Research (selected)

RESEARCH AREAS: Neuro-Inspiration, Neuro-Robotics, Cognitive Science, Cognitive Maps, Computational Neuroscience, Autonomous Robotic Navigation, Bio-Inspired Artificial Intelligence, Episodic Memory, and Alzehimer.

Uncovering how mental representations acquire, recall, and decode spatial information about relative locations and environmental attributes (cognitive map) involves different challenges.  Success in this endeavor could help us understand what is wrong in Alzehimer disease. This project is geared towards theoretical discussions and cognitive robotic implementations on the controversial issue of cognitive scalability for understanding cognitive map emergence from place and grid cells at the intersection between neuroscience and artificial intelligence. In our view, different place maps emerge from parallel and hierarchical neural structures supporting a global cognitive map. The mechanisms sustaining these maps do not only process sensory input but also assign the input to a location. Contentious issues around these concepts and provide concrete suggestions for moving the field forward. We recommend approaching the described challenges guided by AI-based theoretical aspects of encoded place instead of based chiefly on technological aspects to study the brain. 

Main publications: Scientific Reports-Nature, Cognitive Computation and JComputational Intelligence.

RESEARCH AREAS: Neural Data Science, Systems Neuroscience, Electrophysiology, Neural Image Processing,  Signal Processing,  Behavioural Decision Analyses, and Autism.

The recollection of environmental cues associated with cognitive decisions, such as visual, threat or reward cues allows animals to select the most appropriate behavioral responses. Here acquired memories (encoded after learning) and innate memories (those that are defined by evolution) have a crucial role. Neurons in different brain areas, like the prelimbic (PL) cortex respond to threat- and reward-associated cues. Visual neurons also are tuned to specific cues.  However, it remains unknown how these brain areas interact and the principles that govern the animals' decisions depending on previously associated memories. This project is based on different research models to establish neural correlates of specific cognitive behaviors and discuss what is wrong with brain diseases such as in neurology of disease (e.g. anxiety and autism).

Main publications:  eLife , Nature Communications, JNeuroscience, JNeural Engineering, and Cerebral Cortex

RESEARCH AREAS: Computational Neuroscience,    Simulated Neural Networks,  Sleep Transition, and Attention to Memories.

The variational Free Energy Principle (FEP) establishes that a neural system minimizes a free energy function of their internal state through environmental sensing entailing beliefs about hidden states in their environment. Because sensations are drastically reduced during sleep, it is still unclear how a self-organizing neural network can modulate free energy during sleep transitions. To address this issue, we study how network's state-dependent changes in energy, entropy and free energy connect with changes at the synaptic level in the absence of sensing during a sleep-like transition. We use simulations of a physically plausible, environmentally isolated neuronal network that self-organize after inducing a thalamic input to show that the reduction of non-variational free energy depends sensitively upon thalamic input at a slow, rhythmic Poisson (delta) frequency due to spike timing dependent plasticity. We define a non-variational free energy in terms of the relative difference between the energy and entropy of the network from the initial distribution (prior to activity dependent plasticity) to the nonequilibrium steady-state distribution (after plasticity). The modulation of this non-variational free energy in a network that self-organizes, seems to be an organizational network principle. This could open a window to new empirically testable hypotheses about state changes in a neural network.

Main publications: Biosystems and Physics of Life.