Chapter 8

Riding The Human Roller-Coaster…
The Rise—Then Fall—of Human Ability Across a Lifetime

  1. Psychologists studying the development of language-specific phonological processing have shown that newborns can discriminate between phonemic inputs (phonemes are the categorically-processed meaning-bearing sound parts of words) from the time of birth. However, there is a long (many-month) path from a capacity to discriminate a difference between very substantially parametrically different acoustic word sounds to the reliable categorization then recognition then symbolically-translated meaning of those sounds as they fly by. Reliable categorization of word sounds in the child’s native language is achieved by the baby’s 3rd or 4th month out in the world; it takes the child most the rest of that first year to put meaning to a word or two. These advances take months of substantially unregulated Hebbian network plasticity to be accomplished.This has been a rich area of study. For reviews, as a starting point, see Kuhl P et al. (2000) A Scientist In The Crib: What Early Learning Tells Us About The Mind; or, to be a little more expansive, see one of the co-author’s (Alison Gopnik) follow-on book The Philosophical Baby: What Children’s Mind’s Tell Us About The Meaning Of Life (2010).For an example of brain and skill-learning progression in a non-language/non-reading domain, see, e.g., Newcombe NS et al Making Space: The development of spatial representation and reasoning (2000); or Dehaene S, The Number Sense: How the Mind Creates Mathematics (revised and updated edition) (2011).
  2. Babies have special machinery that actually substantially physically regresses as they get older that dis-inhibits (in effect, distracts/attracts) the baby brain so that it responds to almost any sensory surprise or difference. That special ability is a great neurological resource for one of the brain’s main jobs in early child development, which is to explore its new world, responding to everything that moves or seems to differ, as, via brain plasticity, the cerebral cortex competitively sorts out the representations of all of those things that it encounters out (and in) there. Empowered by these processes (which are especially strong in early childhood) every baby brain is a curiosity-sorting (surprise-seeking) machine! We have speculated that the failure of this machinery to regress in older childhood (a brain structure called the “zona incerta”, a probable source of this “respond to anything that is different” infant machinery dramatically shrinks across childhood as a function of how it is engaged in the child) might be a contributor to ADHD.
  3. The physical and functional characteristics of the young infant brain have been the subject of tens of thousands of research reports. For general descriptions and reviews from a hard-core neuroscience perspective, you might begin with Dan Sanes’ Development Of The Nervous System (2nd ed) (2006) or Kandel and colleagues benchmark textbook Principles of Neural Science (5th ed) (2012). For a more psychology-nuanced treatment, see Stiles J, The Fundamentals Of Brain Development: Integrating Nature & Nurture (2008). We have extensively tracked functional changes in infant brains forward into adulthood in our own models (most completely documenting the post-natal “maturation” of the rat’s auditory/listening brain). See Chang EF et al (2005) Development of spectral and temporal response selectivity in the primary auditory cortex. PNAS 102:16460 for an example of one of our own (of several) studies documenting the initially sluggish and temporally and spectrally imprecise and physically immature cerebral cortex in young infant animals. See books on child development cited above, for a description of early perceptual and cognitive limitations in very young infant children.The progressive myelination and the growth and elaboration of synaptic interconnections (“neuropil”) in the brains of infant animals and children has been the subject of several thousand studies, dating back to nearly a hundred and fifty years [when Flechsig, Golgi, Nissl and others developed tissue stains that enabled their reconstruction or brain wiring and neurological morphology as a function of age across the human lifespan]. The books cited earlier should set you on the right path to get started with this extensive literature.
  4. Acceleration of “maturation” has been achieved in several visual system studies by heavily stimulating developing brains with strongly modulated visual stimuli (for a review of the control of critical period onset and closure, see, e.g., Hensch TK, 2004, Critical period regulation. Ann Rev Neurosci 27:549). Auditory system “maturation” can be arrested by simply maintaining baby rats in moderate-level noise through their waking hours (see Chang EF, Merzenich MM (2003) Environmental noise retards auditory cortical development. Science 300:498).
    No cautious person who reads that paper would ever buy a “noise machine” to help their infant baby sleep so that mom and dad aren’t disturbed by their baby’s fussing.

    We have trained animals who appear to have “immature” brains in a number of studies, rapidly driving changes in them that recover neurological status to the young-adult peak-performance form. We have also trained animals who, because of impoverished or negative “childhoods”, grow to adulthood with brains that functionally and physically still look baby-like—then trained those animals in ways that very quickly (2-4 weeks) established normal-adult functional and physical status. Hundreds of human studies manifest the same capacity for neurologic rejuvenation (the recovery of operational abilities that earlier applied for the same cohort studied at a younger age) or ultra-rapid neurological maturation (the normalization of expected older-age abilities in a child or adult whose brain is “behind” because of an impoverished or otherwise-neurological-challenging childhood.) Many of these studies are cited in the comments following later chapters.

    In an exciting human study conducted in infants, April Benasich and colleagues (from Rutgers University) have shown that they can accelerate normal development of listening and language abilities in children by engaging infants with progressively more challenging non-verbal sound stimuli. Results are completely consistent with observations recorded earlier in animal models. Studies documenting these outcomes are in press; references shall be updated at the time of their publication.

  5. Note that neuroscientists have conducted many studies that describe the operation of different synaptic receptor subtypes, that describe the neurological bases of faster vs slower and stronger vs weaker post-synaptic effects, that “explain” changes in the morphology of dendrites or axons or soma, that account for differences in the metabolic activities or cell-maintenance activities or transmitter production in neurons, that differentiate modulatory neurotransmitter receptors and related post-synaptic structure or processes, etc., etc. Different performance characteristics or physical states for all of these processes recorded in infant vs prime-of-life-adult vs aged animals infer differences in a very large number of cellular and molecular processes. Collectively, the kinds of changes recorded across the course of brain “maturation” to advance to young adulthood must be expressed by changes in the genetic expression of many hundreds of aspects of brain chemistry; as must the decline in all of these same processes as we age; as must the reversal of all of those changes achievable via training in older-aged animals; as must the reversal of changes from the prime of life back in the direction of a functionally infantile brain achieved in vigorous young adults by re-introducing a high level of internal ‘chatter’ (processing noise) into the brain. A lot has to change, to account for the transition of a sluggish, imprecise, unreliable, poorly morphologically advanced baby brain to the fast, precise, reliable, highly structured and organized and elaborately morphologically refined brain of the juvenile or young adult—or vice versa. For an introduction into this science documenting the reversibility of brain plasticity, and to get a primitive start at that many-thousands list of further references, see, e.g., de Villers-Sidani E et al, 2010, Recovery of functional and structural age-related changes in the primary auditory cortex with operant training PNAS 107:13900; or Zhou X et al, 2012, Natural restoration of critical period plasticity in the juvenile and adult primary auditory cortex. J Neuroci 31:5625.
  6. We first documented the “reversibility” of brain plasticity processes in studies in monkeys in which, following Hebbian network rules, we could either degrade or refine cutaneous receptive fields and “mapped” representations of hand surfaces by changing schedules of skin engagement in training (see Recanzone et al, 1992, Changes in the distributed temporal response properties of SI cortical neurons reflect improvements in performance on a temporally-based tactile discrimination task. J Neurophysiol 67:1071). For example, if an adult monkey made a distinction about the temporal structures of inputs with stimulation always being applied to an invariant skin location, the receptive fields representing that skin surface grew in size by 10-to 100-fold, presumably because (by the Hebb rule; Hebbian network models showed) that this engaged point on the skin surface was the competitive “winner”; any thalamic neuron with a receptive field that overlapped that skin location that was fed to this cortical zone was plastically incorporated to enlarge that progressively growing receptive field. In the same monkey, the numbers of neurons representing this specific now-very-large receptive field (the “cortical column” representing this integrated resultant) grew by 200- to 300-fold (Recanzone et al, 1992, Topographic reorganization of the hand representational zone in cortical area 3b paralleling improvements in frequency discrimination performance. J Neurophysiol 67:1031). In an alternative experiment form cited in the same scientific reports, in other monkeys, we simply changed the specific skin location engaged in training on every training day. Now, because every location in this skin zone was a competitor for cortical real estate (again, by the Hebb rule played out via Hebbian network plasticity), the receptive field was reduced to about 1/3rd the size originally defined for neurons at this site—and cortical column size was also reduced to little more than half their pre-training diameters—which meant that only about 1/3rd the number of neurons now “represented” any given trained skin location. These studies did not drive these changes to their ultimate limits—but nonetheless, they showed that a several hundred-fold difference in the input selectivity of cortical neurons and about a thousand-fold difference in the cooperative population of neurons representing that “selected” input could be expressed in even this most anatomically constrained somatosensory cortical area, the true primary somatosensory cortex, cortical area 3b. While the potential for plasticity in this cortical zone is obviously enormous—It pales in comparison with that achievable at higher cortical areas, where input sources are much more broadly anatomically convergent and divergent (that is, where the repertoires of inputs that can be plastically strengthened—“co-selected”, as success in behavior commands—are far greater).We later directly demonstrated that temporal response characteristics could also be easily degraded or refined by manipulating early childhood experiences (e.g., Zhang LI et al, 2002, Disruption of primary auditory cortex by synchronous auditory inputs during a critical period. PNAS 99:2309; Nakahara et al, 2004, Specialization of primary auditory cortex processing by sound exposure in the “critical period”. PNAS 202:7070; Zhou X, Merzenich MM, 2008, Enduring effects of early structured noise exposure on temporal modulation in the primary auditory cortex. PNAS 105:4423) or by training older animals following the same Hebbian network-inspired models (e.g., Zhou X and Merzenich MM, 2007, Intensive training in adults refines A1 representations degraded in an early postnatal critical period. PNAS 104:15935).
  7. We more recently conducted several experiments designed to more broadly document the “reversibility” of plasticity processes in adults. Our strategy was: a) to document differences in about 20 different domains of brain anatomy or function in prime-of-life vs near-end-of-life rats; and b) to then train the rat to see which of the functional or physical changes that had been altered in aging could be thereby rejuvenated (see de Villers-Sidani E, Merzenich MM, 2011, Life-long plasticity in the rat auditory cortex: basic mechanisms and role of sensory experience. Prog Brian Res 191:119; and de Villers-Sidani E, et al, ibid.). Our basic findings (augmented from the reports above by studies that are now [May, 2013] in press): a) EVERY anatomical and functional thing we measured was degraded, deteriorated, dysfunctional, when we compared the old to the young adult brain; and b) EVERY anatomical and functional index was “corrected”—in most cases, to completely (or nearly completely) restore the brain to a physical/functional young-adult status—by training. Two different forms of training were required to achieve this remarkable “rejuvenation”. These findings are supported by about a dozen studies cited later (Chapter 28) that showed that individual aspects of degraded or limited cortical processing are “corrected” (reversed) by appropriately targeted training in adult animals.As we’ll relate later ( we have shown that many of these same 20 domains have been demonstrated to be equivalently bi-directional in humans (we believe that all of them are).
  8. In a follow-on study, we asked whether we could “age” the brain by simply maintaining an adult animal in a continuous high-noise environment. After 3 weeks of continuous noise exposure, the auditory rat brain, by every measure, looked like a near-end-of-life brain, i.e., it had “prematurely aged”, just by adding a heavy dose of continuous “chatter” into the machinery of the brain (see Zhou X et al, 2011, Natural restoration of critical period plasticity in the juvenile and adult primary auditory cortex. J Neurosci 31:5625.). Note that the old brain or this “prematurely aging” brain, in every physical and functional characteristic, closely resembled this system in the undeveloped state documented in the INFANT brain.That was hardly a surprise. Given the reversible nature of plasticity processes, the very young brain would be expected to roughly match the very old brain. As Rosencrantz said to Hamlet and Guildenstern (Hamlet, Act 2, Scene 2) “…an old man is twice a child.”The very important practical and medical implications of these findings are discussed in later chapters.
  9. We have trained several million children and adults in ways that advance their brains in the direction of their reaching a neurological ability level closer to “peak performance” landmarks normally achieved in young adulthood. Training a child results in their rapidly advance FORWARD (as if they were very rapidly “maturing”) towards that performance peak. Training an older adult results in their rapid rejuvenation BACKWARD (forward in performance abilities) to recover those greater powers that were formerly expressed at a younger age. Many references citing these improvements in the speed, accuracy, recording fidelity (memory), reliability and control of neurological operations can be found at (child studies) and (adult studies). Also see number of investigators have studied cognitive abilities as a function of chronological age. I shall summarize these and other studies in the notes following a later chapter more directly focusing on age-related performance loss. See