Spiking neural network model of sound localisation using the interaural intensity difference

In this paper, a spiking neural network (SNN) architecture to simulate the sound localization ability of the mammalian auditory pathways using the interaural intensity difference cue is presented. The lateral superior olive was the inspiration for the architecture, which required the integration of an auditory periphery (cochlea) model and a model of the medial nucleus of the trapezoid body. The SNN uses leaky integrateand-fire excitatory and inhibitory spiking neurons, facilitating synapses and receptive fields. Experimentally derived headrelated transfer function (HRTF) acoustical data from adult domestic cats were employed to train and validate the localization ability of the architecture, training used the supervised learning algorithm called the remote supervision method to determine the azimuthal angles. The experimental results demonstrate that the architecture performs best when it is localizing high-frequency sound data in agreement with the biology, and also shows a high degree of robustness when the HRTF acoustical data is corrupted by noise.

Pain expressiveness and altruistic behavior

Predictions which invoke evolutionary mechanisms ar e hard to test. Agent-based modeling in artificial life offers a way to simulate behaviors and interac tions in specific physical or social environments o ver many generations. The outcomes have implications fo r understanding adaptive value of behaviors in context. Pain-related behavior in animals is communicated to other animals that might protect or help, or might exploit or predate. An agent-based model simulated the effects of displaying or not displaying pain (expresser/non-expresser strategies) when injured, and of helping, ignoring or exploiting another in pain (altruistic/non-altruistic/selfish strategies) . Agents modeled in MATLAB interacted at random while foraging (gaining energy); random injury inte rrupted foraging for a fixed time unless help from an altruistic agent, who paid an energy cost, speeded recovery. Environmental and social conditions also varied, and each model ran for 10,000 iterations. Findings were meaningful in that, in general, conti ngencies evident from experimental work with a variety of mammals, over a few interactions, were r eplicated in the agent-based model after selection pressure over many generations. More energy-demandi ng expression of pain reduced its frequency in successive generations, and increasing injury frequ ency resulted in fewer expressers and altruists. Allowing exploitation of injured agents decreased e xpression of pain to near zero, but altruists remained. Decreasing costs or increasing benefits o f helping hardly changed its frequency, while increasing interaction rate between injured agents and helpers diminished the benefits to both. Agent- based modeling allows simulation of complex behavio urs and environmental pressures over evolutionary time.