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M Mascaro

Publications and source records attributed to M Mascaro.

4 recordsLinked to original sources
PubMed2004

Visuotopic mapping through a multichannel stimulating implant in primate V1.

We report on our efforts to establish an animal model for the development and testing of a cortical visual prostheses. One-hundred-fifty-two electrodes were implanted in the primary visual cortex of a rhesus monkey. The electrodes were made from iridium with an activated iridium oxide film, which has a large charge capacity for a given surface area, and insulated with parylene-C. One-hundred-fourteen electrodes were functional after implantation. The activity of small (2-3) neuronal clusters was first recorded to map the visually responsive region corresponding to each electrode. The animal was then trained in a memory (delayed) saccade task, first with a visual target, then to a target defined by direct cortical stimulation with coordinates specified by the stimulating electrode's mapped receptive field. The SD of saccade endpoints was approximately 2.5 larger for electrically stimulated versus visual saccades; nevertheless, when trial-to-trial scatter was averaged out, the correlation between saccade end points and receptive field locations was highly significant and approached unity after several months of training. Five electrodes were left unused until the monkey was fully trained; when these were introduced, the receptive field-saccade correlations were high on the first day of use (R = 0.85, P = 0.03 for angle, R = 0.98, P < 0.001 for eccentricity), indicating that the monkey had not learned to perform the task empirically by memorizing reward zones. The results of this experiment suggest the potential for rigorous behavioral testing of cortical visual prostheses in the macaque.

Animals↗
PubMed2001

Attractor networks for shape recognition.

We describe a system of thousands of binary perceptrons with coarse-oriented edges as input that is able to recognize shapes, even in a context with hundreds of classes. The perceptrons have randomized feedforward connections from the input layer and form a recurrent network among themselves. Each class is represented by a prelearned attractor (serving as an associative hook) in the recurrent net corresponding to a randomly selected subpopulation of the perceptrons. In training, first the attractor of the correct class is activated among the perceptrons; then the visual stimulus is presented at the input layer. The feedforward connections are modified using field-dependent Hebbian learning with positive synapses, which we show to be stable with respect to large variations in feature statistics and coding levels and allows the use of the same threshold on all perceptrons. Recognition is based on only the visual stimuli. These activate the recurrent network, which is then driven by the dynamics to a sustained attractor state, concentrated in the correct class subset and providing a form of working memory. We believe this architecture is more transparent than standard feedforward two-layer networks and has stronger biological analogies.

Animals↗
PubMed1999

Effective neural response function for collective population states.

Collective behaviour of neural networks often divides the ensemble of neurons into sub-classes by neuron type; by selective synaptic potentiation; or by mode of stimulation. When the number of classes becomes larger than two, the analysis, even in a mean-field theory, loses its intuitive aspect because of the number of dimensions of the space of dynamical variables. Often one is interested in the behaviour of a reduced set of sub-populations (in focus) and in their dependence on the system's parameters, as in searching for coexistence of spontaneous activity and working memory; in the competition between different working memories; in the competition between working memory and a new stimulus; or in the interaction between selective activity in two different neural modules. For such cases we present a method for reducing the dimensionality of the system to one or two dimensions, even when the total number of populations involved is higher. In the reduced system the familiar intuitive tools apply and the analysis of the dependence of different network states on ambient parameters becomes transparent. Moreover, when the coding of states in focus is sparse, the computational complexity is much reduced. Beyond the analysis, we present a set of detailed examples. We conclude with a discussion of questions of stability in the reduced system.

Animals↗