The Brain Activity Map Project and the Challenge of Functional Connectomics , USA

The function of neural circuits is an emergent property that arises from the coordinated activity of large numbers of neurons. To capture this, we propose launching a large-scale, international public effort, the Brain Activity Map Project, aimed at reconstructing the full record of neural activity across complete neural circuits.This technological challenge could prove to be an invaluable step toward understanding fundamental and pathological brain processes.‘‘The behavior of large and complex aggregates of elementary

particles, it turns out, is not to be understood in terms of a simple extrapolation of the properties of a few particles. Instead, at each level of complexity entirely new properties appear.

’’ –More Is Different, P.W. Anderson ‘‘New directions in science are launched by new tools much more often than by new concepts. The effect of a concept-driven revolution  is to explain old things in new ways. The effect of a tool-driven revolution is to discover new things that have to be explained.’’ –Imagined Worlds, Freeman Dyson Emergent Properties of Brain Circuits Understanding how the brain works isarguably one of the greatest scientific challenges of our time. Although there have been piecemeal efforts to explain how different brain regions operate, no  general theory of brain function is universally  accepted. A fundamental underlying limitation is our ignorance of the brain’s microcircuitry, the synaptic connections contained within any given brain area, which Cajal referred to as ‘‘impenetrable jungles where many investigators have lost themselves’’ (Ramo´n y Cajal, 1923). To explore these jungles, neuroscientists have traditionally relied on electrodes that sample brain activity only very sparsely—from one to a few neurons within a given region. However, neural circuits can involve millions of neurons, so it is probable that neuronal ensembles operate at a multineuronal level of organization,  one that will be invisible from single neuron recordings, just as it would be pointless to view an HDTV program  looking just at one or a few pixels on a screen. Neural circuit function is therefore likely to beemergent—that is, it could arise from complex interactions among constituents. This hypothesis is supported by the  welldocumented recurrent and distributed architecture of connections in the CNS. Indeed, individual neurons generally form synaptic contacts with thousands of other neurons. In distributed circuits, the larger the connectivity matrix, the greater the redundancy within the network and the less important each neuron is. Despite these anatomical facts, neurophysiological  studies have gravitated toward detailed descriptions of the stable feature selectivity of individual neurons, a natural consequence of single-electrode recordings.  However, given their distributed connections and their plasticity, neurons are likely to be subject to continuous, dynamic rearrangements, participating at different times in different active ensembles.  Because of this, measuring emergent functional  states, such as dynamical attractors, could be more useful for characterizing  the functional properties of a circuit than recording receptive field responses from individual cells. Indeed, in some instances where large-scale population monitoring of neuronal ensembles has been possible, emergent circuit states have not been predictable from responses from individual cells. Emergent-level problems are not unique to neuroscience. Breakthroughs in understanding complex systems in other fields have come from shifting the focus to the emergent level. Examples include statistical mechanics, nonequilibrium  thermodynamics, and many-body and quantum physics. Emergent-level analysis has led to rich branches of science describing novel states of matter involving correlated particles, such as magnetism, superconductivity, superfluidity,  quantum Hall effects, and macroscopic quantum  coherence. In biological sciences, the sequencing of genomes and the ability to simultaneously measure genome-wide expression patterns have enabled emergent models of gene regulation,  developmental control, and disease states with enhanced predictive accuracy. We believe similar emergent-level richness  is in store for circuit neuroscience. An emergent level of analysis appears to us crucial for understanding brain circuits.