researcher
researcher
researcher
researcher
Navigating from one point to another may seem like a straightforward task, but it relies on the coordinated deployment of various complex cognitive capacities. These include operations such as pattern recognition, usage of knowledge about temporal relationships, or behavioral planning and control. Testing spatial behavior is relatively uncomplicated, and the associated neural responses, although distant from the sensorimotor periphery, are often surprisingly easy to correlate with meaningful variables. Consequently, this simplicity has facilitated the identification of numerous cell types that encode different aspects of spatial behavior. This makes spatial navigation an ideal model for exploring the neural mechanisms responsible for higher-level cognitive functions.Among the extensively studied cell types that modulate spatial information are place cells and grid cells. These cells indicate an animal's location by activating only in specific regions of the environment. Notably, these cells exhibit a type of phase coding: their firing during different phases of the theta oscillation appears to represent the positions recently reached or soon to be reached by the animal.
Our research delves into understanding how networks of such phase coding cells can facilitate spatial navigation, encompassing functions like path integration, predicting future positions, and planning movements. To achieve this, we employ a combination of computational modeling, simulation work, and the analysis of experimental data.
CoBeL-spike is one of the software developed in our lab to simulate sophisticated interactions between an environment and a biologically plausible neural network.
Publications
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The Cost of Behavioral Flexibility: Reversal Learning Driven by a Spiking Neural Network