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Fabrizio Gabbiani

Publications and source records attributed to Fabrizio Gabbiani.

11 recordsLinked to original sources
PubMed2006

Influence of electrotonic structure and synaptic mapping on the receptive field properties of a collision-detecting neuron.

The lobula giant movement detector (LGMD) is a visual interneuron of Orthopteran insects involved in collision avoidance and escape behavior. The LGMD possesses a large dendritic field thought to receive excitatory, retinotopic projections from the entire compound eye. We investigated whether the LGMD's receptive field for local motion stimuli can be explained by its electrotonic structure and the eye's anisotropic sampling of visual space. Five locust (Schistocerca americana) LGMD neurons were stained and reconstructed. We show that the excitatory dendritic field and eye can be fitted by ellipsoids having similar geometries. A passive compartmental model fit to electrophysiological data was used to demonstrate that the LGMD is not electrotonically compact. We derived a spike rate to membrane potential transform using intracellular recordings under visual stimulation, allowing direct comparison between experimental and simulated receptive field properties. By assuming a retinotopic mapping giving equal weight to each ommatidium and equally spaced synapses, the model reproduced the experimental data along the eye equator, though it failed to reproduce the receptive field along the ventral-dorsal axis. Our results illustrate how interactions between the distribution of synaptic inputs and the electrotonic properties of neurons contribute to shaping their receptive fields.

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PubMed2006

Spike-frequency adaptation and intrinsic properties of an identified, looming-sensitive neuron.

We investigated in vivo the characteristics of spike-frequency adaptation and the intrinsic membrane properties of an identified, looming-sensitive interneuron of the locust optic lobe, the lobula giant movement detector (LGMD). The LGMD had an input resistance of 4-5 MOmega, a membrane time constant of about 8 ms, and exhibited inward rectification and rebound spiking after hyperpolarizing current pulses. Responses to depolarizing current pulses revealed the neuron's intrinsic bursting properties and pronounced spike-frequency adaptation. The characteristics of adaptation, including its time course, the attenuation of the firing rate, the mutual dependency of these two variables, and their dependency on injected current, closely followed the predictions of a model first proposed to describe the adaptation of cat visual cortex pyramidal neurons in vivo. Our results thus validate the model in an entirely different context and suggest that it might be applicable to a wide variety of neurons across species. Spike-frequency adaptation is likely to play an important role in tuning the LGMD and in shaping the variability of its responses to visual looming stimuli.

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PubMed2005

Time-dependent activation of feed-forward inhibition in a looming-sensitive neuron.

The lobula giant movement detector (LGMD) is an identified neuron in the locust visual system that responds preferentially to objects approaching on a collision course with the animal. For such looming stimuli, the LGMD firing rate gradually increases, peaks, and decays toward the end of approach. The LGMD receives both excitatory and feed-forward inhibitory inputs on distinct branches of its dendritic tree, but little is known about the contribution of feed-forward inhibition to its response properties. We used picrotoxin, a chloride channel blocker, to selectively block feed-forward inhibition to the LGMD. We then computed differences in firing rate and membrane potential between control and picrotoxin conditions to study the activation of feed-forward inhibition. For looming stimuli, a significant activation of inhibition was observed early, as objects exceeded on average approximately 23 degrees in angular extent at the retina. Inhibition then increased in parallel with excitation over the remainder of approach trials. Experiments in which the final angular size of the approaching objects was systematically varied revealed that the relative activation of excitation and inhibition remains well balanced over most of the course of looming trials. Feed-forward inhibition actively contributed to the termination of the response to approaching objects and was particularly effective for large or slowly moving objects. Suddenly appearing and receding objects activated excitation and feed-forward inhibition nearly simultaneously, in contrast to looming stimuli. Under these conditions, the activation of excitation and feed-forward inhibition was weaker than for approaching objects, suggesting that both are preferentially tuned to approaching objects. These results support a phenomenological model of multiplication within the LGMD and provide new constraints for biophysical models of its responses to looming and receding stimuli.

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PubMed2004

Spatial distribution of inputs and local receptive field properties of a wide-field, looming sensitive neuron.

The lobula giant movement detector (LGMD) in the locust visual system and its target neuron, the descending contralateral movement detector (DCMD), respond to approaching objects looming on a collision course with the animal. They thus provide a good model to study the cellular and network mechanisms underlying the sensitivity to this specific class of behaviorally relevant stimuli. We determined over an entire locust eye the density distribution of optical axes describing the spatial organization of local inputs to the visual system and compared it with the sensitivity distribution of the LGMD/DCMD to local motion stimuli. The density of optical axes peaks in the equatorial region of the frontal eye. Local motion sensitivity, however, peaks in the equatorial region of the caudolateral visual field and only correlates positively with the dorso-ventral density of optical axes. On local stimulation, both the velocity tuning and the response latency of the LGMD/DCMD depend on stimulus position within the visual field. Spatial and temporal integration experiments in which several local motion stimuli were activated either simultaneously or at fixed delays reveal that the LGMD processes local motion in a strongly sublinear way. Thus the neuron's integration properties seem to depend on several factors including its dendritic morphology, the local characteristics of afferent fiber inputs, and inhibition mediated by different pathways or by voltage-gated conductances. Our study shows that the selectivity of this looming sensitive neuron to approaching objects relies on more complex biophysical mechanisms than previously thought.

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PubMed2003

Interpolating between Cellular Biophysics and Computation in Single Neurons.

What types of computations are performed on synaptic inputs within the dendritic trees of single neurons? In this issue of Neuron, present a systematic method to reduce complex, biophysically realistic neuron models to more tractable, simplified two-layered neural networks that could shed some light on this issue.

Biophysical Phenomena↗
PubMed2003

Human visual object categorization can be described by models with low memory capacity.

Studies of high-level models of visual object categorization have left unresolved issues of neurobiological relevance, including how features are extracted from the image and the role played by memory capacity in categorization performance. We compared the ability of a comprehensive set of models to match the categorization performance of human observers while explicitly accounting for the models' numbers of free parameters. The most successful models did not require a large memory capacity, suggesting that a sparse, abstracted representation of category properties may underlie categorization performance. This type of representation--different from classical prototype abstraction--could also be extracted directly from two-dimensional images via a biologically plausible early-vision model, rather than relying on experimenter-imposed features.

Adolescent↗
PubMed2002

Multiplicative computation in a visual neuron sensitive to looming.

Multiplicative operations are important in sensory processing, but their biophysical implementation remains largely unknown. We investigated an identified neuron (the lobula giant movement detector, LGMD, of locusts) whose output firing rate in response to looming visual stimuli has been described by two models, one of which involves a multiplication. In this model, the LGMD multiplies postsynaptically two inputs (one excitatory, one inhibitory) that converge onto its dendritic tree; in the other model, inhibition is presynaptic to the LGMD. By using selective activation and inactivation of pre- and postsynaptic inhibition, we show that postsynaptic inhibition has a predominant role, suggesting that multiplication is implemented within the neuron itself. Our pharmacological experiments and measurements of firing rate versus membrane potential also reveal that sodium channels act both to advance the response of the LGMD in time and to map membrane potential to firing rate in a nearly exponential manner. These results are consistent with an implementation of multiplication based on dendritic subtraction of two converging inputs encoded logarithmically, followed by exponentiation through active membrane conductances.

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PubMed2002

Stimulus encoding and feature extraction by multiple sensory neurons.

Neighboring cells in topographical sensory maps may transmit similar information to the next higher level of processing. How information transmission by groups of nearby neurons compares with the performance of single cells is a very important question for understanding the functioning of the nervous system. To tackle this problem, we quantified stimulus-encoding and feature extraction performance by pairs of simultaneously recorded electrosensory pyramidal cells in the hindbrain of weakly electric fish. These cells constitute the output neurons of the first central nervous stage of electrosensory processing. Using random amplitude modulations (RAMs) of a mimic of the fish's own electric field within behaviorally relevant frequency bands, we found that pyramidal cells with overlapping receptive fields exhibit strong stimulus-induced correlations. To quantify the encoding of the RAM time course, we estimated the stimuli from simultaneously recorded spike trains and found significant improvements over single spike trains. The quality of stimulus reconstruction, however, was still inferior to the one measured for single primary sensory afferents. In an analysis of feature extraction, we found that spikes of pyramidal cell pairs coinciding within a time window of a few milliseconds performed significantly better at detecting upstrokes and downstrokes of the stimulus compared with isolated spikes and even spike bursts of single cells. Coincident spikes can thus be considered "distributed bursts." Our results suggest that stimulus encoding by primary sensory afferents is transformed into feature extraction at the next processing stage. There, stimulus-induced coincident activity can improve the extraction of behaviorally relevant features from the stimulus.

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PubMedDate not supplied

Multiplication and stimulus invariance in a looming-sensitive neuron.

Multiplicative operations and invariance of neuronal responses are thought to play important roles in the processing of neural information in many sensory systems. Yet the biophysical mechanisms that underlie both multiplication and invariance of neuronal responses in vivo, either at the single cell or at the network level, remain to a large extent unknown. Recent work on an identified neuron in the locust visual system (the LGMD neuron) that responds well to objects looming on a collision course towards the animal suggests that this cell represents a good model to investigate the biophysical basis of multiplication and invariance at the single neuron level. Experimental and theoretical results are consistent with multiplication being implemented by subtraction of two logarithmic terms followed by exponentiation via active membrane conductances, according to a x 1/b = exp(log(a) - log(b)). Invariance appears to be in part due to non-linear integration of synaptic inputs within the dendritic tree of this neuron.

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