Vesicular transporters regulate the amount and type of neurotransmitter sequestered into synaptic vesicles and, hence, the kind of signal transmitted to postsynaptic neurons. Glutamate is the prominent excitatory neurotransmitter in retina; GABA and glycine are the main inhibitory neurotransmitters. Little is known about the ontogeny of vesicular neurotransmission in retina. We investigated expression of glutamatergic [vesicular glutamate transporter 1 (VGLUT1)] and GABA/glycinergic [vesicular GABA/glycine transporter (VGAT)] vesicular transporters in postnatal retina. VGLUT1 labels glutamatergic synapses. VGLUT1 and synaptic vesicle 2 colocalized to photoreceptor terminals. VGLUT1 colocalized with PKC to rod bipolar terminals and to ON bipolar terminals in metabotropic glutamate receptor 6+/- mice. Developmentally, VGAT expression precedes VGLUT1. In rat and mouse retina, VGAT occurred in the inner retina by postnatal day 1 (P1). In rat retina, VGLUT1 was in the outer retina by P5-P7 and the inner retina by P7. In the mouse retina, VGLUT1 expression was in the outer retina by P3 and the inner retina by P5. Both rat and mouse retina had an adult pattern of VGLUT1 expression by P14. VGLUT1 expression precedes ribbon synapses, which are first observed in the inner retina at P11 (Fisher, 1979) in mouse and P13 (Horsburgh and Sefton, 1987) in rat. The ribbon synapse marker RIBEYE was not detected in inner retina of P5 or P7 rat. Spontaneous EPSCs in mouse ganglion cells were recorded as early as P7. Together, these findings indicate that vesicular GABA and glycine transmission precedes vesicular glutamate transmission in developing rodent retina. Furthermore, vesicular glutamate transmission likely occurs before ribbon synapse formation in the inner retina.
hurst. Independent components of color natural scenes resemble V1 neurons in their spatial and color tuning. J Neurophysiol 91: 2859 -2873, 2004. First published January 28, 2004 10.1152/jn.00775.2003. It has been hypothesized that mammalian sensory systems are efficient because they reduce the redundancy of natural sensory input. If correct, this theory could unify our understanding of sensory coding; here, we test its predictions for color coding in the primate primary visual cortex (V1). We apply independent component analysis (ICA) to simulated cone responses to natural scenes, obtaining a set of colored independent component (IC) filters that form a redundancyreducing visual code. We compare IC filters with physiologically measured V1 neurons, and find great spatial similarity between IC filters and V1 simple cells. On cursory inspection, there is little chromatic similarity; however, we find that many apparent differences result from biases in the physiological measurements and ICA analysis. After correcting these biases, we find that the chromatic tuning of IC filters does indeed resemble the population of V1 neurons, supporting the redundancy-reduction hypothesis.
There is increasing interest in real-time brain-computer interfaces (BCIs) for the passive monitoring of human cognitive state, including cognitive workload. Too often, however, effective BCIs based on machine learning techniques may function as “black boxes” that are difficult to analyze or interpret. In an effort toward more interpretable BCIs, we studied a family of N-back working memory tasks using a machine learning model, Gaussian Process Regression (GPR), which was both powerful and amenable to analysis. Participants performed the N-back task with three stimulus variants, auditory-verbal, visual-spatial, and visual-numeric, each at three working memory loads. GPR models were trained and tested on EEG data from all three task variants combined, in an effort to identify a model that could be predictive of mental workload demand regardless of stimulus modality. To provide a comparison for GPR performance, a model was additionally trained using multiple linear regression (MLR). The GPR model was effective when trained on individual participant EEG data, resulting in an average standardized mean squared error (sMSE) between true and predicted N-back levels of 0.44. In comparison, the MLR model using the same data resulted in an average sMSE of 0.55. We additionally demonstrate how GPR can be used to identify which EEG features are relevant for prediction of cognitive workload in an individual participant. A fraction of EEG features accounted for the majority of the model’s predictive power; using only the top 25% of features performed nearly as well as using 100% of features. Subsets of features identified by linear models (ANOVA) were not as efficient as subsets identified by GPR. This raises the possibility of BCIs that require fewer model features while capturing all of the information needed to achieve high predictive accuracy.
Effective screening for mild traumatic brain injury (mTBI) is critical to accurate diagnosis, intervention, and improving outcomes. However, detecting mTBI using conventional clinical techniques is difficult, time intensive, and subject to observer bias. We examine the use of a simple visuomotor tracking task as a screening tool for mTBI. Thirty participants, 16 with clinically diagnosed mTBI (mean time since injury: 36.4 ± 20.9 days (95 % confidence interval); median = 20 days) were asked to squeeze a hand dynamometer and vary their grip force to match a visual, variable target force for 3 min. We found that controls outperformed individuals with mTBI; participants with mTBI moved with increased variability, as quantified by the standard deviation of the tracking error. We modeled participants' feedback response-how participants changed their grip force in response to errors in position and velocity-and used model parameters to classify mTBI with a sensitivity of 87 % and a specificity of 93 %, higher than several standard clinical scales. Our findings suggest that visuomotor tracking could be an effective supplement to conventional assessment tools to screen for mTBI and track mTBI symptoms during recovery.
For the FAA's Next Generation Air Transportation System (NextGen), the ability to reliably measure the effects of automation changes or new task demands on controller/pilot workload and performance is critical. Much of the EEG research has focused on identifying specific cognitive states associated with levels of workload during post processing. However, there are potential benefits for researchers to having EEG results as a continuous measure that can be observed during simulation experiments. This demonstration is a workload gauge that processes EEG data in real time as the Air Traffic Controller (ATC) participant is performing basic cognitive and operational ATC tasks. The visualizations demonstrated are also available during post processing to enable investigation of the continuous relationships between ATC situational variables, controller performance, and EEG.
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