Diogo Pata SantosPostdoctoral Researcher
Synthetic, Perceptive, Emotive and Cognitive Systems (SPECS)
Staff member publications
The hexagonal tessellation pattern of grid cells scales up progressively along the dorsal-to-ventral axis of the medial entorhinal cortex (MEC) layer II. This scaling gradient has been hypothesized to originate either from inter-population synaptic dynamics as postulated by attractor networks, from projected theta frequencies to different axis levels, as in oscillatory models, or from cellular dynamics dependent on hyperpolarization-activated cation currents. To test the hypothesis that intrinsic cellular properties account for the scale gradient as well as the different oscillatory frequencies observed along the dorsal-to-ventral axis, we have modeled and analyzed data from a population of grid cells simulated with spiking neurons interacting through low-dimensional attractor dynamics. To investigate the causal relationship between oscillatory frequencies and grid-scale increase, we analyzed the dominant frequencies of the membrane potential for cells with distinct after-spike dynamics. We observed that the intrinsic neuronal membrane properties of simulated cells could induce an increase of grid-scale when modulated by after-spike reset values. Differences in the membrane potential oscillatory frequency were observed along the simulated dorsal-to-ventral axis, suggesting that, rather than driving to the increase of grid-scale as proposed by interference models of grid cells, they are the result of intrinsic cellular properties of neurons at each axis level. Overall, our results suggest that the after-spike dynamics of cation currents may play a major role in determining the grid cells scale and that oscillatory frequencies are a consequence of intrinsic cellular properties that are specific to different levels of the dorsal-to-ventral axis in the MEC layer II.
Grid cells in the medial entorhinal cortex (MEC) have known spatial periodic firing fields which provide a metric for the representation of self-location and path planning. The hexagonal tessellation pattern of grid cells scales up progressively along the MEC’s layer II dorsal-to-ventral axis. This scaling gradient has been hypothesized to originate either from inter-population synaptic dynamics as postulated by attractor networks, or from projected theta frequency waves to different axis levels, as in oscillatory models. Alternatively, cellular dynamics and specifically slow high-threshold conductances have been proposed to have an impact on the grid cell scale. To test the hypothesis that intrinsic hyperpolarization-activated cation currents account for both the scaled gradient and the oscillatory frequencies observed along the dorsal-to-ventral axis, we have modeled and analyzed data from a population of grid cells simulated with spiking neurons interacting through low-dimensional attractor dynamics. We observed that the intrinsic neuronal membrane properties of simulated cells were sufficient to induce an increase in grid scale and potentiate differences in the membrane potential oscillatory frequency. Overall, our results suggest that the after-spike dynamics of cation currents may play a major role in determining the grid cells’ scale and that oscillatory frequencies are a consequence of intrinsic cellular properties that are specific to different levels of the dorsal-to-ventral axis in the MEC layer II.
Keywords: Grid cells, Entorhinal, Hyperpolarization, Navigation, Space
Estimating the affective state of a user during a computer task traditionally relies on either subjective reports or analysis of physiological signals, facial expressions, and other measures. These methods have known limitations, can be intrusive and may require specialized equipment. An alternative would be employing a ubiquitous device of everyday use such as a standard keyboard. Here we investigate if we can infer the emotional state of a user by analyzing their typing patterns. To test this hypothesis, we asked 400 participants to caption a set of emotionally charged images taken from a standard database with known ratings of arousal and valence. We computed different keystroke pattern dynamics, including keystroke duration (dwell time) and latency (flight time). By computing the mean value of all of these features for each image, we found a statistically significant negative correlation between dwell times and valence, and between flight times and arousal. These results highlight the potential of using keystroke dynamics to estimate the affective state of a user in a non-obtrusive way and without the need for specialized devices.
Keywords: Feature extraction, Correlation, Keyboards, Task analysis, Statistical analysis, Affective computing, Standards, Keystroke, Keyboard, Typing, Arousal, Valence, Affect
When learning new environments, rats often pause at decision points and look back and forth over their possible trajectories as if they were imagining the future outcome of their actions, a behavior termed “Vicarious trial and error” (VTE). As the animal learns the environmental configuration, rats change from deliberative to habitual behavior, and VTE tends to disappear, suggesting a functional relevance in the early stages of learning. Despite the extensive research on spatial navigation, learning and VTE in the rat model, fewer studies have focused on humans. Here, we tested whether head-scanning behaviors that humans typically exhibit during spatial navigation are as predictive of spatial learning as in the rat. Subjects performed a goal-oriented virtual navigation task in a symmetric environment. Spatial learning was assessed through the analysis of trajectories, timings, and head orientations, under habitual and deliberative spatial navigation conditions. As expected, we found that trajectory length and duration decreased with the trial number, implying that subjects learned the spatial configuration of the environment over trials. Interestingly, IdPhi (a standard metric of VTE) also decreased with the trial number, suggesting that humans benefit from the same head-orientation scanning behavior as rats at spatial decision-points. Moreover, IdPhi captured exclusively at the first decision-point of each trial, was correlated with trial trajectory duration and length. Our findings demonstrate that in VTE is a signature of the stage of spatial learning in humans, and can be used to predict performance in navigation tasks with high accuracy.
Keywords: Deliberation, Habitual, Hippocampus, Navigation, Spatial decision-making
Insects are great explorers, able to navigate through long-distance trajectories and successfully find their way back. Their navigational routes cross dynamic environments suggesting adaptation to novel configurations. Arthropods and vertebrates share neural organizational principles and it has been shown that rodents modulate their neural spatial representation accordingly with environmental changes. However, it is unclear whether insects reflexively adapt to environmental changes or retain memory traces of previously explored situations. We sought to disambiguate between insect behavior in environmental novel situations and reconfiguration conditions. An immersive mixed-reality multi-sensory setup was built to replicate multi-sensory cues. We have designed an experimental setup where female crickets Gryllus Bimaculatus were trained to move towards paired auditory and visual cues during primarily phonotactic driven behavior. We hypothesized that insects were capable of identifying sensory modifications in known environments. Our results show that, regardless of the animal’s history, novel situation conditions did not compromise the animals performance and navigational directionality towards a new target location. However, in trials where visual and auditory stimuli were spatially decoupled, the animals heading variability towards a previously known position significantly increased. Our findings showed that crickets can behaviorally manifest environmental reconfiguration, suggesting the encoding for spatial representation.
Keywords: Insect, Memory, Navigation, Spatial representation
Many hippocampal cell types are characterized by a progressive increase in scale along the dorsal-to-ventral axis, such as in the cases of head-direction, grid and place cells. Also located in the medial entorhinal cortex (MEC), border cells would be expected to benefit from such scale modulations. However, this phenomenon has not been experimentally observed. Grid cells in the MEC of mammals integrate velocity related signals to map the environment with characteristic hexagonal tessellation patterns. Due to the noisy nature of these input signals, path integration processes tend to accumulate errors as animals explore the environment, leading to a loss of grid-like activity. It has been suggested that border-to-grid cells' associations minimize the accumulated grid cells' error when rodents explore enclosures. Thus, the border-grid interaction for error minimization is a suitable scenario to study the effects of border cell scaling within the context of spatial representation. In this study, we computationally address the question of (i) border cells' scale from the perspective of their role in maintaining the regularity of grid cells' firing fields, as well as (ii) what are the underlying mechanisms of grid-border associations relative to the scales of both grid and border cells. Our results suggest that for optimal contribution to grid cells' error minimization, border cells should express smaller firing fields relative to those of the associated grid cells, which is consistent with the hypothesis of border cells functioning as spatial anchoring signals.
Keywords: Border cells, Error minimization, Grid cells, Navigation, Path integration