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The spatial organization and dynamic interactions between excitatory and inhibitory synaptic inputs that define the receptive field (RF) of simple cells in the cat primary visual cortex (V1) still raise the following paradoxical issues: (1) stimulation of simple cells in V1 with drifting gratings supports a wiring schema of spatially segregated sets of excitatory and inhibitory inputs activated in an opponent way by stimulus contrast polarity and (2) in contrast, intracellular studies using flashed bars suggest that although ON and OFF excitatory inputs are indeed segregated, inhibitory inputs span the entire RF regardless of input contrast polarity. Here, we propose a biologically detailed computational model of simple cells embedded in a V1-like network that resolves this seeming contradiction. We varied parametrically the RF-correlation-based bias for excitatory and inhibitory synapses and found that a moderate bias of excitatory neurons to synapse onto other neurons with correlated receptive fields and a weaker bias of inhibitory neurons to synapse onto other neurons with anticorrelated receptive fields can explain the conductance input, the postsynaptic membrane potential, and the spike train dynamics under both stimulation paradigms. This computational study shows that the same structural model can reproduce the functional diversity of visual processing observed during different visual contexts. [ABSTRACT FROM AUTHOR]

In this research, we develop a neuromorphic system to study neural signaling at the level of first order tactile afferents which are slowly adapting type I (SA1) and rapidly adapting type I (RA1) mechanoreceptors. Considering, the linearized Izhikevich model, two digital circuits are developed for both afferents and are executed on the field programmable gate array (FPGA). After implementation of the digital circuits, we investigate how much information is encoded by this hardware-based neuromorphic system. Indeed, the artificial spiking sequences are evoked by applying different force profiles to the sensor connected to the FPGA. Next, the obtained neural responses are classified based on the two fundamental neural coding for brain information processing: spike timing and rate coding. Considering temporal coding, k-nearest neighbors (kNN), support vector machine (SVM) and Decision Tree algorithms are used for forces recognition using acquired artificial spike patterns. The results of classification show that the digital RA1 is susceptible to signal variations, while the digital SA1, on the other hand, is sensitive to the ramp and hold inputs. Furthermore, these responses are better distinguishable to different stimuli when both artificial SA1 and RA1 afferents are regarded. These results, which are functionally compatible with biological observations, yield the promise for fabrication and development of new tactile sensing modules to be employed in bio-robotic and prosthetic applications. [ABSTRACT FROM AUTHOR]

The center-surround organization of the receptive fields (RFs) of retinal ganglion cells highlights the presence of local contrast in visual stimuli. As RF of thalamic relay cells follow the same basic functional organization, it is often assumed that they contribute very little to alter the retinal output. However, in many species, thalamic relay cells largely outnumber their retinal inputs, which diverge to contact simultaneously several units at thalamic level. This gain in cell population as well as retinothalamic convergence opens the door to question how information about contrast is transformed at the thalamic stage. Here, we address this question using a realistic dynamic model of the retinothalamic circuit. Our results show that different components of the thalamic RF might implement filters that are analogous to two types of well-known image processing techniques to preserve the quality of a higher resolution version of the image on its way to the primary visual cortex. [ABSTRACT FROM AUTHOR]

Abstract: Using a biologically realistic model of a single neuron can be very beneficial for visual physiologists to test their electrophysiology setups, train students in the laboratory, or conduct classroom-teaching demonstrations. Here we present a Field Programmable Gate Array (FPGA)-based spiking model of visual cortex neurons, which has the ability to simulate three independent neurons and output analog spike waveform signals in four channels. To realistically simulate multi-electrode (tetrode) recordings, the independently generated spikes of each simulated neuron has a distinct waveform, and each channel outputs a differentially weighted sum of these waveforms. The model can be easily constructed from a small number of inexpensive commercially available parts, and is straightforward to operate. In response to sinewave grating stimuli, the neurons exhibit biologically realistic simple-cell-like response properties, including highly modulated Poisson spike trains, orientation selectivity, spatial/temporal frequency selectivity, and space-time receptive fields. Users can customize their model neurons by downloading modifications to the FPGA with varying parameter values, particularly desired features, or qualitatively different models of their own design. The source code and documentation are provided to enable users to modify or extend the model''s functionality according to their individual needs. [Copyright &y& Elsevier]

Highlights: [•] A simple and robust real-time analog spike simulator is presented. [•] Files for ordering commercial grade circuit boards are included. [•] The system generates two asynchronous overlapping spikes. [•] Spikes can fire at a set rate or under external voltage control. [ABSTRACT FROM AUTHOR]

The mnemonic functions of hippocampal sharp wave ripples (SPW-Rs) have been studied extensively. Because hippocampal outputs affect not only cortical but also subcortical targets, we examined the impact of SPW-Rs on the firing patterns of lateral septal (LS) neurons in behaving rats. A large fraction of SPW-Rs were temporally locked to high-frequency oscillations (HFOs) (120–180 Hz) in LS, with strongest coupling during non-rapid eye movement (NREM) sleep, followed by waking immobility. However, coherence and spike-local field potential (LFP) coupling between the two structures were low, suggesting that HFOs are generated locally within the LS GABAergic population. This hypothesis was supported by optogenetic induction of HFOs in LS. Spiking of LS neurons was largely independent of the sequential order of spiking in SPW-Rs but instead correlated with the magnitude of excitatory synchrony of the hippocampal output. Thus, LS is strongly activated by SPW-Rs and may convey hippocampal population events to its hypothalamic and brainstem targets. • Lateral septal neurons can synchronize to produce local high-frequency oscillations • Lateral septal high-frequency oscillations are coupled to hippocampal ripples • The magnitude, but not content, of hippocampal ripples are read out by lateral septum Tingley and Buzsáki describe that hippocampal sharp-wave-ripple-related bursts are routed to subcortical structures through the lateral septum. [ABSTRACT FROM AUTHOR]