:Psychophysics

We use auditory interval discrimination tasks to characterize, test and generate predictions regarding the neural basis of temporal processing. We have shown that temporal discrimination undergoes perceptual learning: if you train on a 100 ms interval bounded by 1 kHz tones you improve with training. However, learning is interval specific. If you train with 100 ms intervals you do not improve on 50 or 200 ms intervals. This research provides insights into how the brain process time, and suggests different specific neural circuits are dedicated to different intervals (Karmarkar & Buonomano, 2003).

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:Neurophysiology

Electrophysiological studies in our laboratory focus on two principal goals:

1. Understanding the learning rules that govern long-term and homeostatic synaptic plasticity;
2. To use cortical networks in vitro to examine neural dynamics, that is, to determine how hundreds of neurons operate together to process information;

By recording from two neurons simultaneously in vitro, we have shown that neurons can 'tell time' on scales much longer than monosynaptic transmission delays (Buonomano, 2003).

:Artificial Neural Networks:

Given the complexity and highly nonlinear nature of neural networks we use computer simulations to examine how neural networks may underlie information processing, and how this ability may emerge in a self-organizing manner. Computer simulations show that simple neural circuits can exhibit interval selectivity in the absence of specialized timing mechanisms. Parallel changes at multiple synaptic sites can result in interval tuning. These results suggest that orchestrated changes at excitatory and inhibitory synapses provide for more powerful computational algorithms (Buonomano, 2000).