The common marmoset (Callithrix jacchus) is poised to become a standard nonhuman primate aging model. With an average lifespan of 5 to 7 years and a maximum lifespan of 16.5 years, marmosets are the shortest-lived anthropoid primates. They display age-related changes in pathologies that mirror those seen in humans, such as cancer, amyloidosis, diabetes, and chronic renal disease. They also display predictable age-related differences in lean mass, calf circumference, circulating albumin, hemoglobin, and hematocrit. Features of spontaneous sensory and neurodegenerative change—for example, reduced neurogenesis, β-amyloid deposition in the cerebral cortex, loss of calbindin D28k binding, and evidence of presbycusis—appear between the ages of 7 and 10 years. Variation among colonies in the age at which neurodegenerative change occurs suggests the interesting possibility that marmosets could be specifically managed to produce earlier versus later occurrence of degenerative conditions associated with differing rates of damage accumulation. In addition to the established value of the marmoset as a model of age-related neurodegenerative change, this primate can serve as a model of the integrated effects of aging and obesity on metabolic dysfunction, as it displays evidence of such dysfunction associated with high body weight as early as 6 to 8 years of age.

The ability of an animal to detect weak sensory signals is limited, in part, by statistical fluctuations in the spike activity of sensory afferent nerve fibers. In weakly electric fish, probability coding (P-type) electrosensory afferents encode amplitude modulations of the fish's self-generated electric field and provide information necessary for electrolocation. This study characterizes the statistical properties of baseline spike activity in P-type afferents of the brown ghost knifefish, Apteronotus leptorhynchus. Shortterm variability, as measured by the interspike interval (ISI) distribution, is moderately high with a mean ISI coefficient of variation of 44%. Analysis of spike train variability on longer time scales, however, reveals a remarkable degree of regularity. The regularizing effect is maximal for time scales on the order of a few hundred milliseconds, which matches functionally relevant time scales for natural behaviors such as prey detection. Using highorder interval analysis, count analysis, and Markov-order analysis we demonstrate that the observed regularization is associated with memory effects in the ISI sequence which arise from an underlying nonrenewal process. In most cases, a Markov process of at least fourth-order was required to adequately describe the dependencies. Using an ideal observer paradigm, we illustrate how regularization of the spike train can significantly improve detection performance for weak signals. This study emphasizes the importance of characterizing spike train variability on multiple time scales, particularly when considering limits on the detectability of weak sensory signals. Key words: electrosensory afferent; electrolocation; interspike interval analysis; Markov process; spike train variability; weak signal detectionSurvival in an animal's natural environment is dependent on the ability to detect behaviorally relevant stimuli, such as those caused by predators and prey. Being able to reliably and efficiently detect such signals at weak levels confers a competitive advantage. Thus many sensory systems, including the electrosensory system discussed here, have presumably experienced selective pressures over the course of evolution to improve detection performance for weak sensory signals.The decision of whether or not a stimulus is present must ultimately be based on a change in the spike activity of primary afferent nerve fibers. In many cases, this change must be detected in the presence of ongoing spontaneous activity. Intuitively, a subtle change in spike activity caused by a weak external signal should be easier to detect when the baseline activity is regular and predictable than when it is irregular and subject to random fluctuations. To understand the limits on signal detection performance, it is thus important to characterize the variability of baseline activity in primary afferent spike trains.A common approach for characterizing spike train variability is by analysis of the first-order interspike interval (ISI) distribution (Hagiwara, 1954;Moore et al., ...

The reverberation time (RT) is an important parameter for characterizing the quality of an auditory space. Sounds in reverberant environments are subject to coloration. This affects speech intelligibility and sound localization. Many state-of-the-art audio signal processing algorithms, for example in hearing-aids and telephony, are expected to have the ability to characterize the listening environment, and turn on an appropriate processing strategy accordingly. Thus, a method for characterization of room RT based on passively received microphone signals represents an important enabling technology. Current RT estimators, such as Schroeder's method, depend on a controlled sound source, and thus cannot produce an online, blind RT estimate. Here, a method for estimating RT without prior knowledge of sound sources or room geometry is presented. The diffusive tail of reverberation was modeled as an exponentially damped Gaussian white noise process. The time-constant of the decay, which provided a measure of the RT, was estimated using a maximum-likelihood procedure. The estimates were obtained continuously, and an order-statistics filter was used to extract the most likely RT from the accumulated estimates. The procedure was illustrated for connected speech. Results obtained for simulated and real room data are in good agreement with the real RT values.

In real-world situations animals are exposed to multiple sound sources originating from different locations. Most vertebrates have little difficulty in attending to selected sounds in the presence of distractors, even though sounds may overlap in time and frequency. This chapter selectively reviews behavioral and physiological data relevant to hearing in complex auditory environments. Behavioral data suggest that animals use spatial hearing and integrate information in spectral and temporal domains to determine sound source identity. Additionally, attentional mechanisms help improve hearing performance when distractors are present. On the physiological side, although little is known of where and how auditory objects are created in the brain, studies show that neurons extract behaviorally important features in parallel hierarchically arranged pathways. At the highest levels in the pathway these features are often represented in the form of neural maps. Further, it is now recognized that descending auditory pathways can modulate information processing in the ascending pathway, leading to improvements in signal detectability and response selectivity, perhaps even mediating attention. These issues and their relevance to hearing in real-world conditions are discussed with respect to several model systems for which both behavioral and physiological data are available.

A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding) and fidelity (decoding). The threshold mimics a post-synaptic membrane (a low-pass filter) and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint). The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus) and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current) are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code.