used classical analyses based on either firing rate or neural synchronization, but we also combined the two measures using Hawkes process to reconstruct directed connectivity graphs of neurons. This approach enabled us to better unravel the coding capacity of both regions in the course of learning. We showed that DMS and DLS display different task-related activity throughout learning stages. The proportion of DMS coding neurons decreases whereas the number of DLS coding neurons increases over time, but this changes do not influence the capacity to decode animal behaviour from DMS and DLS neural networks. These results suggest that both DMS and DLS neural networks are engaged during all learning stages but they gradually reorganize in different ways, in contrast with the common assumption of a gradual shift from DMS to DLS across learning stages.

Brain activity during sleep is characterized by circuit-specific oscillations, including slow waves, spindles, sharp-wave ripples or theta, that are nested in thalamocortical or hippocampus networks, respectively. However, the activity of other brain circuits is strongly modulated during sleep states. A major challenge is to determine the neural mechanisms underlying these activities and their functional implications. In this lecture, I will summarize our work on the dissection of the neural circuits underlying sleep-wake control, sleep oscillations and their relevance to brain plasticity associated with REM sleep, and discuss their relevance to the elaboration of goal-directed behaviors including feeding and emotional processing.

A crucial challenge in targeted manipulation of neural activity is to identify perturbation sites whose stimulation exerts significant effects downstream (high efficacy), a procedure currently achieved by labor-intensive and potentially harmful trial-and-error. Targeted perturbations will be greatly facilitated by understanding causal interactions within neural ensembles to guide the design of intervention protocols. Can one predict the effects of electrical stimulation on neural activity solely based on the properties of circuit dynamics during spontaneous periods? Here, we show that the effects of single-site microstimulation on ensemble activity in an alert monkey’s prefrontal cortex can be predicted solely based on the ensemble spontaneous activity. 

We infer the ensemble’s functional causal flow (FCF) based on the functional interactions inferred at rest. We compare the performance of alternative classes of models based on information theory, such as transfer entropy and granger causality, or deterministic dynamical systems, such as convergent cross-mapping (CCM). We find that FCF, inferred via CCM, successfully predicts intervention effects, outperforming all information-based methods and it reveals a causal hierarchy between the array electrodes. We elucidate the computational features underlying FCF using ground truth data from recurrent neural network models, showing that FCF is robust to noise and common inputs. Our results provide the foundation for using targeted circuit manipulations to develop targeted interventions suitable for brain-machine interfaces and ameliorating cognitive dysfunctions in the human brain.

Ongoing neural activity unfolds across different timescales, reflecting networks´ specialization for task-relevant computations. However, it is unknown whether these timescales can be flexibly modulated during trial-to-trial alternations of cognitive states (e.g., attention state) and what mechanisms can cause such modulations. We analyzed autocorrelations of population spiking activity recorded from individual cortical columns of the primate area V4 during a spatial attention task and a fixation task. We developed a method to correctly estimate timescales from short trials and rigorously determine the number of timescales in neural activity. We found at least two distinct timescales in spontaneous and stimulus-driven activity. The slower timescale was significantly longer on trials when monkeys attended to the location of the receptive field of the recorded neurons than on control trials when monkeys attended to a different location. Interestingly, under the attention condition, the timescale predicted the reaction time. Using computational models, we show that the observed timescales emerge from the recurrent network dynamics shaped by the spatial connectivity structure. Finally, we discuss how the timescales could reflect optimization to perform the information processing tasks.

Research on the “emotional brain” often focuses on specific structures, such as the amygdala and the ventral striatum. In this presentation, I will discuss research that embraces a distributed view of both emotion- and motivation-related processing, as well as efforts to unravel the impact of emotion and motivation across large-scale cortical-subcortical brain networks. In this context, I will discuss experimental paradigms used to study the dynamics of threat- and reward-related processes, as well as their interactions. Taken together, these studies support a framework where emotion and motivation have broad effects on brain and behavior, such that brain parts dynamically assemble together into functional circuits to support complex cognitive-emotional behaviors.