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Artur Luczak

3 papers in the library · 5 citations · publishing 2021-2024

Papers

Psilocybin reduces functional correlation and the encoding of spatial information by neurons in mouse retrosplenial cortex

European Journal of Neuroscience October 4, 2024 Victorita E. Ivan, David P Tomàs-Cuesta, Ingrid M. Esteves et al. 4 citations

Psychedelic drugs such as psilocybin reduce the place specificity of neurons in the retrosplenial cortex of mice navigating a treadmill, making neural activity less tied to distinct locations. The stability of place-related activity across trials also decreases, and functional correlations among simultaneously recorded neurons are lowered. These effects are blocked by the serotonin 2A receptor antagonist ketanserin, indicating that the 5-HT2A receptor mediates them. The findings suggest that psychedelics increase the entropy of neural signaling, which may contribute to the disorientation often reported by humans after taking psychedelics.

Psilocybin reduces functional connectivity and the encoding of spatial information by neurons in mouse retrosplenial cortex

April 22, 2024 Victorita E. Ivan, David P Tomàs-Cuesta, Ingrid M. Esteves et al. 1 citation

Psilocybin, a classic psychedelic, reduces the spatial specificity and stability of neural activity in the retrosplenial cortex of mice navigating a treadmill. Place-related firing of neurons became less selective for distinct locations, and the consistency of this activity across trials decreased. Functional connectivity between simultaneously recorded neurons also declined. Most of these effects were blocked by the serotonin 2A receptor antagonist ketanserin, implicating 5-HT2AR signaling. The findings align with the proposal that psychedelics increase neural entropy and may explain the disorientation often reported by humans after taking such drugs.

Predictive Neuronal Adaptation as a Basis for Consciousness.

Frontiers in Systems Neuroscience January 1, 2021 Artur Luczak, Yoshimasa Kubo

Consciousness may arise from individual neurons minimizing surprise—the mismatch between actual and expected activity. Simulations show that as a neural network learns, neuron-level surprise changes similarly to the development of conscious skills in humans. Adapting neuronal activity to reduce surprise at fast time scales (tens of milliseconds) improved network performance, likely because each neuron uses its internal predictive model to respond more effectively. The authors propose that such predictive adaptation is a basic building block of conscious processing and offer an equation quantifying consciousness as surprise minus adaptation error. They argue that substances affecting neuronal adaptation should also affect consciousness, and note consistency with global workspace theory, integrated information, attention schema theory, and predictive processing.