Differences in reward processing between putative cell types in primate prefrontal cortex

Deep brain electrophysiology in freely moving sheep

Although rodents are arguably the easiest animals to use for studying brain function, relying on them as model species for translational research comes with its own set of limitations. Here, we propose sheep as a practical large animal species to use for in vivo brain function studies performed in naturalistic settings. We conducted proof-of-principle deep brain electrophysiological recording experiments using unrestrained sheep during behavioral testing. Recordings were made from cortex and hippocampus, both while sheep performed goal-directed behaviors (two-choice discrimination tasks) and across states of vigilance, including sleep. Hippocampal and cortical oscillatory rhythms were consistent with those seen in rodents and non-human primates, and included cortical alpha oscillations and hippocampal sharp wave ripple oscillations (∼150 Hz) during immobility and hippocampal theta oscillations (5-6 Hz) during locomotion. Recordings were conducted over a period of many months during which time the animals participated willingly in the experiments. Over 3,000 putative neurons were identified, including examples whose activity was modulated by task, speed of locomotion, spatial position, reward and vigilance states, and one whose firing rate was potentially modulated by the sight of the investigator. Together, these experiments demonstrate that sheep are excellent experimental animals to use for longitudinal studies requiring a large-brained mammal and/or large-scale recordings across distributed neuronal networks. Sheep could be used safely for studying not only neural encoding of decision-making and spatial-mapping in naturalistic environments outside the confines of the traditional laboratory but also the neural basis of both intra- and inter-species social interactions.

Neural mechanism of visual information degradation from retina to V1 area

The information processing mechanism of the visual nervous system is an unresolved scientific problem that has long puzzled neuroscientists. The amount of visual information is significantly degraded when it reaches the V1 after entering the retina; nevertheless, this does not affect our visual perception of the outside world. Currently, the mechanisms of visual information degradation from retina to V1 are still unclear. For this purpose, the current study used the experimental data summarized by Marcus E. Raichle to investigate the neural mechanisms underlying the degradation of the large amount of data from topological mapping from retina to V1, drawing on the photoreceptor model first. The obtained results showed that the image edge features of visual information were extracted by the convolution algorithm with respect to the function of synaptic plasticity when visual signals were hierarchically processed from low-level to high-level. The visual processing was characterized by the visual information degradation, and this compensatory mechanism embodied the principles of energy minimization and transmission efficiency maximization of brain activity, which matched the experimental data summarized by Marcus E. Raichle. Our results further the understanding of the information processing mechanism of the visual nervous system.

A cortical model with multi-layers to study visual attentional modulation of neurons at the synaptic level

Visual attention is a selective process of visual information and improves perceptual performance by modulating activities of neurons in the visual system. It has been reported that attention increased firing rates of neurons, reduced their response variability and improved reliability of coding relevant stimuli. Recent neurophysiological studies demonstrated that attention also enhanced the synaptic efficacy between neurons mediated through NMDA and AMPA receptors. Majority of computational models of attention usually are based on firing rates, which cannot explain attentional modulations observed at the synaptic level. To understand mechanisms of attentional modulations at the synaptic level, we proposed a neural network consisting of three layers, corresponding to three different brain regions. Each layer has excitatory and inhibitory neurons. Each neuron was modeled by the Hodgkin-Huxley model. The connections between neurons were through excitatory AMPA and NMDA receptors, as well as inhibitory GABAA receptors. Since the binding process of neurotransmitters with receptors is stochastic in the synapse, it is hypothesized that attention could reduce the variation of the stochastic binding process and increase the fraction of bound receptors in the model. We investigated how attention modulated neurons' responses at the synaptic level on the basis of this hypothesis. Simulated results demonstrated that attention increased firing rates of neurons and reduced their response variability. The attention-induced effects were stronger in higher regions compared to those in lower regions, and stronger for inhibitory neurons than for excitatory neurons. In addition, AMPA receptor antagonist (CNQX) impaired attention-induced modulations on neurons' responses, while NMDA receptor antagonist (APV) did not. These results suggest that attention may modulate neuronal activity at the synaptic level.