Reduced depth in the basins of attraction of cortical attractor states destabilizes neural activity at the network level due to constant statistical fluctuations from stochastic spiking of neurons. In integrate-and-fire network simulations, decreasing NMDA receptor conductances reduces attractor basin depth, destabilizes short-term memory states, and increases distractibility. Cognitive symptoms of schizophrenia—distractibility, working memory deficits, poor attention—could stem from this instability in prefrontal cortical networks. Lower firing rates in orbitofrontal and anterior cingulate cortex may account for negative symptoms like reduced emotions. Decreasing both GABA and NMDA conductances causes switches between attractor states and jumps from spontaneous activity into attractors, linked to positive symptoms such as delusions, paranoia, and hallucinations from shallow basins in temporal lobe semantic memory networks.
Computational neuroscience offers a new approach to classic philosophical problems—the mind-body problem, determinism and free will, and phenomenal consciousness. The mind and brain are different levels of explanation for information processing, and their correspondence can be understood through neural computation. However, a gap remains in understanding how brain events produce subjective experience. The proposed explanation is that 'feeling like something' arises from a computational process involving higher-order thoughts grounded in the world.
Noise from random neuronal spiking gives the brain advantages like probabilistic decision-making but makes it a non-deterministic system, raising implications for free will. Decisions are taken probabilistically between the reasoning system and the implicit reward system. The reasoning system's operation can be described as free will, and consciousness arises from using higher order syntactic thoughts (HOSTs) to correct first order thoughts. When the implicit system decides, we may confabulate a reason, making the feeling of free will an illusion.