Evidence that cochlear-implanted deaf patients are better multisensory integrators.

The cochlear implant (CI) is a neuroprosthesis that allows profoundly deaf patients to recover speech intelligibility. This recovery goes through long-term adaptative processes to build coherent percepts from the coarse information delivered by the implant. Here we analyzed the longitudinal postimplantation evolution of word recognition in a large sample of CI users in unisensory (visual or auditory) and bisensory (visuoauditory) conditions. We found that, despite considerable recovery of auditory performance during the first year postimplantation, CI patients maintain a much higher level of word recognition in speechreading conditions compared with normally hearing subjects, even several years after implantation. Consequently, we show that CI users present higher visuoauditory performance when compared with normally hearing subjects with similar auditory stimuli. This better performance is not only due to greater speechreading performance, but, most importantly, also due to a greater capacity to integrate visual input with the distorted speech signal. Our results suggest that these behavioral changes in CI users might be mediated by a reorganization of the cortical network involved in speech recognition that favors a more specific involvement of visual areas. Furthermore, they provide crucial indications to guide the rehabilitation of CI patients by using visually oriented therapeutic strategies.

Efficient computation and cue integration with noisy population codes.

The brain represents sensory and motor variables through the activity of large populations of neurons. It is not understood how the nervous system computes with these population codes, given that individual neurons are noisy and thus unreliable. We focus here on two general types of computation, function approximation and cue integration, as these are powerful enough to handle a range of tasks, including sensorimotor transformations, feature extraction in sensory systems and multisensory integration. We demonstrate that a particular class of neural networks, basis function networks with multidimensional attractors, can perform both types of computation optimally with noisy neurons. Moreover, neurons in the intermediate layers of our model show response properties similar to those observed in several multimodal cortical areas. Thus, basis function networks with multidimensional attractors may be used by the brain to compute efficiently with population codes.

Reading population codes: a neural implementation of ideal observers.

Many sensory and motor variables are encoded in the nervous system by the activities of large populations of neurons with bell-shaped tuning curves. Extracting information from these population codes is difficult because of the noise inherent in neuronal responses. In most cases of interest, maximum likelihood (ML) is the best read-out method and would be used by an ideal observer. Using simulations and analysis, we show that a close approximation to ML can be implemented in a biologically plausible model of cortical circuitry. Our results apply to a wide range of nonlinear activation functions, suggesting that cortical areas may, in general, function as ideal observers of activity in preceding areas.

Narrow versus wide tuning curves: What's best for a population code?

Neurophysiologists are often faced with the problem of evaluating the quality of a code for a sensory or motor variable, either to relate it to the performance of the animal in a simple discrimination task or to compare the codes at various stages along the neuronal pathway. One common belief that has emerged from such studies is that sharpening of tuning curves improves the quality of the code, although only to a certain point; sharpening beyond that is believed to be harmful. We show that this belief relies on either problematic technical analysis or improper assumptions about the noise. We conclude that one cannot tell, in the general case, whether narrow tuning curves are better than wide ones; the answer depends critically on the covariance of the noise. The same conclusion applies to other manipulations of the tuning curve profiles such as gain increase.

Statistically efficient estimation using population coding.

Coarse codes are widely used throughout the brain to encode sensory and motor variables. Methods designed to interpret these codes, such as population vector analysis, are either inefficient (the variance of the estimate is much larger than the smallest possible variance) or biologically implausible, like maximum likelihood. Moreover, these methods attempt to compute a scalar or vector estimate of the encoded variable. Neurons are faced with a similar estimation problem. They must read out the responses of the presynaptic neurons, but, by contrast, they typically encode the variable with a further population code rather than as a scalar. We show how a nonlinear recurrent network can be used to perform estimation in a near-optimal way while keeping the estimate in a coarse code format. This work suggests that lateral connections in the cortex may be involved in cleaning up uncorrelated noise among neurons representing similar variables.