Spatiotemporal Summation Research Articles

Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields

When multiple visual stimuli are presented simultaneously in the receptive field, the neural response is suppressed compared to presenting the same stimuli sequentially. The prevailing hypothesis suggests that this suppression is due to competition among multiple stimuli for limited resources within receptive fields, governed by task demands. However, it is unknown how stimulus-driven computations may give rise to simultaneous suppression. Using fMRI, we find simultaneous suppression in single voxels, which varies with both stimulus size and timing, and progressively increases up the visual hierarchy. Using population receptive field (pRF) models, we find that compressive spatiotemporal summation rather than compressive spatial summation predicts simultaneous suppression, and that increased simultaneous suppression is linked to larger pRF sizes and stronger compressive nonlinearities. These results necessitate a rethinking of simultaneous suppression as the outcome of stimulus-driven compressive spatiotemporal computations within pRFs, and open new opportunities to study visual processing capacity across space and time.

Reconfiguration of the visual code and retinal cell type complement in closely related diurnal and nocturnal mice.

How does evolution act on neuronal populations to match computational characteristics to functional demands? We address this problem by comparing visual code and retinal cell composition in closely related murid species with different behaviours. Rhabdomys pumilio are diurnal and have substantially thicker inner retina and larger visual thalamus than nocturnal Mus musculus. High-density electrophysiological recordings of visual response features in the dorsal lateral geniculate nucleus (dLGN) reveals that Rhabdomys attains higher spatiotemporal acuity both by denser coverage of the visual scene and a selective expansion of elements of the code characterised by non-linear spatiotemporal summation. Comparative analysis of single cell transcriptomic cell atlases reveals that realignment of the visual code is associated with increased relative abundance of bipolar and ganglion cell types supporting OFF and ON-OFF responses. These findings demonstrate how changes in retinal cell complement can reconfigure the coding of visual information to match changes in visual needs.

Investigating the Spatiotemporal Summation of Perimetric Stimuli in Dry Age-Related Macular Degeneration.

To measure achromatic spatial, temporal, and spatiotemporal summation in dry age-related macular degeneration (AMD) compared to healthy controls under conditions of photopic gaze-contingent perimetry. Twenty participants with dry AMD (mean age, 74.6 years) and 20 healthy controls (mean age, 67.8 years) performed custom, gaze-contingent perimetry tests. An area-modulation test generated localized estimates of Ricco's area (RA) at 2.5° and 5° eccentricities along the 0°, 90°, 180°, and 270° meridians. Contrast thresholds were measured at the same test locations for stimuli of six durations (3.7-190.4 ms) with a Goldmann III stimulus (GIII, 0.43°) and RA-scaled stimuli. The upper limit (critical duration) of complete temporal summation (using the GIII stimulus) and spatiotemporal summation (using the RA stimuli) was estimated using iterative two-phase regression analysis. Median (interquartile range [IQR]) RA estimates were significantly larger in AMD participants (2.5°: 0.21 [0.09-0.41] deg2; 5°: 0.32 [0.15-0.65 deg2]) compared to healthy controls (2.5°: 0.08 [0.05-0.13] deg2; 5°: 0.15 [0.08-0.22] deg2) at all test locations (all P < 0.05). No significant difference in median critical duration was found in AMD participants with the GIII stimulus (19.6 [9.9-30.4] ms) and RA-scaled stimuli (22.9 [13.9-40.3] ms) compared to healthy controls (GIII: 17.0 [11.3-24.0] ms; RA-scaled: 22.4 [14.3-33.1] ms) at all test locations (all P > 0.05). Spatial summation is altered in dry AMD, without commensurate changes in temporal summation. The sensitivity of perimetry to AMD may be improved by utilizing stimuli that probe alterations in spatial summation in the disease.

Novel therapeutic for multiple sclerosis protects white matter function in EAE mouse model.

Multiple sclerosis (MS) is a chronic demyelinating disease with prominent axon dysfunction. Our previous studies in an MS mouse model, experimental autoimmune encephalomyelitis (EAE), demonstrated that major histocompatibility complex Class II constructs can reverse clinical signs of EAE. These constructs block binding and downstream signaling of macrophage migration inhibitory factors (MIF-1/2) through CD74, thereby inhibiting phosphorylation of extracellular signal-regulated kinase (ERK) activation and tissue inflammation and promoting remyelination. To directly assess the effects of a novel third generation construct, DRhQ, on axon integrity in EAE, we compared axon conduction properties using electrophysiology on corpus callosum slices and optic nerves. By using two distinct white matter (WM) tracts, we aimed to assess the impact of the EAE and the benefit of DRhQ on myelinated and unmyelinated axons as well as to test the clinical value of DRhQ on demyelinating lesions in CC and optic myelitis. Our study found that EAE altered axon excitability, delayed axon conduction and slowed spatiotemporal summation correlated with diffuse astrocyte and microglia activation. Because MS predisposes patients to stroke, we also investigated and showed that vulnerability to WM ischemia is increased in the EAE MS mouse model. Treatment with DRhQ after the onset of EAE drastically inhibited microglial and astrocyte activation, improved functional integrity of the myelinated axons and enhanced recovery after ischemia. These results demonstrate that DRhQ administered after the onset of EAE promotes WM integrity and function, and reduces subsequent vulnerability to ischemic injury, suggesting important therapeutic potential for treatment of progressive MS.

Spatiotemporal summation of perimetric stimuli in healthy observers.

Spatial summation of perimetric stimuli has been used to derive conclusions about the spatial extent of retinal-cortical convergence, mostly from the size of the critical area of summation (Ricco's area, RA) and critical number of retinal ganglion cells (RGCs). However, spatial summation is known to change dynamically with stimulus duration. Conversely, temporal summation and critical duration also vary with stimulus size. Such an important and often neglected spatiotemporal interaction has important implications for modeling perimetric sensitivity in healthy observers and for formulating hypotheses for changes measured in disease. In this work, we performed experiments on visually heathy observers confirming the interaction of stimulus size and duration in determining summation responses in photopic conditions. We then propose a simplified computational model that captures these aspects of perimetric sensitivity by modelling the total retinal input, the combined effect of stimulus size, duration, and retinal cones-to-RGC ratio. We additionally show that, in the macula, the enlargement of RA with eccentricity might not correspond to a constant critical number of RGCs, as often reported, but to a constant critical total retinal input. We finally compare our results with previous literature and show possible implications for modeling disease, especially glaucoma.

Two mechanisms for direction selectivity in a model of the primate starburst amacrine cell.

In a recent study, visual signals were recorded for the first time in starburst amacrine cells of the macaque retina, and, as for mouse and rabbit, a directional bias observed in calcium signals was recorded from near the dendritic tips. Stimulus motion from the soma toward the tip generated a larger calcium signal than motion from the tip toward the soma. Two mechanisms affecting the spatiotemporal summation of excitatory postsynaptic currents have been proposed to contribute to directional signaling at the dendritic tips of starbursts: (1) a "morphological" mechanism in which electrotonic propagation of excitatory synaptic currents along a dendrite sums bipolar cell inputs at the dendritic tip preferentially for stimulus motion in the centrifugal direction; (2) a "space-time" mechanism that relies on differences in the time-courses of proximal and distal bipolar cell inputs to favor centrifugal stimulus motion. To explore the contributions of these two mechanisms in the primate, we developed a realistic computational model based on connectomic reconstruction of a macaque starburst cell and the distribution of its synaptic inputs from sustained and transient bipolar cell types. Our model suggests that both mechanisms can initiate direction selectivity in starburst dendrites, but their contributions differ depending on the spatiotemporal properties of the stimulus. Specifically, the morphological mechanism dominates when small visual objects are moving at high velocities, and the space-time mechanism contributes most for large visual objects moving at low velocities.

Solution-processed oxide semiconductor-based artificial optoelectronic synapse array for spatiotemporal synaptic integration

Optoelectronic neuromorphic systems mimicking the structure and the signal processing of biological neural systems have gained significant interest due to their potential advantages such as high computing speed, high bandwidth, parallel processing, and low power requirements. Here, we demonstrate an optoelectronic synapse array consisting of solution-processed indium-gallium-zinc-oxide (IGZO) synapse devices emulating complex neural functions such as the spatiotemporal synaptic integration for neuromorphic implementation. Particularly, we adopted a vertically stacked metal-insulator-semiconductor-metal structure for IGZO synapse device to enable efficient light-induced conductance update and a low-voltage operation typically below 3 V. Using light as the stimulation, versatile synaptic functions including short-term memory/long-term memory, symmetric spike-timing dependent plasticity, and spike-number dependent plasticity were mimicked. In addition, we obtained a high recognition accuracy of handwritten digit images up to 89.4% by implementing ADAM training algorithm. Furthermore, using a crossbar structure 3 × 3 IGZO synapse array, the emulation of spatiotemporal summation was successfully carried out which is believed to be responsible for the neural encoding and the auditory recognition processes occur in the brain.

Arrhythmogenic late Ca2+ sparks in failing heart cells and their control by action potential configuration

Sudden death in heart failure patients is a major clinical problem worldwide, but it is unclear how arrhythmogenic early afterdepolarizations (EADs) are triggered in failing heart cells. To examine EAD initiation, high-sensitivity intracellular Ca2+ measurements were combined with action potential voltage clamp techniques in a physiologically relevant heart failure model. In failing cells, the loss of Ca2+ release synchrony at the start of the action potential leads to an increase in number of microscopic intracellular Ca2+ release events ("late" Ca2+ sparks) during phase 2-3 of the action potential. These late Ca2+ sparks prolong the Ca2+ transient that activates contraction and can trigger propagating microscopic Ca2+ ripples, larger macroscopic Ca2+ waves, and EADs. Modification of the action potential to include steps to different potentials revealed the amount of current generated by these late Ca2+ sparks and their (subsequent) spatiotemporal summation into Ca2+ ripples/waves. Comparison of this current to the net current that causes action potential repolarization shows that late Ca2+ sparks provide a mechanism for EAD initiation. Computer simulations confirmed that this forms the basis of a strong oscillatory positive feedback system that can act in parallel with other purely voltage-dependent ionic mechanisms for EAD initiation. In failing heart cells, restoration of the action potential to a nonfailing phase 1 configuration improved the synchrony of excitation-contraction coupling, increased Ca2+ transient amplitude, and suppressed late Ca2+ sparks. Therapeutic control of late Ca2+ spark activity may provide an additional approach for treating heart failure and reduce the risk for sudden cardiac death.

Spatiotemporal summation and correlation mimicked in a four-emitter light-induced artificial synapse

In the brain, each postsynaptic neuron interconnects many presynaptic neurons and performs functions that are related to summation and recognition as well as correlation. Based on a convolution operation and nonlinear distortion function, we propose a mathematical model to explore the elementary synaptic mechanism. A four-emitter light-induced artificial synapse is implemented on an III-nitride-on-silicon platform to validate the device concept for emulating the synaptic behaviors of a biological synapse with multiple presynaptic inputs. In addition to a progressive increase in the amplitude of successive spatiotemporal excitatory postsynaptic voltages, the differences in the stimulations are remembered for signal recognition. When repetitive stimulations are simultaneously applied and last over a long period of time, resonant spatiotemporal correlation occurs because an association is formed between the presynaptic stimulations. Four resonant spatiotemporal correlations of each triple-stimulation combination are experimentally demonstrated and agree well with the simulation results. The repetitive stimulation combinations with prime number-based periods inherently exhibit the maximum capacity of resonant spatiotemporal correlation. Our work offers a new approach to building artificial synapse networks.

Spatiotemporal Summation and Recognition Effects for a Dual-Emitter Light-Induced Neuromorphic Device

We propose and fabricate a dual-emitter light-induced neuromorphic device composed of two light-induced devices with a common collector and base. Two InGaN multiple quantum well diodes (MQW-diodes) are used as the emitters to generate light, and one InGaN MQW-diode is used as the common collector to absorb the emitted light. When the presynaptic voltages are synchronously applied to the two emitters, the collector demonstrates an adding together of the excitatory post synaptic voltage (EPSV). The width and period of the two input signals constitute the code to generate spatial summation and recognition effects at the same time. Experimental results confirm that temporal summation caused by the repetitive-pulse facilitation could significantly strengthen the spatial summation effect due to the adding together behavior when the repetitive stimulations are applied to the two emitters in rapid succession. Particularly, the resonant summation effect occurs at the co-summation region when the two repetitive-pulse signals have a resonant period, which offers a more sophisticated spatiotemporal EPSV summation function for the dual-emitter neuromorphic device.

Finger movements are mainly represented by a linear transformation of energy in band-specific ECoG signals.

Electrocardiogram (ECoG) recordings are very attractive for Brain Machine Interface (BMI) applications due to their balance between good signal to noise ratio and minimal invasiveness. The design of ECoG signal decoders is an open research area to date which requires a better understanding of the nature of these signals and how information is encoded in them. In this study, a linear and a non-linear method, Linear Regression Model (LRM) and Artificial Neural Network (ANN) respectively, were used to decode finger movements from energy in band-specific ECoG signals. It is shown that the ANN only slightly outperformed the LRM, which suggests that finger movements are mainly represented by a linear transformation of energy in band-specific ECoG signals. In addition, comparing our results to similar Electroencephalogram (EEG) studies illustrated that the spatio-temporal summation of multiple neural signals is itself linearly correlated with movement, and is not an artifact introduced by the scalp or cranium. Furthermore, a new algorithm was employed to reduce the number of spectral features of the input signals required for either of the decoding methods.

On Temporal Summation in Chaotic Neural Network with Incremental Learning

The incremental learning is a method to compose an associate memory using a chaotic neural network and provides larger capacity than correlative learning in compensation for a large amount of computation. A chaotic neuron has spatiotemporal summation in it and the temporal summation makes the learning stable to input noise. When there is no noise in input, the neuron may not need temporal summation. In this paper, to reduce the computations, a simplified network without temporal summation is introduced and investigated through the computer simulations comparing with the network as in the past, which is called here the usual network. It turns out that the simplified network has the same capacity in comparison with the usual network and can learn faster than the usual one, but that the simplified network loses the learning ability in noisy inputs. To improve this ability, the parameters in the chaotic neural network are adjusted.

The Remarkable Visual Abilities of Nocturnal Insects: Neural Principles and Bioinspired Night-Vision Algorithms

Despite their tiny eyes and brains, nocturnal insects have remarkable visual abilities. Recent work - particularly on fast-flying moths and bees and on ball-rolling dung beetles - has shown that nocturnal insects are able to distinguish colors, to detect faint movements, to learn visual landmarks, to orient to the faint pattern of polarized light produced by the moon, and to navigate using the stars. These impressive visual abilities are the result of exquisitely adapted eyes and visual systems, the product of millions of years of evolution. Even though we are only at the threshold of understanding the neural mechanisms responsible for reliable nocturnal vision, growing evidence suggests that the neural summation of photons in space and time is critically important: even though vision in dim light becomes necessarily coarser and slower, those details that are preserved are seen clearly. These benefits of spatio-temporal summation have obvious implications for dim-light video technologies. In addition to reviewing the visual adaptations of nocturnal insects, we here describe an algorithm inspired by nocturnal visual processing strategies - from amplification of primary image signals to optimized spatio-temporal summation to reduce noise - that dramatically increases the reliability of video collected in dim light, including the preservation of color.

Generation of field potentials and modulation of their dynamics through volume integration of cortical activity.

Field potentials (FPs) recorded within the brain, often called "local field potentials" (LFPs), are useful measures of net synaptic activity in a neuronal ensemble. However, due to volume conduction, FPs spread beyond regions of underlying synaptic activity, and thus an "LFP" signal may not accurately reflect the temporal patterns of synaptic activity in the immediately surrounding neuron population. To better understand the physiological processes reflected in FPs, we explored the relationship between the FP and its membrane current generators using current source density (CSD) analysis in conjunction with a volume conductor model. The model provides a quantitative description of the spatiotemporal summation of immediate local and more distant membrane currents to produce the FP. By applying the model to FPs in the macaque auditory cortex, we have investigated a critical issue that has broad implications for FP research. We have shown that FP responses in particular cortical layers are differentially susceptible to activity in other layers. Activity in the supragranular layers has the strongest contribution to FPs in other cortical layers, and infragranular FPs are most susceptible to contributions from other layers. To define the physiological processes generating FPs recorded in loci of relatively weak synaptic activity, strong effects produced by synaptic events in the vicinity have to be taken into account. While outlining limitations and caveats inherent to FP measurements, our results also suggest specific peak and frequency band components of FPs can be related to activity in specific cortical layers. These results may help improving the interpretability of FPs.

Memristor-based chaotic neural networks for associative memory

In chaotic neural networks, the rich dynamic behaviors are generated from the contributions of spatio-temporal summation, continuous output function, and refractoriness. However, a large number of spatio-temporal summations in turn make the physical implementation of a chaotic neural network impractical. This paper proposes and investigates a memristor-based chaotic neural network model, which adequately utilizes the memristor with unique memory ability to realize the spatio-temporal summations in a simple way. Furthermore, the associative memory capabilities of the proposed memristor-based chaotic neural network have been demonstrated by conventional methods, including separation of superimposed pattern, many-to-many associations, and successive learning. Thanks to the nanometer scale size and automatic memory ability of the memristors, the proposed scheme is expected to greatly simplify the structure of chaotic neural network and promote the hardware implementation of chaotic neural networks.

Adaptation to Changes in Higher-Order Stimulus Statistics in the Salamander Retina

Adaptation in the retina is thought to optimize the encoding of natural light signals into sequences of spikes sent to the brain. While adaptive changes in retinal processing to the variations of the mean luminance level and second-order stimulus statistics have been documented before, no such measurements have been performed when higher-order moments of the light distribution change. We therefore measured the ganglion cell responses in the tiger salamander retina to controlled changes in the second (contrast), third (skew) and fourth (kurtosis) moments of the light intensity distribution of spatially uniform temporally independent stimuli. The skew and kurtosis of the stimuli were chosen to cover the range observed in natural scenes. We quantified adaptation in ganglion cells by studying linear-nonlinear models that capture well the retinal encoding properties across all stimuli. We found that the encoding properties of retinal ganglion cells change only marginally when higher-order statistics change, compared to the changes observed in response to the variation in contrast. By analyzing optimal coding in LN-type models, we showed that neurons can maintain a high information rate without large dynamic adaptation to changes in skew or kurtosis. This is because, for uncorrelated stimuli, spatio-temporal summation within the receptive field averages away non-gaussian aspects of the light intensity distribution.

Direction Selectivity Mediated by Adaptation in the Owl's Inferior Colliculus

Motion direction is a crucial cue for predicting future states in natural scenes. In the auditory system, the mechanisms that confer direction selectivity to neurons are not well understood. Neither is it known whether sound motion is encoded independently of stationary sound location. Here we investigated these questions in neurons of the owl's external nucleus of the inferior colliculus, where auditory space is represented in a map. Using a high-density speaker array, we show that the preferred direction and the degree of direction selectivity can be predicted by response adaptation to sounds moving over asymmetric spatial receptive fields. At the population level, we found that preference for sounds moving toward frontal space increased with eccentricity in spatial tuning. This distribution was consistent with larger receptive-field asymmetry in neurons tuned to more peripheral auditory space. A model of suppression based on spatiotemporal summation predicted the observations. Thus, response adaptation and receptive-field shape can explain direction selectivity to acoustic motion and an orderly distribution of preferred direction.

Locomotion Control of MEMS Microrobot Using Pulse‐Type Hardware Neural Networks

SUMMARYThis paper presents the locomotion control of a microelectromechanical system (MEMS) microrobot. The MEMS microrobot demonstrates locomotion control by pulse‐type hardware neural networks (P‐HNN). P‐HNN generate oscillatory patterns of electrical activity like those of living organisms. The basic component of P‐HNN is a pulse‐type hardware neuron model (P‐HNM). The P‐HNM has the same basic features as biological neurons, such as the threshold, the refractory period, and spatiotemporal summation characteristics, and allows the generation of continuous action potentials. P‐HNN has been constructed with MOSFETs and can be integrated by CMOS technology. Like living organisms, P‐HNN has realized robot control without using software programs or A/D converters. The size of the microrobot fabricated by MEMS technology was 4 × 4 × 3.5 mm. The frame of the robot was made of a silicon wafer, equipped with rotary actuators, link mechanisms, and six legs. The MEMS microrobot emulated the locomotion method and the neural networks of an insect by rotary actuators, link mechanisms, and the P‐HNN. We show that the P‐HNN can control the forward and backward locomotion of the fabricated MEMS microrobot, and that it is possible to switch its direction by inputting an external trigger pulse. The locomotion speed was 19.5 mm/min and the step size was 1.3 mm. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 186(3): 43–50, 2014; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.22473

A Reconfigurable Single-Photon-Counting Integrating Receiver for Optical Communications

A reconfigurable Single-Photon Avalanche Diode integrating receiver in standard 130 nm CMOS is presented for optical links with an array readout bandwidth of 100 MHz. A maximum count rate of 58 G photon/s is observed, with a dynamic range of ≈ 79 dB, a sensitivity of ≈ - 31.7 dBm at 100 MHz and a BER of ≈ 1 ×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-9</sup> . The sensor core draws 89 mW at the maximum count rate and obtains a peak SNR of ≈157 dB. We investigate the properties of the receiver for optical communications in the visible spectrum, using its added functionality and reconfigurability to experimentally explore non-ideal influences. The all-digital 32 × 32 SPAD array, achieves a minimum dead time of 5.9 ns, and a median dark count rate of 2.5 kHz/SPAD. The internal gain of SPADs and spatio-temporal summation removes the need for analogue amplification. High noise devices can be weighted or removed to optimize the SNR. The power requirements, transient response and received data are explored and limiting factors similar to those of photodiode receivers are observed.

1P1-B09 生物の脳を模したパルス形ハードウェアニューラルネットワークによるサーボモータ用PWM波形の生成(脳・神経・認知ロボティクス)

This paper presents the pulse-type hardware neural networks (P-HNN) which could output the pulse width modulation (PWM) waveform of PWM servo motor for robots. Basic components of P-HNN were pulse type hardware neuron model (P-HNM). P-HNM consisted by the cell body model, the axon model and the synaptic model. P-HNM had the same basic features of biological neurons such as threshold, refractory period, spatio-temporal summation characteristics and enabled the generation of continuous action potentials. We used the synaptic model which could vary the synaptic weights by controlling voltage. As a result, we showed that P-HNN could control the PWM servo motor from 0 to 180 degrees.