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Marc A Heiser

Publications and source records attributed to Marc A Heiser.

2 recordsLinked to original sources

Experience-dependent adult cortical plasticity requires cognitive association between sensation and reward.

We tested the involvement of cognition in adult experience-dependent neuroplasticity using primate cortical implants. In a prior study, learning an operant sensory discrimination increased cortical excitability and target selectivity. Here, the prior task was separated into three behavioral phases. First, naive animals were exposed to stimulus-reward pairings from the prior study. These yoked animals did not have to discriminate to be rewarded and did not learn the discrimination. The plasticity observed in the prior study did not occur. Second, the animals were classically conditioned to discriminate the same stimuli in a simplified format. Learning was accompanied by increased sensory response strength and an increased range of sensory inputs eliciting responses. The third study recreated the original operant discrimination, and selectivity for task targets increased. These studies demonstrate that cognitive association between sensory stimuli and reinforcers accompanies adult experience-dependent cortical plasticity and suggest that selectivity in representation and action are linked.

Acoustic Stimulation↗

Associative learning shapes the neural code for stimulus magnitude in primary auditory cortex.

Since the dawn of experimental psychology, researchers have sought an understanding of the fundamental relationship between the amplitude of sensory stimuli and the magnitudes of their perceptual representations. Contemporary theories support the view that magnitude is encoded by a linear increase in firing rate established in the primary afferent pathways. In the present study, we have investigated sound intensity coding in the rat primary auditory cortex (AI) and describe its plasticity by following paired stimulus reinforcement and instrumental conditioning paradigms. In trained animals, population-response strengths in AI became more strongly nonlinear with increasing stimulus intensity. Individual AI responses became selective to more restricted ranges of sound intensities and, as a population, represented a broader range of preferred sound levels. These experiments demonstrate that the representation of stimulus magnitude can be powerfully reshaped by associative learning processes and suggest that the code for sound intensity within AI can be derived from intensity-tuned neurons that change, rather than simply increase, their firing rates in proportion to increases in sound intensity.

Acoustic Stimulation↗