EPILOGUE

In this book, we explored the neural underpinning of the human capacity for innovation, with a specific focus on the neural mechanisms involved in imagination and abstraction. We began our journey with the finding that the hippocampus, a brain structure well known for its role in learning and memory, is also involved in imagination (part 1). In other words, memory and imagination are supported by the same brain structure, which may explain why our memories are malleable and why we sometimes form false memories of events that did not occur.

We then explored hippocampal neural circuit processes underlying imagination (part 2). The CA3 region of the hippocampus, whose neurons are connected to each other by massive recurrent projections and hence prone to self-excitation, generates both experienced and unexperienced (novel) activity sequences during sleep and idle states. CA3 neurons are interconnected by a huge number of individually weak synapses, making them ideal for generating variable, rather than fixed, activity sequences. The CA3 network appears to have gained a degree of randomness during evolution.

We went on to examine the simulation-selection model, which posits that the CA3-CA1 neural network simulates and reinforces neural representations for high-value events and actions in preparation for the future rather than simply recalling what has already happened. We also explored the hypothesis that the hippocampus's simulation-selection function has evolved in land-navigating mammals, but not in birds, because of the necessity to choose optimal trajectories between two arbitrary locations.

Studies indicate that similar hippocampal neural processes underpin memory and imagination in humans and other mammals. The simulation-selection model posits that all land-navigating mammals have similar hippocampal neural processes that support imagination. In other words, the ability to imagine appears to be shared by all mammals. What then makes humans so innovative? We explored the neural underpinning of high-level abstraction in humans with the premise that humans are particularly innovative because they can imagine freely using high-level abstract concepts (part 3). The prefrontal cortex and the precuneus were examined as potential brain regions critical for high-level abstraction in humans. We also considered the possibility that the depth (the number of layers) of a neural network might determine the level of abstraction.

How much do we know about the neural underpinning of human innovation? Have we identified the critical neural processes that underpin imagination and high-level abstraction? Unfortunately, not yet. We are only beginning to understand the neural underpinning of this great human mental faculty. Furthermore, what we covered in this book only scratches the surface of the subject. First, in terms of the neural basis of imagination, the hippocampus does not function independently but interacts with a large number of brain regions during imagination. In this book, we only looked at hippocampal neural processes. Second, the neural basis of high-level abstraction is less well understood compared to that of imagination. As a result, our discussions on this subject (part 3) are necessarily limited. Third, as we discussed in chapter 13, imagination alone is insufficient for creativity and innovation. An evaluative process that constrains ideas to meet a specific need should follow the generation of ideas. We concentrated on the former (spontaneous creativity) while ignoring the latter (deliberate creativity). Please be aware of these constraints.

Part 4 covered some topics related to but distinct from the book's main thesis (the brain foundation of imagination and high-level abstraction), such as language and how to become creative. In the final chapter, we contrasted two different perspectives on the consequences of innovation, namely the sustainability problem and intelligence explosion. It is noteworthy that we have made significant progress in artificial intelligence despite our limited understanding of the neural basis of human innovation. Artificial neural networks are superior to the human brain in terms of signal processing speed (GHz versus less than 100 Hz), network depth (more than a hundred layers versus less than twenty layers; see chapter 11), evolution speed (days, months, or years for a new version of an artificial neural network versus multiple generations for natural evolution), and technology spread (instantaneous duplication versus an inability to duplicate). These factors enabled artificial neural networks to outperform human intelligence in many domains. We may be able to build much more powerful artificial neural networks in the near future as we better understand the neural basis of human innovation and apply this knowledge to the construction of next-generation artificial neural networks. Then, as Kurzweil predicted, 'the singularity' may be near.

As elaborated in chapter 14, we may be on the verge of a tipping point that will lead to either a utopia or a dystopia. Our capacity for innovation is a double-edged sword. We need to make full use of it to resolve the crisis resulting from its misuse rather than facilitate it. We will most likely need to take a multifaceted approach. We need technological breakthroughs that can effectively mitigate the effects of climate change. At the same time, we need to understand ourselves better in order to foster global collaboration. We need to make the best use of all available social resources in order to address the ongoing environmental challenges.