A mathematician wakes up and smells smoke. He goes to the hall and sees both a fire and a fire hose. He thinks for a moment and exclaims, “Ah, a solution exists!” and then goes back to bed.
Many researchers studying consciousness are similarly content to theorize over the mere feasibility of explaining phenomenal experiences, instead of examining the anatomical substrates that will allow us to answer the more intermediary questions. As an exercise in demonstrating the utility of these intermediary steps for our theories, this paper will examine how human neural circuit diagrams could immediately improve our understanding of the relationship between various components of sleep and consciousness.
One of the most prominent theories of the function of sleep is metabolic restoration. Benington and Heller (1995) propose that glycogen stores in astrocytes are exhausted during waking and replenished during sleep, and that the need for sleep is dictated by high levels of adenosine, which promotes neural synchronization. The authors advocate that astrocytes are necessary for preserving the proper cellular environment for neurons during and between action potentials. So, in their model the levels of glycogen in astrocytes indirectly affect neuronal responsiveness. But more recent research suggests that astrocytes may be directly involved in synaptic networks via the calcium-dependent release of glutamate to neurons (Fiacco et al, 2009). Additionally, astrocytes have been found to release ATP, which is quickly hydrolyzed to adenosine, and then causes a persistent synaptic suppression at neurons with adenosine-1 receptors (Pascual et al, 2005). Moreover, when mice without adenosine-1 receptors are sleep deprived, they do not display any change in synchronized slow-wave activity, even though synchronized slow wave activity does vary directly with previous waking duration in wildtype mice (Bjorness et al, 2009). This implies a mechanism through which adenosine could regulate sleep homeostasis. Overall, Benington and Heller’s original proposal has been falsified in some respects and confirmed in others since its publication, as a result of anatomical data. Given a working circuit diagram in the cerebral cortex and/or thalamus of either mice or humans, the metabolic theory could be further refined and potentially falsified.
Another major theory for the function of sleep is that sleep allows for memory consolidation. Sejnowski and Destexhe (2000) postulate that bursts of synchronized high-frequency action potentials in thalamic neurons depolarize dendrites but not the soma, allowing calcium ions to enter dendrites and affect gene expression in the nucleus. This allows for plasticity but comes at a cost, as synchronized networks can feedforward into epilepsy if not properly regulated. This inhibitory regulation is difficult during the day because we are constantly in the presence of sensory stimuli. So, since sleep allows synchronized high frequency action potentials without the risk of uncontrolled positive feedback, sleep may be the toll we pay for plasticity. The feasibility of their argument is based in large part on a reconstruction of synaptic connections of individual neurons in the thalamus. But in order to simulate the proper levels of firing in their model they had to assume that specific levels of GABAA-mediated and excitatory inputs would be afferent to their model neurons. This trial-by-error technique ensures that their computational results make sense but it risks biological irrelevance. Given more anatomical data, the researchers could have ensured the use of biologically plausible synaptic inputs.
The study of other sleep exotica could also benefit from additional anatomical data. Blackmore’s chapter on sleep (2003) touches on the evolution of dreams, a question that would be easier to answer if we knew the differences between our physiological substrates for dreams and those of other animals. Leberge’s (1990) article on lucid dreaming includes the speculation that seritonergic neurons form a system that normally inhibits hallucinations but is itself inhibited in REM sleep, a hypothesis that requires anatomical data to answer. Iranzo et al (2009) are able to note that a dopaminergic deficiency is likely not the cause of REM sleep behavior disorder, but are unable to conclude which brainstem deficiencies do cause the disorder. Although lesion and knock-out studies are good at falsifying hypotheses, they are unable to suggest any on their own in the way that a large neural circuit data corpus would be able to. Neural anatomical data might one day explain Cheyne and Girard’s (2007) observation that vestibular motor sleep paralysis experiences are associated with bliss while intruder and incubus experiences are not, beyond the presumption that the amygdala is somehow involved. And although Johns (1991) is correct in asserting that self-reports are currently our only gateway into subjective experience, this may not always be the case. Miyawaki et al (2008) were able to use fMRI to determine what subjects were currently viewing, based on training data where subjects saw random images. As anatomical data interfaces with new imaging techniques, the possibilities are tremendous.
If we want to answer the hard question (i.e., explaining phenomenal experiences) we will be best served iterating towards technical solutions of the easy problems. The ultimate solution to this technical problem is the creation of a “connectome,” perhaps initially via diffusion imaging but eventually via serial section transmission electron microscopy. Now, it is quite possible that the individual neuron level will not contain enough information to recover the computational detail necessary for consciousness. Indeed, it is conceivable that the density of NMDA receptors on a given pyramidal neuron in the hippocampus could correlate to the information content of a given memory. In that case, we would need to image and reconstruct the circuit at a resolution level that could capture membrane protein receptors. Although less likely, it is also conceivable that small cellular molecules such as microRNAs, which are known to be asymmetrically distributed throughout the brain (Olsen et al, 2009), could play a particular role in producing consciousness. In that case an effort to computationally simulate a conscious mind would be futile in the near future. But we cannot know the answers to these questions until we try. Although it may not be a sexy answer, the truth is that we need more anatomical data before we can intelligently discuss the hard question.
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