Asymmetric electrode geometry induced photovoltaic behavior for self-powered organic artificial synapses

Optoelectronic synapses have attracted considerable attention because of their promising potential in artificial visual perception systems for neuromorphic computing. Despite remarkable progress in mimicking synaptic functions, reduction of energy consumption of artificial synapses is still a substantial obstacle that is required to be overcome to promote advanced emerging applications. Herein, we propose a zero-power artificial optoelectrical synapses using ultrathin organic crystalline semiconductors, which can be self-driven by exploiting the photovoltaic effect induced by asymmetric electrode geometry contacts. The photogenerated charge carrier collection at the two electrodes is unbalanced due to the asymmetric contacts, leading to the in-plane current without bias voltage. Our devices successfully mimic a range of important synaptic functions, such as paired-pulse facilitation (PPF) and spike rate-dependent plasticity (SRDP). Furthermore, we demonstrate that our devices can realize the simulation of image sharpening under self-driven optical-sensing synaptic operations, offering prospects for the development of retinomorphic visual systems.

Effective and Efficient Neural Networks for Spike Inference from In Vivo Calcium Imaging

Calcium imaging technique provides irreplaceable advantages in monitoring large population of neuronal activities simultaneously. However, due to the generally low signal to noise ratio (SNR) of the calcium signal and variability in dye properties, it is still challenging to faithfully infer neuronal spikes from these calcium signals, especially from in vivo experiments. In this study, we tackled the problem of both spike-rate and spike-event predictions using a data-driven approach, based on a public pool of dataset with simultaneously recorded calcium and electrophysiological signals using different dyes and recorded from different brain regions. We proposed the ENS2 (effective and efficient neural networks for spike inference from calcium signals) system using raw calcium inputs and it consistently outperforms state-of-the-arts algorithms in both spike-rate and spike-event predictions with reduced computational load. We have also demonstrated that factors such as sampling rates, smoothing window sizes and parametric evaluation metrics could readily bias the interpretation of inference performance. We concluded that optimizing our system for spike-event prediction could produce a more versatile inference system for real neuroscience studies.

Visual modulation of spectrotemporal receptive fields in mouse auditory cortex

Recent studies have established significant anatomical and functional connections between visual areas and primary auditory cortex (A1), which may be important for perceptual processes such as communication and spatial perception. However, much remains unknown about the microcircuit structure of these interactions, including how visual context may affect different cell types across cortical layers, each with diverse responses to sound. The present study examined activity in putative excitatory and inhibitory neurons across cortical layers of A1 in awake male and female mice during auditory, visual, and audiovisual stimulation. We observed a subpopulation of A1 neurons responsive to visual stimuli alone, which were overwhelmingly found in the deep cortical layers and included both excitatory and inhibitory cells. Other neurons for which responses to sound were modulated by visual context were similarly excitatory or inhibitory but were less concentrated within the deepest cortical layers. Important distinctions in visual context sensitivity were observed among different spike rate and timing responses to sound. Spike rate responses were themselves heterogeneous, with stronger responses evoked by sound alone at stimulus onset, but greater sensitivity to visual context by sustained firing activity following transient onset responses. Minimal overlap was observed between units with visual-modulated firing rate responses and spectrotemporal receptive fields (STRFs) which are sensitive to both spike rate and timing changes. Together, our results suggest visual information in A1 is predominantly carried by deep layer inputs and influences sound encoding across cortical layers, and that these influences independently impact qualitatively distinct responses to sound.

Noise induced quiescence of epileptic spike generation in patients with epilepsy

Clinical scalp electroencephalographic recordings from patients with epilepsy are distinguished by the presence of epileptic discharges i.e. spikes or sharp waves. These often occur randomly on a background of fluctuating potentials. The spike rate varies between different brain states (sleep and awake) and patients. Epileptogenic tissue and regions near these often show increased spike rates in comparison to other cortical regions. Several studies have shown a relation between spike rate and background activity although the underlying reason for this is still poorly understood. Both these processes, spike occurrence and background activity show evidence of being at least partly stochastic processes. In this study we show that epileptic discharges seen on scalp electroencephalographic recordings and background activity are driven at least partly by a common biological noise. Furthermore, our results indicate noise induced quiescence of spike generation which, in analogy with computational models of spiking, indicate spikes to be generated by transitions between semi-stable states of the brain, similar to the generation of epileptic seizure activity. The deepened physiological understanding of spike generation in epilepsy that this study provides could be useful in the electrophysiological assessment of different therapies for epilepsy including the effect of different drugs or electrical stimulation.

Novel Therapeutic Approach for Excitatory/Inhibitory Imbalance in Neurodevelopmental and Neurodegenerative Diseases

Excitatory/inhibitory (E/I) balance, defined as the balance between excitation and inhibition of synaptic activity in a neuronal network, accounts in part for the normal functioning of the brain, controlling, for example, normal spike rate. In many pathological conditions, this fine balance is perturbed, leading to excessive or diminished excitation relative to inhibition, termed E/I imbalance, reflected in network dysfunction. E/I imbalance has emerged as a contributor to neurological disorders that occur particularly at the extremes of life, including autism spectrum disorder and Alzheimer's disease, pointing to the vulnerability of neuronal networks at these critical life stages. Hence, it is important to develop approaches to rebalance neural networks. In this review, we describe emerging therapies that can normalize the E/I ratio or the underlying abnormality that contributes to the imbalance in electrical activity, thus improving neurological function in these maladies.