TY  - JOUR
AB  - We have recently demonstrated the emergence of dynamic feature sensitivity through exposure to formative stimuli in a real-time neuromorphic system implementing a hybrid analog/digital network of spiking neurons. This network, inspired by models of auditory processing in mammals, includes several mutually connected layers with distance-dependent transmission delays and learning in the form of spike timing dependent plasticity, which effects stimulus-driven changes in the network connectivity. Here we present results that demonstrate that the network is robust to a range of variations in the stimulus pattern, such as are found in naturalistic stimuli and neural responses. This robustness is a property critical to the development of realistic, electronic neuromorphic systems. We analyze the variability of the response of the network to “noisy” stimuli which allows us to characterize the acuity in information-theoretic terms. This provides an objective basis for the quantitative comparison of networks, their connectivity patterns, and learning strategies, which can inform future design decisions. We also show, using stimuli derived from speech samples, that the principles are robust to other challenges, such as variable presentation rate, that would have to be met by systems deployed in the real world. Finally we demonstrate the potential applicability of the approach to real sounds.
DA  - 2014
DO  - 10.3389/fnins.2013.00278
KW  - neuromorphic
KW  - VLSI
KW  - information
KW  - plasticity
KW  - modeling
KW  - auditory
LA  - eng
IS  - 278
M2  - 1
PY  - 2014
SN  - 1662-453X
SP  - 1-10
T2  - Neuromorphic Engineering
TI  - A robust sound perception model suitable for neuromorphic implementation
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-26549844
Y2  - 2024-11-22T09:44:18
ER  -