Fluorescence imaging is an attractive method for monitoring neuronal activity. A key challenge for optically monitoring voltage is development of sensors that can give large and fast responses to changes in transmembrane potential. We now present fluorescent sensors that detect voltage changes in neurons by modulation of photo-induced electron transfer (PeT) from an electron donor through a synthetic molecular wire to a fluorophore. These dyes give bigger responses to voltage than electrochromic dyes, yet have much faster kinetics and much less added capacitance than existing sensors based on hydrophobic anions or voltagesensitive ion channels. These features enable single-trial detection of synaptic and action potentials in cultured hippocampal neurons and intact leech ganglia. Voltage-dependent PeT should be amenable to much further optimization, but the existing probes are already valuable indicators of neuronal activity. F luorescence imaging can map the electrical activity and communication of multiple spatially resolved neurons and thus complements traditional electrophysiological measurements (1, 2). Ca 2+ imaging is the most popular of such techniques, because the indicators are well-developed (3-6), highly sensitive (5, 6), and genetically encodable (7-13), enabling investigation of the spatial distribution of Ca 2+ dynamics in structures as small as dendritic spines and as large as functional circuits. However, because neurons translate depolarizations into Ca 2+ signals via a complex series of pumps, channels, and buffers, fluorescence imaging of Ca 2+ transients cannot provide a complete picture of electrical activity in neurons. Observed Ca 2+ spikes are temporally low-pass filtered from the initial depolarization and provide limited information regarding hyperpolarizations and subthreshold events. Direct measurement of transmembrane potential with fluorescent indicators would provide a more accurate account of the timing and location of neuronal activity. Despite the promise of fluorescent voltage-sensitive dyes (VSDs), previous classes of VSDs have each been hampered by some combination of insensitivity, slow kinetics (14-16), heavy capacitative loading (17-21), lack of genetic targetability, or phototoxicity. Two of the more widely used classes of VSDs, electrochromic and FRET dyes, illustrate the problems associated with developing fast and sensitive fluorescent VSDs.Electrochromic dyes respond to voltage through a direct interaction between the chromophore and the electric field (Scheme 1A). This Stark effect leads to small wavelength shifts in the absorption and emission spectrum. Because the electric field directly modulates the energy levels of the chromophore, the kinetics of voltage sensing occur on a timescale commensurate with absorption and emission, resulting in ultrafast (fs to ps) hypso-or bathochromic shifts many orders-of-magnitude faster than required to resolve fast spiking events and action potentials in neurons. This small wavelength shift dictates that the fluorescence signal ...

Previous studies of eye gaze have shown that when looking at images containing human faces, observers tend to rapidly focus on the facial regions. But is this true of other high-level image features as well? We here investigate the extent to which natural scenes containing faces, text elements, and cell phones-as a suitable control-attract attention by tracking the eye movements of subjects in two types of tasks-free viewing and search. We observed that subjects in free-viewing conditions look at faces and text 16.6 and 11.1 times more than similar regions normalized for size and position of the face and text. In terms of attracting gaze, text is almost as effective as faces. Furthermore, it is difficult to avoid looking at faces and text even when doing so imposes a cost. We also found that subjects took longer in making their initial saccade when they were told to avoid faces/text and their saccades landed on a non-face/non-text object. We refine a well-known bottom-up computer model of saliency-driven attention that includes conspicuity maps for color, orientation, and intensity by adding high-level semantic information (i.e., the location of faces or text) and demonstrate that this significantly improves the ability to predict eye fixations in natural images. Our enhanced model's predictions yield an area under the ROC curve over 84% for images that contain faces or text when compared against the actual fixation pattern of subjects. This suggests that the primate visual system allocates attention using such an enhanced saliency map.

VoltageFluor (VF) dyes have the potential to optically measure voltage in excitable membranes with the combination of high spatial and temporal resolution essential to better characterize the voltage dynamics of large groups of excitable cells. VF dyes sense voltage with high speed and sensitivity using photoinduced electron transfer (PeT) through a conjugated molecular wire. We show that tuning the driving force for PeT (ΔGPeT + w) through systematic chemical substitution modulates voltage sensitivity, estimate (ΔGPeT + w) values from experimentally measured redox potentials, and validate the voltage sensitivities in patch-clamped HEK cells for 10 new VF dyes. VF2.1(OMe).H, with a 48% ΔF/F per 100 mV, shows approximately 2-fold improvement over previous dyes in HEK cells, dissociated rat cortical neurons, and medicinal leech ganglia. Additionally, VF2.1(OMe).H faithfully reports pharmacological effects and circuit activity in mouse olfactory bulb slices, thus opening a wide range of previously inaccessible applications for voltage sensitive dyes.

SUMMARY A critical feature of neural networks is that they balance excitation and inhibition to prevent pathological dysfunction. How this is achieved is largely unknown, though deficits in the balance contribute to many neurological disorders. We show here that a microRNA (miR-101) is a key orchestrator of this essential feature, shaping the developing network to constrain excitation in the adult. Transient early blockade of miR-101 induces long-lasting hyper-excitability and persistent memory deficits. Using target-site blockers in vivo, we identify multiple developmental programs regulated in parallel by miR-101 to achieve balanced networks. Repression of one target, NKCC1, initiates the switch in GABA signaling, limits early spontaneous activity, and constrains dendritic growth. Kif1a and Ank2 are targeted to prevent excessive synapse formation. Simultaneous de-repression of these three targets completely phenocopies major dysfunctions produced by miR-101 blockade. Our results provide new mechanistic insight into brain development and suggest novel candidates for therapeutic intervention.

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To accommodate structured approaches of neural computation, we propose a class of recurrent neural networks for indexing and storing sequences of symbols or analog data vectors. These networks with randomized input weights and orthogonal recurrent weights implement coding principles previously described in vector symbolic architectures (VSA) and leverage properties of reservoir computing. In general, the storage in reservoir computing is lossy, and crosstalk noise limits the retrieval accuracy and information capacity. A novel theory to optimize memory performance in such networks is presented and compared with simulation experiments. The theory describes linear readout of analog data and readout with winner-take-all error correction of symbolic data as proposed in VSA models. We find that diverse VSA models from the literature have universal performance properties, which are superior to what previous analyses predicted. Further, we propose novel VSA models with the statistically optimal Wiener filter in the readout that exhibit much higher information capacity, in particular for storing analog data. The theory we present also applies to memory buffers, networks with gradual forgetting, which can operate on infinite data streams without memory overflow. Interestingly, we find that different forgetting mechanisms, such as attenuating recurrent weights or neural nonlinearities, produce very similar behavior if the forgetting time constants are matched. Such models exhibit extensive capacity when their forgetting time constant is optimized for given noise conditions and network size. These results enable the design of new types of VSA models for the online processing of data streams.

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Large-scale data collection efforts to map the brain are underway at multiple spatial and temporal scales, but all face fundamental problems posed by high-dimensional data and intersubject variability. Even seemingly simple problems, such as identifying a neuron/brain region across animals/subjects, become exponentially more difficult in high dimensions, such as recognizing dozens of neurons/brain regions simultaneously. We present a framework and tools for functional neurocartography-the large-scale mapping of neural activity during behavioral states. Using a voltage-sensitive dye (VSD), we imaged the multifunctional responses of hundreds of leech neurons during several behaviors to identify and functionally map homologous neurons. We extracted simple features from each of these behaviors and combined them with anatomical features to create a rich medium-dimensional feature space. This enabled us to use machine learning techniques and visualizations to characterize and account for intersubject variability, piece together a canonical atlas of neural activity, and identify two behavioral networks. We identified 39 neurons (18 pairs, 3 unpaired) as part of a canonical swim network and 17 neurons (8 pairs, 1 unpaired) involved in a partially overlapping preparatory network. All neurons in the preparatory network rapidly depolarized at the onsets of each behavior, suggesting that it is part of a dedicated rapid-response network. This network is likely mediated by the S cell, and we referenced VSD recordings to an activity atlas to identify multiple cells of interest simultaneously in real time for further experiments. We targeted and electrophysiologically verified several neurons in the swim network and further showed that the S cell is presynaptic to multiple neurons in the preparatory network. This study illustrates the basic framework to map neural activity in high dimensions with large-scale recordings and how to extract the rich information necessary to perform analyses in light of intersubject variability.