Marco Idiart

The Input–Output Transformation of the Hippocampal Granule Cells: From Grid Cells to Place Fields

Grid cells in the rat medial entorhinal cortex fire (periodically) over the entire environment. These cells provide input to hippocampal granule cells whose output is characterized by one or more small place fields. We sought to understand how this input–output transformation occurs. Available information allows simulation of this process with no freely adjustable parameters. We first examined the spatial distribution of excitation in granule cells produced by the convergence of excitatory inputs from randomly chosen grid cells. Because the resulting summation depends on the number of inputs, it is necessary to use a realistic number (∼1200) and to take into consideration their 20-fold variation in strength. The resulting excitation maps have only modest peaks and valleys. To analyze how this excitation interacts with inhibition, we used an E%-max (percentage of maximal suprathreshold excitation) winner-take-all rule that describes how gamma-frequency inhibition affects firing. We found that simulated granule cells have firing maps that have one or more place fields whose size and number approximates those observed experimentally. A substantial fraction of granule cells have no place fields, as observed experimentally. Because the input firing rates and synaptic properties are known, the excitatory charge into granule cells could be calculated (2–3 pC) and was found to be only somewhat larger than required to fire granule cells (1 pC). We conclude that the input–output transformation of dentate granule does not depend strongly on synaptic modification; place field formation can be understood in terms of simple summation of randomly chosen excitatory inputs, in conjunction with a winner-take-all network mechanism.

A Second Function of Gamma Frequency Oscillations: An E%-Max Winner-Take-All Mechanism Selects Which Cells Fire

The role of gamma oscillations in producing synchronized firing of groups of principal cells is well known. Here, we argue that gamma oscillations have a second function: they select which principal cells fire. This selection process occurs through the interaction of excitation with gamma frequency feedback inhibition. We sought to understand the rules that govern this process. One possibility is that a constant fraction of cells fire. Our analysis shows, however, that the fraction is not robust because it depends on the distribution of excitation to different cells. A robust description is termed E%-max: cells fire if they have suprathreshold excitation (E) within E% of the cell that has maximum excitation. The value of E%-max is approximated by the ratio of the delay of feedback inhibition to the membrane time constant. From measured values, we estimate that E%-max is 5–15%. Thus, an E%-max winner-take-all process can discriminate between groups of cells that have only small differences in excitation. To test the utility of this framework, we analyzed the role of oscillations in V1, one of the few systems in which both spiking and intracellular excitation have been directly measured. We show that an E%-max winner-take-all process provides a simple explanation for why the orientation tuning of firing is narrower than that of the excitatory input and why this difference is not affected by increasing excitation. Because gamma oscillations occur in many brain regions, the framework we have developed for understanding the second function of gamma is likely to have wide applicability.

Exploration method using harmonic functions

Abstract Harmonic functions provide optimal potential maps for robot navigation in a previously explored static environment. Here we investigate the performance of an algorithm for exploration based on partial updates of a harmonic potential in an occupancy grid. We consider that while the robot moves it carries along an activation window whose size is of the order of the sensor’s range. The activation window recruits grid points to participate in the potential calculation. By using simulations and experiments with the Nomad 200 robot we investigate the algorithm performance in respect to parameters such as the frequency of updates and the numerical method used to calculate the harmonic potential.

Propagation of excitation in neural network models

We study the propagation of waves of excitation in neural network models. Through analytic calculation and computer simulation, they determine how the propagation velocity depends on the range and strength of synaptic interactions, the firing threshold and on transmission delays. For the models considered, the propagation velocity depends on either the first or the second moment of the distribution function characterizing the length of synaptic interactions.

Automated Multiword Expression Prediction for Grammar Engineering

However large a hand-crafted wide-coverage grammar is, there are always going to be words and constructions that are not included in it and are going to cause parse failure. Due to their heterogeneous and flexible nature, Multiword Expressions (MWEs) provide an endless source of parse failures. As the number of such expressions in a speakers lexicon is equiparable to the number of single word units (Jackendoff, 1997), one major challenge for robust natural language processing systems is to be able to deal with MWEs. In this paper we propose to semi-automatically detect MWE candidates in texts using some error mining techniques and validating them using a combination of the World Wide Web as a corpus and some statistical measures. For the remaining candidates possible lexico-syntactic types are predicted, and they are subsequently added to the grammar as new lexical entries. This approach provides a significant increase in the coverage of these expressions.

Exploratory Navigation Based on Dynamical Boundary Value Problems

The paper presents a general framework for concurrent navigation and exploration of unknown environments based on discrete potential fields that guide the robot motion. These potentials are obtained from a class of partial differential equation (PDE) problems called boundary value problems (BVP). The boundaries are generated from sensor readings and therefore they change as the robot moves. This framework corresponds to an extension of our previous work (Prestes, E., Idiart, M. A. P., Engel, P. and Trevisan, M.: Exploration technique using potential fields calculated from relaxation methods, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001, p. 2012; Prestes, E., Engel, P. M., Trevisan, M. and Idiart, M. A.: Exploration method using harmonic functions, Robot. Auton. Syst.40(1) (2002), 25–42). Here, we propose that a careful choice of the PDE and the boundary conditions can produce efficient exploratory behaviors in sparse and dense environments. Furthermore, we show how to extend the exploratory behavior to produce new ones by changing dynamically the boundary function (the value of the potential at the boundaries) as the exploration takes course. Our framework is validated through a series of experiments with a real robot in office environments.

Rounding of aggregates of biological cells: Experiments and simulations

The influence of surface tension and size on rounding of cell aggregates is studied using chick embryonic cells and numerical simulations based on the cellular Potts model. Our results show exponential relaxation in both cases as verified in previous studies using 2D Hydra cell aggregates. The relaxation time decreases with higher surface tension as expected from hydrodynamics laws. However, it increases faster than linearly with aggregate size. The results provide an additional support to the validity of the cellular Potts model for non-equilibrium situations and indicate that aggregate shape relaxation is not governed by the hydrodynamics of viscous liquids.

A model of cholinergic modulation in olfactory bulb and piriform cortex

In this work we investigate in a computational model how cholinergic inputs to the olfactory bulb (OB) and piriform cortex (PC) modulate odor representations. We use experimental data derived from different physiological studies of ACh modulation of the bulbar and cortical circuitry and the interaction between these two areas. The results presented here indicate that cholinergic modulation in the OB significantly increases contrast and synchronization in mitral cell output. Each of these effects is derived from distinct neuronal interactions, with different groups of interneurons playing different roles. Both bulbar modulation effects contribute to more stable learned representations in PC, with pyramidal networks trained with cholinergic-modulated inputs from the bulb exhibiting more robust learning than those trained with unmodulated bulbar inputs. This increased robustness is evidenced as better recovery of memories from corrupted patterns and lower-concentration inputs as well as increased memory capacity.

Rupture of a liposomal vesicle

We discuss pore dynamics in osmotically stressed vesicles. A set of equations which govern the liposomal size, internal solute concentration, and pore diameter is solved numerically. We find that dependent on the internal solute concentration and vesicle size, liposomes can stay pore free, nucleate a short-lived pore, or nucleate a long-lived pore. The phase diagram of pore stability is constructed, and the different scaling regimes are deduced analytically.

Pore dynamics of osmotically stressed vesicles

We present a theory for pore dynamics of osmotically stressed vesicles. When a liposome with an internal concentration of solute is placed inside a solute-depleted medium, an osmotic flow of solvent through the lipid bilayer leads to swelling of vesicle and to increase in membrane surface tension. This can result in membrane rupture and opening of thermal pores. Depending on the internal concentration of solute and the size of the vesicle, pores can close rapidly or be long lived. We find that the life span of the long-lived pores scales non-trivially with the size of the liposome. Closure of the long-lived pore is followed by a rapid flicker-like opening and closing of short-lived pores. Our model is consistent with the observation of long-lived pores in red blood cell ghosts.