Acousto-optical Scanning-Based High-Speed 3D Two-Photon Imaging In Vivo.

Recording of the concerted activity of neuronal assemblies and the dendritic and axonal signal integration of downstream neurons pose different challenges, preferably a single recording system should perform both operations. We present a three-dimensional (3D), high-resolution, fast, acousto-optic two-photon microscope with random-access and continuous trajectory scanning modes reaching a cubic millimeter scan range (now over 950 × 950 × 3000 μm3) which can be adapted to imaging different spatial scales. The resolution of the system allows simultaneous functional measurements in many fine neuronal processes, even in dendritic spines within a central core (>290 × 290 × 200 μm3) of the total scanned volume. Furthermore, the PSF size remained sufficiently low (PSFx < 1.9 μm, PSFz < 7.9 μm) to target individual neuronal somata in the whole scanning volume for simultaneous measurement of activity from hundreds of cells. The system contains new design concepts: it allows the acoustic frequency chirps in the deflectors to be adjusted dynamically to compensate for astigmatism and optical errors; it physically separates the z-dimension focusing and lateral scanning functions to optimize the lateral AO scanning range; it involves a custom angular compensation unit to diminish off-axis angular dispersion introduced by the AO deflectors, and it uses a high-NA, wide-field objective and high-bandwidth custom AO deflectors with large apertures. We demonstrate the use of the microscope at different spatial scales by first showing 3D optical recordings of action potential back propagation and dendritic Ca2+ spike forward propagation in long dendritic segments in vitro, at near-microsecond temporal resolution. Second, using the same microscope we show volumetric random-access Ca2+ imaging of spontaneous and visual stimulation-evoked activity from hundreds of cortical neurons in the visual cortex in vivo. The selection of active neurons in a volume that respond to a given stimulus was aided by the real-time data analysis and the 3D interactive visualization accelerated selection of regions of interest

Distributed processing and temporal codes in neuronal networks

The cerebral cortex presents itself as a distributed dynamical system with the characteristics of a small world network. The neuronal correlates of cognitive and executive processes often appear to consist of the coordinated activity of large assemblies of widely distributed neurons. These features require mechanisms for the selective routing of signals across densely interconnected networks, the flexible and context dependent binding of neuronal groups into functionally coherent assemblies and the task and attention dependent integration of subsystems. In order to implement these mechanisms, it is proposed that neuronal responses should convey two orthogonal messages in parallel. They should indicate (1) the presence of the feature to which they are tuned and (2) with which other neurons (specific target cells or members of a coherent assembly) they are communicating. The first message is encoded in the discharge frequency of the neurons (rate code) and it is proposed that the second message is contained in the precise timing relationships between individual spikes of distributed neurons (temporal code). It is further proposed that these precise timing relations are established either by the timing of external events (stimulus locking) or by internal timing mechanisms. The latter are assumed to consist of an oscillatory modulation of neuronal responses in different frequency bands that cover a broad frequency range from <2 Hz (delta) to >40 Hz (gamma) and ripples. These oscillations limit the communication of cells to short temporal windows whereby the duration of these windows decreases with oscillation frequency. Thus, by varying the phase relationship between oscillating groups, networks of functionally cooperating neurons can be flexibly configurated within hard wired networks. Moreover, by synchronizing the spikes emitted by neuronal populations, the saliency of their responses can be enhanced due to the coincidence sensitivity of receiving neurons in very much the same way as can be achieved by increasing the discharge rate. Experimental evidence will be reviewed in support of the coexistence of rate and temporal codes. Evidence will also be provided that disturbances of temporal coding mechanisms are likely to be one of the pathophysiological mechanisms in schizophrenia

Order-Based Representation in Random Networks of Cortical Neurons

The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on a trial-to-trial basis. This is probably the most severe biophysical constraint on putative time-based primitives of stimulus representation in neuronal networks. Here we show that in spontaneously developing large-scale random networks of cortical neurons in vitro the order in which neurons are recruited following each stimulus is a naturally emerging representation primitive that is invariant to significant temporal changes in spike times. With a relatively small number of randomly sampled neurons, the information about stimulus position is fully retrievable from the recruitment order. The effective connectivity that makes order-based representation invariant to time warping is characterized by the existence of stations through which activity is required to pass in order to propagate further into the network. This study uncovers a simple invariant in a noisy biological network in vitro; its applicability under in vivo constraints remains to be seen

Cellular and Network Contributions to Excitability of Layer 5 Neocortical Pyramidal Neurons in the Rat

There is a considerable gap between investigating the dynamics of single neurons and the computational aspects of neural networks. A growing number of studies have attempted to overcome this gap using the excitation in brain slices elicited by various chemical manipulations of the bath solution. However, there has been no quantitative study on the effects of these manipulations on the cellular and network factors controlling excitability. Using the whole-cell configuration of the patch-clamp technique we recorded the membrane potential from the soma of layer 5 pyramidal neurons in acute brain slices from the somatosensory cortex of young rats at 22°C and 35°C. Using blockers of synaptic transmission, we show distinct changes in cellular properties following modification of the ionic composition of the artificial cerebrospinal fluid (ACSF). Thus both cellular and network changes may contribute to the observed effects of slice excitation solutions on the physiology of single neurons. Furthermore, our data suggest that the difference in the ionic composition of current standard ACSF from that of CSF measured in vivo cause ACSF to depress network activity in acute brain slices. This may affect outcomes of experiments investigating biophysical and physiological properties of neurons in such preparations. Our results strongly advocate the necessity of redesigning experiments routinely carried out in the quiescent acute brain slice preparation

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons

Parallel Computational Subunits in Dentate Granule Cells Generate Multiple Place Fields

A fundamental question in understanding neuronal computations is how dendritic events influence the output of the neuron. Different forms of integration of neighbouring and distributed synaptic inputs, isolated dendritic spikes and local regulation of synaptic efficacy suggest that individual dendritic branches may function as independent computational subunits. In the present paper, we study how these local computations influence the output of the neuron. Using a simple cascade model, we demonstrate that triggering somatic firing by a relatively small dendritic branch requires the amplification of local events by dendritic spiking and synaptic plasticity. The moderately branching dendritic tree of granule cells seems optimal for this computation since larger dendritic trees favor local plasticity by isolating dendritic compartments, while reliable detection of individual dendritic spikes in the soma requires a low branch number. Finally, we demonstrate that these parallel dendritic computations could contribute to the generation of multiple independent place fields of hippocampal granule cells