No abstract

Sensory deprivation caused by peripheral injury can trigger functional cortical reorganization across the initially silenced cortical area. It is proposed that intracortical connectivity enables recovery of function within such a lesion projection zone (LPZ), thus substituting lost subcortical input. Here, we investigated retinal lesioninduced changes in the function of lateral connections in the primary visual cortex of the adult rat. Using voltage-sensitive dye recordings, we visualized in millisecond-time resolution spreading synaptic activity across the LPZ. Shortly after lesion, the majority of neurons within the LPZ were subthresholdly activated by delayed propagation of activity that originated from unaffected cortical regions. With longer recovery time, latencies within the LPZ gradually decreased, and activation reached suprathreshold levels. Targeted electrode recordings confirmed that receptive fields of intra-LPZ neurons were displaced to the retinal lesion border while displaying normal orientation and direction selectivity. These results corroborate the view that cortical horizontal connections have a central role in functional reorganization, as revealed here by progressive facilitation of synaptic activity and the traveling wave of excitation that propagates horizontally into the deprived cortical region.horizontal connections ͉ plasticity ͉ striate cortex

Atypical sensory processing is now thought to be a core feature of the autism spectrum. Influential theories have proposed that both increased and decreased neural response reliability within sensory systems could underlie altered sensory processing in autism. Here, we report evidence for abnormally increased reliability of visual-evoked responses in layer 2/3 neurons of adult primary visual cortex in the MECP2-duplication syndrome animal model of autism. Increased response reliability was due in part to decreased response amplitude, decreased fluctuations in endogenous activity, and decreased neuronal coupling to endogenous activity. Similarly to what was observed neuronally, the optokinetic reflex occurred more reliably at low contrasts in mutant mice compared to controls. Retinal responses did not explain our observations. These data suggest that the circuit mechanisms for convolution of sensoryevoked and endogenous signal and noise may be altered in this form of syndromic autism.

Neuronal circuits often display small-world network architecture characterized by neuronal cliques of dense local connectivity communicating with each other through a limited number of cells that participate in multiple cliques. The principles by which such cliques organize to encode information remain poorly understood. Similarly tuned pyramidal cells that preferentially target each other may form multicellular encoding units performing distinct computational tasks. The existence of such units can reflect upon both spontaneous and stimulus-driven population events. We applied two-photon calcium imaging to study spontaneous population bursts in layer 2/3 of area V1 in male C57BL/6 mice. To identify potential small-world cliques, we searched for pyramidal cells whose calcium events had a consistent temporal relationship with the events of local inhibitory interneurons. This was guided by the intuition that groups of neurons whose synchronous firing represents a temporally coherent computational unit should be inhibited together. Pyramidal members of these interneuron-centered clusters on average displayed stronger functional connectivity between each other than with nonmember pyramidal neurons. The structure of the clusters evolved during postnatal development: cluster size and overlap between clusters decreased with developmental maturation. Pyramidal neurons in a cluster showed higher than chance tuning function similarity between each other and with the linked interneuron. Thus, spontaneous population events in V1 are shaped by small-world subnetworks of pyramidal neurons that share functional properties and work as a coherent unit with a local interneuron. These interneuron-pyramidal cell partnerships may represent a fundamental neocortical unit of computation at the population level.

Extracting common patterns of neural circuit computations in autism spectrum and pinning them as a cause of specific core traits of autism is the first step towards identifying cell- and circuit-level targets for effective clinical intervention. Studies in human subjects with autism have identified functional links and common anatomical substrates between core restricted behavioral repertoire, cognitive rigidity, and over-stability of visual percepts during visual rivalry. To be able to study these processes with single-cell precision and exhaustive neuronal population coverage, we developed the visual bi-stable perception task for mice. Our task is based on plaid patterns consisting of two transparent gratings drifting at an angle of 120 degrees relative to each other. This results in spontaneous reversals of the perception between local component motion (motion of the plaid perceived as two separate moving grating components) and integrated global pattern motion (motion of the plaid perceived as a fused moving texture). This robust paradigm does not depend on the explicit report of the mouse, as the direction of the optokinetic nystagmus (OKN, rapid eye movements driven by either pattern or component motion) is used to infer the dominant percept. Using this paradigm, we found that the rate of perceptual reversals between global and local motion interpretations of the stimulus is reduced in MECP2 duplication mouse model of autism. Moreover, the stability of local motion percepts is greatly increased in MECP2 duplication mice at the expense of global motion percepts. Thus, our model reproduces a subclass of core features in human autism (reduced rate of visual rivalry and atypical perception of visual motion). This further offers a well-controlled approach for dissecting neuronal circuits underlying these core features if combined with mesoscale 2-photon imaging and optogenetic manipulation of neuronal circuits.

The field of neuroscience is experiencing rapid growth in the complexity and quantity of the recorded neural activity allowing us unprecedented access to its dynamics in different brain areas. The objective of this work is to discover directly from the experimental data rich and comprehensible models for brain function that will be concurrently robust to noise. Considering this task from the perspective of dimensionality reduction, we develop an innovative, robust to noise dictionary learning framework based on adversarial training methods for the identification of patterns of synchronous firing activity as well as within a time lag. We employ real-world binary datasets describing the spontaneous neuronal activity of laboratory mice over time, and we aim to their efficient low-dimensional representation. The results on the classification accuracy for the discrimination between the clean and the adversarial-noisy activation patterns obtained by an SVM classifier highlight the efficacy of the proposed scheme compared to other methods, and the visualization of the dictionary's distribution demonstrates the multifarious information that we obtain from it.

Abstract-Modeling the activity of an ensemble of neurons can provide critical insights into the workings of the brain. In this work we examine if learning based signal modeling can contribute to a high quality modeling of neuronal signal data. To that end, we employ the sparse coding and dictionary learning schemes for capturing the behavior of neuronal responses into a small number of representative prototypical signals. Performance is measured by the reconstruction quality of clean and noisy test signals, which serves as an indicator of the generalization and discrimination capabilities of the learned dictionaries. To validate the merits of the proposed approach, a novel dataset of the actual recordings from 183 neurons from the primary visual cortex of a mouse in early postnatal development was developed and investigated. The results demonstrate that high quality modeling of testing data can be achieved from a small number of training examples and that the learned dictionaries exhibit significant specificity when introducing noise.

SUMMARYSensory stimuli are encoded by the joint firing of neuronal groups composed of pyramidal cells and interneurons,