Biomedical subjects

Angelo Arleo

Publications and source records attributed to Angelo Arleo.

6 recordsLinked to original sources

PubMed2005

Spatial navigation impairment in mice lacking cerebellar LTD: a motor adaptation deficit?

L7-PKCI transgenic mice, which lack parallel fiber-Purkinje cell long-term depression (LTD), were tested with two different mazes to dissociate the relative importance of declarative and procedural components of spatial navigation. We show that L7-PKCI mice are deficient in acquisition of an adapted goal-oriented behavior, part of the procedural component of the task. This supports the hypothesis that cerebellar LTD may subserve a general sensorimotor adaptation process shared by motor and spatial learning functions.

Adaptation, Physiological↗

PubMed2004

Cognitive navigation based on nonuniform Gabor space sampling, unsupervised growing networks, and reinforcement learning.

We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in the high-dimensional input space is found. The representation consists of a population of localized overlapping place fields covering the 2-D space densely and uniformly. This space coding is comparable to the representation provided by hippocampal place cells in rats. Place fields are learned by extracting spatio-temporal properties of the environment from sensory inputs. The visual scene is modeled using the responses of modified Gabor filters placed at the nodes of a sparse Log-polar graph. Visual sensory aliasing is eliminated by taking into account self-motion signals via path integration. This solves the hidden state problem and provides a suitable representation for applying reinforcement learning in continuous space for action selection. A temporal-difference prediction scheme is used to learn sensorimotor mappings to perform goal-oriented navigation. Population vector coding is employed to interpret ensemble neural activity. The model is validated on a mobile Khepera miniature robot.

Cognition↗

PubMed2004

Rat anterodorsal thalamic head direction neurons depend upon dynamic visual signals to select anchoring landmark cues.

Head direction cells, which are functionally coupled to 'place' cells of the hippocampus, a structure critically involved in spatial cognition, are likely neural substrates for the sense of direction. Here we studied the mechanism by which head direction cells are principally anchored to background visual cues [M.B. Zugaro et al. (2001) J. Neurosci., 21, RC154,1-5]. Anterodorsal thalamic head direction cells were recorded while the rat foraged on a small elevated platform in a 3-m diameter cylindrical enclosure. A large card was placed in the background, near the curtain, and a smaller card was placed in the foreground, near the platform. The cards were identically marked, proportionally dimensioned, subtended the same visual angles from the central vantage point and separated by 90 degrees. The rat was then disoriented in darkness, the cards were rotated by 90 degrees in opposite directions about the center and the rat was returned. Preferred directions followed either the background card, foreground card or midpoint between the two cards. In continuous lighting, preferred directions shifted to follow the background cue in most cases (30 of the 53 experiments, Batschelet V-test, P < 0.01). Stroboscopic illumination, which perturbs dynamic visual signals (e.g. motion parallax), blocked this selectivity. Head direction cells remained equally anchored to the background card, foreground card or configuration of the two cards (Watson test, P > 0.1). This shows that dynamic visual signals are critical in distinguishing typically more stable background cues which govern spatial neuronal responses and orientation behaviors.

Animals↗

PubMed2003

Rapid spatial reorientation and head direction cells.

It is surprising how quickly we can find our bearings when suddenly confronted with a familiar environment, for instance when the lights are turned on in a dark room. Subjectively, this appears to occur almost instantaneously, yet the neural processes permitting this rapid reorientation are unknown. A likely candidate is the head direction (HD) cell system. These limbic neurons found in several brain regions, including the thalamus and the hippocampus, discharge selectively when the head of an animal is oriented in a particular ("preferred") direction. This neuronal activity is independent of position and ongoing behavior and is thus likely to constitute a physiological basis for the sense of direction. Remarkably, although the HD cell system has properties resembling those of a compass, it is independent of geomagnetic fields. Rather, the preferred directions of the HD cells are strongly anchored to visual cues in the environment. Here, we bring evidence for the first time that a fundamental component of the capacity to rapidly reorient in a familiar environment may be brought about by updating of HD cell responses as rapidly as 80 msec after changes in the visual scene. Continuous attractor networks have been used successfully to model HD cell ensemble dynamics. The present results suggest that after large rotations of the surrounding landmarks, activity in such networks may be propagated in abrupt jumps rather than in a gradually progressive manner.

Action Potentials↗

PubMedDate not supplied

A continuous attractor network model without recurrent excitation: maintenance and integration in the head direction cell system.

Motivated by experimental observations of the head direction system, we study a three population network model that operates as a continuous attractor network. This network is able to store in a short-term memory an angular variable (the head direction) as a spatial profile of activity across neurons in the absence of selective external inputs, and to accurately update this variable on the basis of angular velocity inputs. The network is composed of one excitatory population and two inhibitory populations, with inter-connections between populations but no connections within the neurons of a same population. In particular, there are no excitatory-to-excitatory connections. Angular velocity signals are represented as inputs in one inhibitory population (clockwise turns) or the other (counterclockwise turns). The system is studied using a combination of analytical and numerical methods. Analysis of a simplified model composed of threshold-linear neurons gives the conditions on the connectivity for (i) the emergence of the spatially selective profile, (ii) reliable integration of angular velocity inputs, and (iii) the range of angular velocities that can be accurately integrated by the model. Numerical simulations allow us to study the proposed scenario in a large network of spiking neurons and compare their dynamics with that of head direction cells recorded in the rat limbic system. In particular, we show that the directional representation encoded by the attractor network can be rapidly updated by external cues, consistent with the very short update latencies observed experimentally by Zugaro et al. (2003) in thalamic head direction cells.

Action Potentials↗

PubMedDate not supplied

Persistent activity in limbic system neurons: neurophysiological and modeling perspectives.

Neural activity persisting for one to hundreds of seconds has been postulated to be a substrate of memory. This review article illustrates examples of such activity in limbic system structures including the hippocampus, postsubiculum, and the anterodorsal thalamus. These neuronal responses include better known correlates with the spatial position as well as with head direction of the animal relative to its environment as well as other lesser known examples. Since head direction responses are greater when the animal is actively moving than when passively rotated, it has been proposed that there might be a general mechanism where the behavioral state of the animal can provide modulatory gating of such persistent signals. This would regulate the relative influence of these signals on downstream structures. Neural network attractor models of the head direction cell system are presented to demonstrate how these responses might originate, as well as the dynamics by which they are updated during movements.

Animals↗