Charney draws a plausible picture about how genome structure, function, and heritability can be modified in a complex manner by both inner (body) and outer environments, as well as by the environment-modulated epigenome. The concepts raised in his article challenge the current knowledge about brain function, environment and self-representation, and human behavior. In addition, the dynamics of the neogenome, the active genetic material employed by the organism resulting from the interaction of the genome and epigenome, brings difficulties to intuitive understanding of cognitive processes. Thus, it becomes important for neuroscientists to develop new theoretical and computational methods to extract useful information about the influence of neogenomic regulatory networks on evolutionary and developmental processes.

The understanding of heritability studies was one of those completely changed under the neogenomics perspective, and it has direct impact on the neurosciences. For example, it is now necessary to investigate the real chance that acquired modifications at F0 male/female neogenome have to be transferred to F1 neogenome (i.e., the chance that F1 has to be a heritor of a neogenomic potential to develop F0 male/female acquired phenotypes). It is well known that acquired modifications in the somatic F0 male/female neogenome are related to F0 male/female acquired phenotypes, and that both acquired modifications cannot be transferred to F1. In contrast, acquired modifications in the neogenome of F0 male/female germ cells can be transferred to F1. What if the acquired modifications in the neogenome of F0 male/female germ cells were identical to that occurred in the neogenome of F0 male/female somatic cells related to acquired phenotypes? Could such acquired modifications in the neogenome of F0 male/female germ cells transfer to F1 a neogenomic potential to F1 to develop phenotypes acquired by F0 male/female? Theoretically, the transference of acquired phenotypes from F0 to F1 through the transference of modified neogenome of F0 male/female germ cells to F1 would be highly constrained. For example, it would require the absence of a direct environmental exposure beyond F0 male/female and transmission of a given phenotype through germ cells to at least the F3 generation (Charney's target article, note 7). In addition, the development of F0 acquired phenotypes by F1 would be highly constrained by other diverse neogenomic and environmental factors. For example, the acquired modifications in the neogenome of F0 male/female germ cells transferred to F1 should be similar in amount, extent, and specificity to the acquired modifications in the neogenome of F0 male/female somatic cells linked to the acquisition of new phenotypes. Further, instead of solely assured by the heritability of a neogenome with acquired modifications, the development of F0 male/female acquired phenotypes by F1 also should be constrained by stochastic environment-induced neogenomic events related to the regulatory dynamics of genes transcription and translation, as well as protein structure processing in F1 cells. However, despite the number and nature of constraints involved in this issue, investigation of the real chance that acquired phenotypes have to be inherited is needed.

Another field that will undergo important changes due to the introduction of neogenomics concepts is the investigation of the spatiotemporal dynamics of neuronal microcircuitry involved in the neuromodulation of cortical activity states. The neogenomic events described by Charney often are related to synaptic plasticity and neurogenesis (Allen Reference Allen2008; Day & Sweatt Reference Day and Sweatt2011). They are events assumed to be involved in perceptual learning and attentional modulation (Day & Sweatt Reference Day and Sweatt2011; Feng et al. Reference Feng, Fouse and Fan2007). In the temporal domain, visual attention shifts can occur at a millisecond scale, while in the spatial domain the scale corresponds to a displacement of 10° (Koenig-Robert & VanRullen Reference Koenig-Robert and VanRullen2011). Perceptual learning may early and rapidly improve performance (Hawkey et al. Reference Hawkey, Amitay and Moore2004) and is thought to be driven by external inputs controlling the ongoing cortical plasticity (Sagi Reference Sagi2010). These two mechanisms are also related to competition between cortical populations for specific features of sensory stimuli (Ashwin et al. Reference Ashwin, Karabacak and Nowotny2011; Lewkowicz Reference Lewkowicz2000). Neuromodulatory subcortical neurons implicated in the modulation of attentional shifts and experience-dependent cortical plasticity (Goard & Dan Reference Goard and Dan2009) also play a role during neuronal competitions, contributing significantly to the decorrelation between cortical populations responses to a given stimuli. Are such decorrelations between the responses of cortical populations a result of their neogenomic regulatory systems decorrelation? Are the neogenomic regulatory systems of subcortical neuromodulatory neurons controlling the neogenomic regulatory systems of cortical populations? Are the neogenomic regulatory systems capable of implement an action in the same spatiotemporal limits of attentional shifts and perceptual learning? New theoretical and computational formal methods are demanded to answer questions currently raised under the neogenomic perspective.

Finally, a small set of canonical simple computations have been identified as the mathematical basis of neuronal population behavior, and it is interesting to consider how analogous these operations are to our original view of how the genome worked. Exponentiation, linear filtering, and divisive normalization are the operations most commonly described (Baylor & Fuortes Reference Baylor and Fuortes1970; Grossberg Reference Grossberg1988; Heeger Reference Heeger1992; Lo & Wang Reference Lo and Wang2006; Naka & Rushton Reference Naka and Rushton1966; Priebe & Ferster Reference Priebe and Ferster2008; Stanford et al. Reference Stanford, Shankar, Massoglia, Costello and Salinas2010; Wang Reference Wang2002). In addition, several more complex theoretical and computational formalisms also have been employed to understand how genetic events are implicated in the control of intracellular and intercellular processes: direct and indirect graphs, Bayesian networks, Boolean networks, stochastic equations, rule-based formalisms, winner takes all and winnerless competition models, and so forth (for a review, see Jong Reference Jong2002). However, under the recent progress of neogenomics, algorithms based on the original genome paradigm will have to be reviewed or changed towards algorithms based on the neogenome dynamics. In this way, epigenetic robotics is a relatively new field of science that aims to understand the brain by constructing embodied systems and to build intelligent systems by learning from brain studies (Zlatev & Balkenius Reference Zlatev and Balkenius2001). Considering the perspective of neogenomic dynamics, epigenetic robotic operations should be guided by software (genome) modifiable by both inherent reprogramming tasks (transposable elements) and developmental experiences (epigenome), which in turn should be driven by interactions with robot inner/outer environments, all contributing to the emergence of robot self-programming (Prince et al. Reference Prince, Helder, Hollich, Berthouze, Kaplan, Kozima, Yano, Konczak, Metta, Nadel, Sandini, Stojanov and Balkenius2005; Markman et al. Reference Markman, Quittner, Eisenberg, Tobey, Thal, Niparko and Wang2011). Therefore, through this new modeling perspective, epigenetic robotics should provide useful explanatory neogenomic brain components and contribute to the validation and further development of machine learning foundations.