Reverse engineering the cognitive brain.

One of the greatest aspirations of the human mind has been to realize machines that surpass its cognitive intelligence. The rapid expansion in computing power, about to exceed the equivalent of the human brain, has yet to produce such a machine. The article by Neftci et al. in PNAS (1) offers a refreshing and humbling reminder that the brain’s cognition does not arise from exacting digital precision in high-performance computing, but rather emerges from an extremely efficient and resilient collective form of computation extending over very large ensembles of sluggish, imprecise, and unreliable analog components. This observation, first made by John von Neumann in his final opus (2), continues to challenge scientists and engineers several decades later in figuring and reproducing the mechanisms underlying brain-like forms of cognitive computing. Related developments are currently unfolding in collaborative initiatives engaging scientists and engineers, on a grander scale, in advancing neuroscience toward understanding the brain. In parallel with the Human Brain Project in Europe, the Brain Research through Advancing Innovative Neurotechnologies Initiative promises groundbreaking advances in enabling tools for revolutionizing neuroscience by developing nanotechnology to probe brain function at greatly increased spatial and temporal detail (3). Engineers are poised to contribute even further in revolutionizing such developments in neuroscience. In this regard it is helpful to relate the inquisitive nature of science—analysis—to the constructive power of engineering, synthesis. Despite fantastic feats of neuroscience in the analysis of the inner workings of neural and synaptic machinery down to the molecular scale, extending the level of understanding to something as complex as the human brain, not to mention its cognitive function, requires the power of synthesis in bridging across scales of analysis. Synthesis of complex function through hierarchical modular assemblies of successively more abstract representations is the forte of systems engineering, and provides a foundation for systems neuroscience in the multiscale investigation of the central nervous system (4). In his 1990 manifesto that launched the field of neuromorphic systems engineering (5), Carver Mead makes a compelling case for such analysis by synthesis in reverse engineering neural circuits in silicon, drawing isomorphic parallels between modules representing various levels of neural computation in the brain and their emulation in silicon electronics, down to the fundamental physical level of Boltzmann statistics in ionic transport across lipid membranes, and electronic transport across similar energy barriers in metal-oxidesemiconductor transistors in the subthreshold regime (6) (Fig. 1A). In addition to supporting advances in systems neuroscience, experiments in neural analysis by synthesis using silicon offer tremendous side benefits to the engineering of extremely low-power miniaturized devices. By emulating functional structure of their biological counterparts and approaching their energy efficiency in sensory processing and computing, these neuromorphic devices can operate more effectively and more naturally in their surroundings (7). Some examples of recent feats of neuromorphic systems engineering—just to name a few—include silicon retinae and cochleae seeing and hearing the world through our senses (8), silicon cortical models running at speeds greater than real time (9), and synapse arrays running cool at nominal energy efficiency on par with that of synaptic transmission in the human brain (10). However, demonstration of machine intelligence at the level of human cognition has remained elusive to date. A deeper look into the complexity of cognition helps to shed some light on the apparent challenges (Fig. 1B). Cognition is considered here, for the sake of the argument, as decision making by a motivated agent acting in the context of a given environment, such as a chess player making moves on the board. The cognitive task complexity, accounting for all possible states of the environment reachable by the agent, suffers from exponential scaling in the depth onto breadth of decision making, and quickly grows to astronomical proportions for any but relatively simple tasks. Efficient tree-search algorithms, capable of exhausting the search space using variants on Bellman’s principle of optimality in dynamic programming (11), are capable of tackling relatively complex problems, such as games like chess, but at a significant cost by expending computing resources that scale almost linearly with task complexity. Such linear scaling is a fundamental limitation when having a need to sample large portions of the state space, a consequence of exact symbolic reasoning in search. In contrast, our brains execute such essentially sequential logic operations with substantial difficulty because of the need to dynamically instantiate a heap of nested working memory (2, 12). As such, it is not surprising that A