Damián Oliva

A Subjective Distance Between Stimuli: Quantifying the Metric Structure of Representations

As subjects perceive the sensory world, different stimuli elicit a number of neural representations. Here, a subjective distance between stimuli is defined, measuring the degree of similarity between the underlying representations. As an example, the subjective distance between different locations in space is calculated from the activity of rodents hippocampal place cells and lateral septal cells. Such a distance is compared to the real distance between locations. As the number of sampled neurons increases, the subjective distance shows a tendency to resemble the metrics of real space.

Visuo-motor transformations involved in the escape response to looming stimuli in the crab Neohelice (=Chasmagnathus) granulata

SUMMARY Escape responses to directly approaching predators represent one instance of an animals ability to avoid collision. Usually, such responses can be easily evoked in the laboratory using two-dimensional computer simulations of approaching objects, known as looming stimuli. Therefore, escape behaviors are considered useful models for the study of computations performed by the brain to efficiently transform visual information into organized motor patterns. The escape response of the crab Neohelice (previously Chasmagnathus) granulata offers an opportunity to investigate the processing of looming stimuli and its transformation into complex motor patterns. Here we studied the escape performance of this crab to a variety of different looming stimuli. The response always consisted of a vigorous run away from the stimulus. However, the moment at which it was initiated, as well as the developed speed, closely matched the expansion dynamics of each particular stimulus. Thus, we analyzed the response events as a function of several variables that could theoretically be used by the crab (angular size, angular velocity, etc.). Our main findings were that: (1) the decision to initiate the escape run is made when the stimulus angular size increases by 7 deg; (2) the escape run is not a ballistic kind of response, as its speed is adjusted concurrently with changes in the optical stimulus variables; and (3) the speed of the escape run can be faithfully described by a phenomenological input–output relationship based on the stimulus angular increment and the angular velocity of the stimulus.

Computation of object approach by a system of visual motion-sensitive neurons in the crab Neohelice

Similar to most visual animals, crabs perform proper avoidance responses to objects directly approaching them. The monostratified lobula giant neurons of type 1 (MLG1) of crabs constitute an ensemble of 14-16 bilateral pairs of motion-detecting neurons projecting from the lobula (third optic neuropile) to the midbrain, with receptive fields that are distributed over the extensive visual field of the animals eye. Considering the crab Neohelice (previously Chasmagnathus) granulata, here we describe the response of these neurons to looming stimuli that simulate objects approaching the animal on a collision course. We found that the peak firing time of MLG1 acts as an angular threshold detector signaling, with a delay of δ = 35 ms, the time at which an object reaches a fixed angular threshold of 49°. Using in vivo intracellular recordings, we detected the existence of excitatory and inhibitory synaptic currents that shape the neural response. Other functional features identified in the MLG1 neurons were phasic responses at the beginning of the approach, a relation between the stimulus angular velocity and the excitation delay, and a mapping between membrane potential and firing frequency. Using this information, we propose a biophysical model of the mechanisms that regulate the encoding of looming stimuli. Furthermore, we found that the parameter encoded by the MLG1 firing frequency during the approach is the stimulus angular velocity. The proposed model fits the experimental results and predicts the neural response to a qualitatively different stimulus. Based on these and previous results, we propose that the MLG1 neuron system acts as a directional coding system for collision avoidance.

Maximum Evidence Method for classification of brain tissues in MRI

Within the family of statistical image segmentation methods, those based on Bayesian inference have been commonly applied to classify brain tissues as obtained with Magnetic Resonance Imaging (MRI). In this framework we present an unsupervised algorithm to account for the main tissue classes that constitute MR brain volumes. Two models are examined: the Discrete Model (DM), in which every voxel belongs to a single tissue class, and the Partial Volume Model (PVM), where two classes may be present in a single voxel with a certain probability. We make use of the Maximum Evidence (ME) criterion to estimate the most probable parameters describing each model in a separate fashion. Since an exact image inference would be computationally very expensive, we propose an approximate algorithm for model optimization. Such method was tested on a simulated MRI-T1 brain phantom in 3D, as well as on clinical MR images. As a result, we found that the PVM slightly outperforms the DM, both in terms of Evidence and Mean Absolute Error (MAE). We also show that the Evidence is a very useful figure of merit for error prediction as well as a convenient tool to determine the most probable model from measured data.

Object approach computation by a giant neuron and its relationship with the speed of escape in the crab Neohelice.

ABSTRACT Upon detection of an approaching object, the crab Neohelice granulata continuously regulates the direction and speed of escape according to ongoing visual information. These visuomotor transformations are thought to be largely accounted for by a small number of motion-sensitive giant neurons projecting from the lobula (third optic neuropil) towards the supraesophageal ganglion. One of these elements, the monostratified lobula giant neuron of type 2 (MLG2), proved to be highly sensitive to looming stimuli (a 2D representation of an object approach). By performing in vivo intracellular recordings, we assessed the response of the MLG2 neuron to a variety of looming stimuli representing objects of different sizes and velocities of approach. This allowed us to: (1) identify some of the physiological mechanisms involved in the regulation of the MLG2 activity and test a simplified biophysical model of its response to looming stimuli; (2) identify the stimulus optical parameters encoded by the MLG2 and formulate a phenomenological model able to predict the temporal course of the neural firing responses to all looming stimuli; and (3) incorporate the MLG2-encoded information of the stimulus (in terms of firing rate) into a mathematical model able to fit the speed of the escape run of the animal. The agreement between the model predictions and the actual escape speed measured on a treadmill for all tested stimuli strengthens our interpretation of the computations performed by the MLG2 and of the involvement of this neuron in the regulation of the animals speed of run while escaping from objects approaching with constant speed. Summary: Visually guided behaviors operate in closed loop. In crabs, the information of approaching objects is encoded and conveyed by identified giant neurons to continuously regulate the speed of the escape run.

Bayesian Estimation of Hyperparameters in MRI through the Maximum Evidence Method

Bayesian inference methods are commonly applied to the classification of brain magnetic resonance images (MRI). We use the maximum evidence (ME) approach to estimate the most probable parameters and hyperparameters for models that take into account discrete classes (DM) and models accounting for the partial volume effect (PVM). An approximate algorithm was developed for model optimization, since the exact image inference calculation is computationally expensive. The method was validated using simulated images and a digital phantom. We show that the evidence is a very useful figure for error prediction, which is to be maximized respect to the hyperparameters. Additionally, it provides a tool to determine the most probable model given measured data.

Probabilidad de infracción de velocidad de vehículos utilizando visión artificial en cámaras de campo amplio

We estimate vehicle velocities and detect speeds greater than the speed limit in videos captured with a wide-field fisheye camera (360° × 183°) in the context of transport analysis. To do this: 1. We implemented a calibration method that allows us to map points from the fisheye cameras image to a georeferenced map obtained from a satellite image. 2. blobs of moving objects were detected using a background subtraction algorithm. Feature points were detected inside the blobs and tracked using an algorithm based on SURF and FLANN techniques. 3. We fitted polynomials to the point trajectories, Its coefficients were estimated online by the recursive least squares algorithm. 4. The speed of feature points was estimated. 5. We calculated the probability of the speed being above the allowed limit.

Georeferenced feature tracking in wide field images

In this paper we implemented a system of fisheye vision (a 360° × 180° visual field) for georeferenced tracking of feature points associated with moving objects in urban scenes. To develop this system: 1. We calibrate the intrinsic parameters of a fisheye camera based on the stereographic model. 2. We propose a geometric model of image formation and adjust the position and orientation parameters using satellite images of the observation zone. With this calibration process, we establish a mapping that allows us to convert positions of feature points from the fisheye image to the georeferenced image. 3. We study the uncertainties of the camera pose and the mapping. 4. We propose a system based on SURF and FLANN to detect features trajectories in fisheye video. 5. We calculate georeferenced trajectories of vehicles in the fisheye video.