Sara A. Solla

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We have used information-theoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural network. By removing unimportant weights from a network, several improvements can be expected: better generalization, fewer training examples required, and improved speed of learning and/or classification. The basic idea is to(More)
Cortical layering is a hallmark of the mammalian neocortex and a major determinant of local synaptic circuit organization in sensory systems. In motor cortex, the laminar organization of cortical circuits has not been resolved, although their input-output operations are crucial for motor control. Here, we developed a general approach for estimating(More)
  • References, D B Schwartz, +5 authors B A Huberman Generalization
  • 1992
It has been observed in numerical simulations that a weight decay can improve generalization in a feed-forward neural network. This paper explains why. It is proven that a weight decay has two e ects in a linear network. First, it suppresses any irrelevant components of the weight vector by choosing the smallest vector that solves the learning problem.(More)
Single-unit activity in the neostriatum of awake monkeys shows a marked dependence on expected reward. Responses to visual cues differ when animals expect primary reinforcements, such as juice rewards, in comparison to secondary reinforcements, such as tones. The mechanism of this reward-dependent modulation has not been established experimentally. To(More)
Abst ract . Learning in layered neu ral networks is posed as the minimizat ion of an error function defined over the training set. A probabilist ic interpretation of the target act ivities suggests the use of relat ive ent ropy as an error measure. We investigate t he merits of using this error function over t he traditional quad ratic function for gradient(More)
A robust identification algorithm has been developed for linear, time-invariant, multiple-input single-output systems, with an emphasis on how this algorithm can be used to estimate the dynamic relationship between a set of neural recordings and related physiological signals. The identification algorithm provides a decomposition of the system output such(More)
We study the dynamics of excitable integrate-and-fire neurons in a small-world network. At low densities p of directed random connections, a localized transient stimulus results either in self-sustained persistent activity or in a brief transient followed by failure. Averages over the quenched ensemble reveal that the probability of failure changes from 0(More)
The prefrontal cortex and basal ganglia are deeply implicated in working memory. Both structures are subject to dopaminergic neuromodulation in a way that exerts a critical influence on the proper operation of working memory. We present a novel network model to elucidate the role of phasic dopamine in the interaction of these two structures in initiating(More)
Loss of hand use is considered by many spinal cord injury survivors to be the most devastating consequence of their injury. Functional electrical stimulation (FES) of forearm and hand muscles has been used to provide basic, voluntary hand grasp to hundreds of human patients. Current approaches typically grade pre-programmed patterns of muscle activation(More)
Movement representation by the motor cortex (M1) has been a theoretical interest for many years, but in the past several years it has become a more practical question, with the advent of the brain-machine interface. An increasing number of groups have demonstrated the ability to predict a variety of kinematic signals on the basis of M1 recordings and to use(More)