Emergent feature sensitivity in a model of the auditory thalamocortical system

Synaptic Delays for Insect-Inspired Temporal Feature Detection in Dynamic Neuromorphic Processors

Spiking neural networks are well-suited for spatiotemporal feature detection and learning, and naturally involve dynamic delay mechanisms in the synapses, dendrites, and axons. Dedicated delay neurons and axonal delay circuits have been considered when implementing such pattern recognition networks in dynamic neuromorphic processors. Inspired by an auditory feature detection circuit in crickets, featuring a delayed excitation by post-inhibitory rebound, we investigate disynaptic delay elements formed by inhibitory-excitatory pairs of dynamic synapses. We configured such disynaptic delay elements in the DYNAP-SE neuromorphic processor and characterized the distribution of delayed excitations resulting from device mismatch. Interestingly, we found that the disynaptic delay elements can be configured such that the timing and magnitude of the delayed excitation depend mainly on the efficacy of the inhibitory and excitatory synapses, respectively, and that a neuron with multiple delay elements can be tuned to respond selectively to a specific pattern. Furthermore, we present a network with one disynaptic delay element that mimics the auditory feature detection circuit of crickets, and we demonstrate how varying synaptic weights, input noise and processor temperature affect the circuit. Dynamic delay elements of this kind open up for synapse level temporal feature tuning with configurable delays of up to 100 ms.

Developmental alterations of the auditory brainstem centers--pathogenetic implications in Sudden Infant Death Syndrome

Sudden Infant Death Syndrome (SIDS), despite the success of campaigns to reduce its risks, is the leading cause of infant death in the Western world. Even though the pathogenesis remains unexplained, brainstem abnormalities of the neuronal network that mediates breathing and protective responses to asphyxia, particularly in the arousal phase from sleep, are believed to play a fundamental role. This is the first study to identify, in SIDS, developmental defects of specific brainstem centers involved in hearing pathways, particularly in the cochlear and vestibular nuclei, in the superior olivary complex and in the inferior colliculus, suggesting a possible influence of the acoustic system on respiratory activity. In 49 SIDS cases and 20 controls an in-depth anatomopathological examination of the autonomic nervous system was performed, with the main aim of detecting developmental alterations of brainstem structures controlling both the respiratory and auditory activities. Overall, a significantly higher incidence of cytoarchitectural alterations of both the auditory and respiratory network components were observed in SIDS victims compared with matched controls. Even if there is not sufficient evidence to presume that developmental defects of brainstem auditory structures can affect breathing, our findings, showing that developmental deficit in the control respiratory areas are frequently accompanied by alterations of auditory structures, highlight an additional important element for the understanding the pathogenetic mechanism of SIDS.

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems.