Neurons and Objects: The Case of Auditory Cortex

Sounds are encoded into electrical activity in the inner ear, where they are represented (roughly) as patterns of energy in narrow frequency bands. However, sounds are perceived in terms of their high-order properties. It is generally believed that this transformation is performed along the auditory hierarchy, with low-level physical cues computed at early stages of the auditory system and high-level abstract qualities at high-order cortical areas. The functional position of primary auditory cortex (A1) in this scheme is unclear – is it ‘early’, encoding physical cues, or is it ‘late’, already encoding abstract qualities? Here we argue that neurons in cat A1 show sensitivity to high-level features of sounds. In particular, these neurons may already show sensitivity to ‘auditory objects’. The evidence for this claim comes from studies in which individual sounds are presented singly and in mixtures. Many neurons in cat A1 respond to mixtures in the same way they respond to one of the individual components of the mixture, and in many cases neurons may respond to a low-level component of the mixture rather than to the acoustically dominant one, even though the same neurons respond to the acoustically-dominant component when presented alone

INA Early Intervention for Babies at Risk

Brain and nervous system development are experience dependent. Indeed, the sequence of development is laid out genetically, but early environmental events are major contributors to the system’s development and optimal functioning. Various fetal injuries and birth trauma make babies vulnerable to developmental problems: cerebral palsy, seizures, abnormal muscle tone, delayed developmental milestones, sensory integration, and more. Our goal in the study presented here was to improve the neurodevelopmental track of babies at risk using Infant Neural Aquatic. Parent and baby dyads who met initial criteria were recruited for a 5–6 months intervention period through an open invitation, followed by a conversation and signing informed consent. In the beginning and end of intervention period, participants completed questionnaires, and developmental features of the babies were assessed using analysis of neuro-motor and vocal characteristics. Significant neurodevelopmental delta between values at the end and beginning of intervention period, comparing intervention and control, is described, and the strength of INA specific intervention tool is analyzed

Neuroplasticity in Young Age: Computer-Based Early Neurodevelopment Classifier

Neurodevelopmental syndromes, a continuously growing issue, are impairments in the growth and development of the brain and CNS which are pronounced in a variety of emotional, cognitive, motor and social skills. Early assessment and detection of typical, clinically correlated early signs of developmental abnormalities is crucial for early and effective intervention, supporting initiation of early treatment and minimizing neurological and functional deficits. Successful early interventions would then direct to early time windows of higher neural plasticity. Various syndromes are reflected in early vocal and motor characteristics, making them suitable indicators of an infant’s neural development. Performance of the computerized classifiers we developed shows approximately 90% accuracy on a database of diagnosed babies. The results demonstrate the potential of vocal and motor analysis for computer-assisted early detection of neurodevelopmental insults

Summary

Information processing by a sensory system is reflected in the changes in stimulus representation along its successive processing stages. We measured information content and stimulus-induced redundancy in the neural responses to a set of natural sounds in three successive stations of the auditory pathway—inferior colliculus (IC), auditory thalamus (MGB), and primary auditory cortex (A1). Information about stimulus identity was somewhat reduced in single A1 and MGB neurons relative to single IC neurons, when information is measured using spike counts, latency, or temporal spiking patterns. However, most of this difference was due to differences in firing rates. On the other hand, IC neurons were substantially more redundant than A1 and MGB neurons. IC redundancy was largely related to frequency selectivity. Redundancy reduction may be a generic organization principle of neural systems, allowing for easier readout of the identity of complex stimuli in A1 relative to IC