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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. SIGNIFICANCE STATEMENT Identifying generic connectivity motives in cortical circuitry encoding for specific functions is crucial for understanding the computations implemented in the cortex. Indirect evidence points to correlation-based biases in the connectivity pattern in V1 of higher mammals, whereby excitatory and inhibitory neurons preferentially synapse onto neurons respectively with correlated and anticorrelated receptive fields. A recent intracellular study questions this push-pull hypothesis, failing to find spatial anticorrelation patterns between excitation and inhibition across the receptive field. We present here a spiking model of V1 that integrates relevant anatomic and physiological constraints and shows that a more versatile motif of correlation-based connectivity with selectively tuned excitation and broadened inhibition is sufficient to account for the diversity of functional descriptions obtained for different classes of stimuli., (Copyright © 2021 the authors.)

Background: Modern computerized spike recording systems are increasingly powerful and sophisticated. However, this increases the importance of performing validation by recording signals from a system with a known input-output relationship., New Method: We present here a simple and robust analog circuit that uses a minimum number of commonly available components to simulate two independently spiking neurons. The two neurons generate asynchronous overlapping spikes. These can be independently set to spike at either a constant rate, or at a rate set by an external control voltage., Results: The circuit is simple enough to easily assemble by hand, however, standard files for ordering commercial printed circuit boards are also supplied. Several units were built by different people, using both hand-assembly and commercially manufactured printed circuit boards: all worked well. The circuit is robust with respect to supply voltages and component values., Comparison With Existing Methods: Existing analog circuits tend to be complex, hard to assemble, and use hard-to-find components. Digital simulators typically require specific development systems that have steep learning curves and are likely to change radically or become unavailable very quickly. This system has been optimized to be robust, simple, and use only commonly available components., Conclusions: When validating a system there could be an advantage to using a calibrator that is robust, whose input-output relationship is simple, and whose design is stable over time., (Copyright © 2013 Elsevier B.V. All rights reserved.)

The diversity of intrinsic dynamics observed in neurons may enhance the computations implemented in the circuit by enriching network-level emergent properties such as synchronization and phase locking. Large-scale spiking network models of entire brain regions offer a platform to test theories of neural computation and cognitive function, providing useful insights on information processing in the nervous system. However, a systematic in-depth investigation requires network simulations to capture the biological intrinsic diversity of individual neurons at a sufficient level of accuracy. The computationally efficient Izhikevich model can reproduce a wide range of neuronal behaviors qualitatively. Previous studies using optimization techniques, however, were less successful in quantitatively matching experimentally recorded voltage traces. In this article, we present an automated pipeline based on evolutionary algorithms to quantitatively reproduce features of various classes of neuronal spike patterns using the Izhikevich model. Employing experimental data from Hippocampome.org, a comprehensive knowledgebase of neuron types in the rodent hippocampus, we demonstrate that our approach reliably fit Izhikevich models to nine distinct classes of experimentally recorded spike patterns, including delayed spiking, spiking with adaptation, stuttering, and bursting. Importantly, by leveraging the parameter-exploration capabilities of evolutionary algorithms, and by representing qualitative spike pattern class definitions in the error landscape, our approach creates several suitable models for each neuron type, exhibiting appropriate feature variabilities among neurons. Moreover, we demonstrate the flexibility of our methodology by creating multi-compartment Izhikevich models for each neuron type in addition to single-point versions. Although the results presented here focus on hippocampal neuron types, the same strategy is broadly applicable to any neural systems. [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]