Usher syndrome type I (USH1) is an autosomal recessive disorder characterized by congenital sensorineural hearing loss, vestibular dysfunction and visual impairment due to early onset retinitis pigmentosa (RP). So far, six loci (USH1A-USH1F) have been mapped, but only two USH1 genes have been identified: MYO7A for USH1B and the gene encoding harmonin for USH1C. We identified a Cuban pedigree linked to the locus for Usher syndrome type 1D (MIM 601067) within the q2 region of chromosome 10). Affected individuals present with congenital deafness and a highly variable degree of retinal degeneration. Using a positional candidate approach, we identified a new member of the cadherin gene superfamily, CDH23. It encodes a protein of 3,354 amino acids with a single transmembrane domain and 27 cadherin repeats. In the Cuban family, we detected two different mutations: a severe course of the retinal disease was observed in individuals homozygous for what is probably a truncating splice-site mutation (c.4488G-->C), whereas mild RP is present in individuals carrying the homozygous missense mutation R1746Q. A variable expression of the retinal phenotype was seen in patients with a combination of both mutations. In addition, we identified two mutations, Delta M1281 and IVS51+5G-->A, in a German USH1 patient. Our data show that different mutations in CDH23 result in USH1D with a variable retinal phenotype. In an accompanying paper, it is shown that mutations in the mouse ortholog cause disorganization of inner ear stereocilia and deafness in the waltzer mouse.

Although neuronal excitability is well understood and accurately modeled over timescales of up to hundreds of milliseconds, it is currently unclear whether extrapolating from this limited duration to longer behaviorally relevant timescales is appropriate. Here we used an extracellular recording and stimulation paradigm that extends the duration of single-neuron electrophysiological experiments, exposing the dynamics of excitability in individual cultured cortical neurons over timescales hitherto inaccessible. We show that the long-term neuronal excitability dynamics is unstable and dominated by critical fluctuations, intermittency, scale-invariant rate statistics, and long memory. These intrinsic dynamics bound the firing rate over extended timescales, contrasting observed short-term neuronal response to stimulation onset. Furthermore, the activity of a neuron over extended timescales shows transitions between quasi-stable modes, each characterized by a typical response pattern. Like in the case of rate statistics, the short-term onset response pattern that often serves to functionally define a given neuron is not indicative of its long-term ongoing response. These observations question the validity of describing neuronal excitability based on temporally restricted electrophysiological data, calling for in-depth exploration of activity over wider temporal scales. Such extended experiments will probably entail a different kind of neuronal models, accounting for the unbounded range, from milliseconds up.

Learning in multilayer neural networks (MNNs) relies on continuous updating of large matrices of synaptic weights by local rules. Such locality can be exploited for massive parallelism when implementing MNNs in hardware. However, these update rules require a multiply and accumulate operation for each synaptic weight, which is challenging to implement compactly using CMOS. In this paper, a method for performing these update operations simultaneously (incremental outer products) using memristor-based arrays is proposed. The method is based on the fact that, approximately, given a voltage pulse, the conductivity of a memristor will increment proportionally to the pulse duration multiplied by the pulse magnitude if the increment is sufficiently small. The proposed method uses a synaptic circuit composed of a small number of components per synapse: one memristor and two CMOS transistors. This circuit is expected to consume between 2% and 8% of the area and static power of previous CMOS-only hardware alternatives. Such a circuit can compactly implement hardware MNNs trainable by scalable algorithms based on online gradient descent (e.g., backpropagation). The utility and robustness of the proposed memristor-based circuit are demonstrated on standard supervised learning tasks.

The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on a trial-to-trial basis. This is probably the most severe biophysical constraint on putative time-based primitives of stimulus representation in neuronal networks. Here we show that in spontaneously developing large-scale random networks of cortical neurons in vitro the order in which neurons are recruited following each stimulus is a naturally emerging representation primitive that is invariant to significant temporal changes in spike times. With a relatively small number of randomly sampled neurons, the information about stimulus position is fully retrievable from the recruitment order. The effective connectivity that makes order-based representation invariant to time warping is characterized by the existence of stations through which activity is required to pass in order to propagate further into the network. This study uncovers a simple invariant in a noisy biological network in vitro; its applicability under in vivo constraints remains to be seen.

A candidate gene for Norrie disease, an X-linked disorder characterized by blindness, deafness and mental disturbances, was recently isolated and found to contain microdeletions in numerous patients. No strong homologies were identified. By studying the number and spacing of cysteine residues, we now detect homologies between the Norrie gene product and a C-terminal domain which is common to a group of proteins including mucins. Three newly-characterized missense mutations, replacing evolutionarily conserved cysteines or creating new cysteine codons, emphasize the functional importance of these sites. These findings and the clinical features of this disorder suggest a possible role for the Norrie gene in neuroectodermal cell-cell interaction.

A group of 46 European patients with mucopolysaccharidosis type I (MPS I) was screened for mutations of the alpha-L-iduronidase gene. The 2 common nonsense mutations, W402X and Q70X, were identified in, respectively, 37% and 35% of mutant alleles. Considerable differences were seen in the frequency of these 2 mutations in patients from North Europe (Norway and Finland) and other European countries (mainly The Netherlands and Germany). In Scandinavia, W402X and Q70X account for 17% and 62% of the MPS I alleles, respectively, while in other European countries W402X is about 2.5 times more frequent (48%) than Q70X (19%). Eight novel mutations are described including 4 missense mutations, 1 nonsense mutation, 1 insertion of 2 base pairs, and 2 deletions of 1 and 12 base pairs.

Neural representation is pivotal in neuroscience. Yet, the large number and variance of underlying determinants make it difficult to distinguish general physiologic constraints on representation. Here we offer a general approach to the issue, enabling a systematic and well controlled experimental analysis of constraints and tradeoffs, imposed by the physiology of neuronal populations, on plausible representation schemes. Using in vitro networks of rat cortical neurons as a model system, we compared the efficacy of different kinds of "neural codes" to represent both spatial and temporal input features. Two rate-based representation schemes and two time-based representation schemes were considered. Our results indicate that, by large, all representation schemes perform well in the various discrimination tasks tested, indicating the inherent redundancy in neural population activity; Nevertheless, differences in representation efficacy are identified when unique aspects of input features are considered. We discuss these differences in the context of neural population dynamics.

Responses of individual neurons to ongoing input are highly variable, reflecting complex threshold dynamics. Experimental access to this threshold dynamics is required in order to fully characterize neuronal input–output relationships. The challenge is practically intractable using present day experimental paradigms due to the cumulative, non-linear interactions involved. Here we introduce the Neuronal Response Clamp, a closed-loop technique enabling control over the instantaneous response probability of the neuron. The potential of the technique is demonstrated by showing direct access to threshold dynamics of cortical neuron in vitro using extracellular recording and stimulation, over timescales ranging from seconds to many hours. Moreover, the method allowed us to expose the sensitivity of threshold dynamics to spontaneous input from the network in which the neuron is embedded. The Response-Clamp technique follows the rationale of the voltage-clamp and dynamic-clamp approaches, extending it to the neuron's spiking behavior. The general framework offered here is applicable in the study of other neural systems, beyond the single neuron level.