Spike Responses Research Articles

Stress-induced artificial neuron spiking in diffusive memristors

Diffusive memristors owing to their ability to produce current spiking when a constant or slowly changing voltage is applied are competitive candidates for development of artificial electronic neurons. These artificial neurons can be integrated into various prospective autonomous and robotic systems as sensors, e.g. ones implementing object grasping and classification. We report here Ag nanoparticle-based diffusive memristor prepared on a flexible polyethylene terephthalate substrate in which the electric spiking behaviour was induced by the electric voltage under an additional stimulus of external mechanical impact. By changing the magnitude and frequency of the mechanical impact, we are able to manipulate the spiking response of our artificial neuron. This functionality to control the spiking characteristics paves a pathway for the development of touch-perception sensors that can convert local pressure into electrical spikes for further processing in neural networks. We have proposed a mathematical model which captures the operation principle of the fabricated memristive sensors and qualitatively describes the measured spiking behaviour. Employing such flexible diffusive memristors that can directly translate tactile information into spikes, similar to force and pressure sensors, could offer substantial benefits for various applications in robotics.

Differential intrinsic firing properties in sustained and transient mouse alpha RGCs match their light response characteristics and persist during retinal degeneration.

Retinal ganglion cells (RGCs) are the neuronal connections between the eye and the brain conveying multiple features of the outside world through parallel pathways. While there is a large body of literature how these pathways arise in the retinal network, the process of converting presynaptic inputs into RGC spiking output is little understood. In this study, we show substantial differences in the spike generator across three types of alpha RGCs in female and male mice, the αON sustained, αOFF sustained and αOFF transient RGC. The differences in their intrinsic spiking responses match the differences of the light responses across RGC types. While sustained RGC types have spike generators that are able to generate sustained trains of action potentials at high rates, the transient RGC type fired shortest action potentials enabling it to fire high-frequency transient bursts. The observed differences were also present in late-stage photoreceptor-degenerated retina demonstrating long-term functional stability of RGC responses even when presynaptic circuitry is deteriorated for long periods of time. Our results demonstrate that intrinsic cell properties support the presynaptic retinal computation and are, once established, independent of them.Significance Statement Spiking output from retinal ganglion cells (RGCs) has long been thought to be solely determined by synaptic inputs from the retinal network. We show that the cell-intrinsic spike generator varies across RGC populations and therefore that postsynaptic processing shapes retinal spiking output in three types of mouse alpha RGCs (αRGCs). While sustained αRGC types have spike generators that are able to generate sustained trains of action potentials at high rates, the transient αRGC type fired shortest action potentials enabling them to fire high-frequency transient bursts. Computational modeling suggests that intrinsic response differences are not driven by dendritic morphology but by somatodendritc biophysics. After photoreceptor degeneration, the observed variability is preserved indicating stable physiology across the three αRGC types.

Effects of elevated intraocular pressure on alpha ganglion cells in experimental glaucoma mice

Glaucoma is a leading cause of blindness worldwide and glaucoma patients exhibit an early diffuse loss of retinal sensitivity followed by focal loss of RGCs. Combining some previous published results and some new data, this paper provides our current view on how high IOP (H-IOP) affects the light response sensitivity of a subset of RGCs, the alpha-ganglion cells (αGCs), as well as their presynaptic bipolar cells (DBCs and HBCs) and A2 amacrine cells (AIIACs) in dark-adapted mouse retinas. Our data demonstrate that H-IOP in experimental glaucoma mice significantly decreases light-evoked spike response sensitivity of sONαGCs and sOFFαGCs (i.e., raises thresholds by 1.5–2.5 log units), but not that of the tONαGCs and tOFFαGCs. The sensitivity loss in sONαGCs and sOFFαGCs is mediated by a H-IOP induced suppression of AIIAC response which is caused by a decrease of transmission efficacy of the DBCR→AIIAC synapse. We also provide evidence supporting the hypothesis that BK channels in the A17AC→DBCR feedback synapse are the H-IOP sensor that regulates the DBCR→AIIAC synaptic efficacy, as BK channel blocker IBTX mimics the action of H-IOP. Our results provide useful information for designing strategies for early detection and possible treatments of glaucoma as physiological changes occur before irreversible structural damage.

Auditory competition and stimulus selection across spatial locations from midbrain to forebrain in barn owls

Barn owls enable investigation of neural mechanisms underlying stimulus selection of concurrent stimuli. The audio-visual space map in the optic tectum (OT), avian homologue of the superior colliculus, encodes relative strength of concurrent auditory stimuli through spike response rate and interneuronal spike train synchrony (STS). Open questions remain regarding stimulus selection in downstream forebrain regions lacking topographic coding of auditory space, including the functional consequences of interneuronal STS on interregional signaling. To this end, we presented concurrent stimuli at different locations and manipulated relative strength while simultaneously recording neural responses from OT and its downstream thalamic target, nucleus rotundus (nRt), in awake barn owls of both sexes. Results demonstrated that broadly spatially tuned nRt units exhibit different spike response patterns to competition depending on spatial tuning preferences. Modeling suggests nRt units integrate convergent inputs from distant locations across midbrain map regions. Additionally, STS within nRt reflects the temporal properties of the strongest stimulus. Furthermore, interregional STS between OT and nRt was strongest when spatial tuning overlap between units across regions was large and when the strongest stimulus location during competition was favorable for units in both regions. Additionally, though gamma oscillations synthesized within OT are weakly propagated within nRt, average gamma power across regions correlates with strength of interregional STS. Overall, we demonstrate that nRt integrates inputs across distant areas of OT, retains spatial information through differences in strength of inputs from various locations of the midbrain map across neurons, and prioritizes coding of identity features to the strongest sound.Significance StatementThe brain strategically selects and preferentially processes salient stimuli. A critical function to this process involves transferring salient information across regions that may exhibit drastic transformations in coding schemes. Our study in barn owls investigates bottom-up signaling between the midbrain space map and its downstream thalamic target, which lacks spatial topography as also observed in mammalian auditory forebrain regions to elucidate general mechanisms underlying how spatial location information and other properties of the strongest sound are relayed between regions. Results show that the thalamus integrates neural responses widely across the midbrain map, retains coding of spatial location through varying strength of inputs of the map across neurons, and prioritizes further coding of identity features only to the strongest sound.

Exploration on the spiking response of a single compartment neuron with multiple active properties under electrical stimuli

The study of modulating the neuronal signals by electrical stimuli is important to intervene the abnormal neuronal firing and bring them to a normal state. Spike trains, which are the highest quality of brain signals, have been deficiently explored and analyzed owing to the challenges of obtaining them in reality. In this regard, this paper aims to investigate and analyze the effect of electrical stimuli on the spiking response of neurons, and the following work is to be carried out. The relationships between the spiking response and three parameters (namely, the amplitude of the electrode current (EC), the angular velocity of the electric field current (EFC), and the signal-noise ratio (SNR)) are examined on a neuronal model with spatial length and multiple active properties. When specific currents with different SNRs are imposed on the neurons, their influence on the spiking response is further explored. With regard to the spiking response, the main focus is on three characteristics, i.e., the spiking pattern, the spike count (SC), and the spiking arrangement. An algorithm, called the return map distance (RMD) algorithm, is proposed in this paper, and gives the classification of spiking patterns a quantitative criterion. Based on it, the spiking patterns are classified in this paper as busting spike train, regular spike train (RST), and meager spike train (MST). Simulation results indicate that both the amplitude of the EC and the angular velocity of the EFC change the neuronal spiking patterns. As the amplitude (angular velocity) of the EC (EFC) increases, the spiking pattern of the Soldado-Magraner model (SMM) eventually tends to RST (MST). In addition, the SC increases with the amplitude of the EC, while it does not hold for the SC with respect to the angular velocity of the EFC. Furthermore, the spiking arrangement and the SC are severely destroyed for the EC with low SNRs, while three spiking features of the SMM under EFC are all robust to the different SNRs, which implies that compared with the EC, the spiking responses of the SMM under EFC are more stable. The findings in this paper may provide some theoretical guidance to the fields related to neuronal firing, such as brain-computer interfaces and electrotherapy. The RMD algorithm proposed here can be applied to more individual neurons, and the spiking arrangement discussed here could be regarded as an effective encoding way for spike trains.

Mechanically evoked spike responses of pentascolopidial chordotonal organs of Drosophila melanogaster larvae.

Mechanosensitive ensembles of neurons in insects, known as chordotonal organs (COs), function in proprioception, the detection of sound and substrate vibrations. Here, we characterized the mechanical sensitivity of the lateral pentascolopidial CO (lch5) of Drosophila melanogaster larvae to establish its postulated role in proprioception. We developed a physiologically realistic method to replicate proprioceptive input to lch5 by pulling the apodeme (tendon) to which the tips of the neurons attach. We found that lch5 sensory neurons respond transiently with a short latency to the velocity component of stretch displacements and the release of stretch (relaxation). In the mechanosensory mutant inactive, lch5 has a decreased response to mechanical stimuli and a lower overall spontaneous spike rate. Finally, we simulated the input that lch5 receives during crawling and observed spike rate changes of peristaltic body contraction. We provide a characterization of proprioceptive feedback in D. melanogaster larvae and firmly establish the proprioceptive function of lch5 in larval locomotion.

Artificial Amacrine Retinal Circuits.

Event-based imaging represents a new paradigm in visual information processing that addresses the speed and energy efficiency shortcomings inherently present in the current complementary metal oxide semiconductor-based machine vision. Realizing such imaging systems has previously been sought using very large-scale integration technologies that have complex circuitries consisting of many photodiodes, differential amplifiers, capacitors, and resistors. Here, we demonstrate that event-driven sensing can be achieved using a simple one-resistor, one-capacitor (1R1C) circuit, where the capacitor is modified with colloidal quantum dots (CQDs) to have a photoresponse. This sensory circuit emulates the motion-tracking function of the biological retina, in which the amacrine cells in the bipolar-to-ganglion synaptic pathway produce a transient spiking signal only in response to changes in light intensity but remain inactive under constant illumination. When extended to a 2D imaging array, the individual sensors work independently and output signals only when a change in the light intensity is detected; hence, the concept of the frame in image processing is thereby removed. In this work, we present the fabrication and characterization of a CQD photocapacitor-based 1R1C circuit that has a spectral response at 1550 nm in the short-wave infrared (SWIR). We report on the key performance parameters including peak responsivity, noise, and optical noise equivalent power and discuss the operating mechanism that is responsible for spiking responses in these artificial retinal circuits. The present work sets the foundation for expanding the bioinspired vision sensor capability toward midwave infrared (MWIR) and long-wave infrared (LWIR) spectral regions that are invisible to human eyes and mainstream semiconductor technologies.

A Survey on Neuromorphic Architectures for Running Artificial Intelligence Algorithms

Neuromorphic computing, a brain-inspired non-Von Neumann computing system, addresses the challenges posed by the Moore’s law memory wall phenomenon. It has the capability to enhance performance while maintaining power efficiency. Neuromorphic chip architecture requirements vary depending on the application and optimising it for large-scale applications remains a challenge. Neuromorphic chips are programmed using spiking neural networks which provide them with important properties such as parallelism, asynchronism, and on-device learning. Widely used spiking neuron models include the Hodgkin–Huxley Model, Izhikevich model, integrate-and-fire model, and spike response model. Hardware implementation platforms of the chip follow three approaches: analogue, digital, or a combination of both. Each platform can be implemented using various memory topologies which interconnect with the learning mechanism. Current neuromorphic computing systems typically use the unsupervised learning spike timing-dependent plasticity algorithms. However, algorithms such as voltage-dependent synaptic plasticity have the potential to enhance performance. This review summarises the potential neuromorphic chip architecture specifications and highlights which applications they are suitable for.

Dynamic gating of perceptual flexibility by non-classically responsive cortical neurons.

The ability to flexibly respond to sensory cues in dynamic environments is essential to adaptive auditory-guided behaviors. Cortical spiking responses during behavior are highly diverse, ranging from reliable trial-averaged responses to seemingly random firing patterns. While the reliable responses of 'classically responsive' cells have been extensively studied for decades, the contribution of irregular spiking 'non-classically responsive' cells to behavior has remained underexplored despite their prevalence. Here, we show that flexible auditory behavior results from interactions between local auditory cortical circuits comprised of heterogeneous responses and inputs from secondary motor cortex. Strikingly, non-classically responsive neurons in auditory cortex were preferentially recruited during learning, specifically during rapid learning phases when the greatest gains in behavioral performance occur. Population-level decoding revealed that during rapid learning mixed ensembles comprised of both classically and non-classically responsive cells encode significantly more task information than homogenous ensembles of either type and emerge as a functional unit critical for learning. Optogenetically silencing inputs from secondary motor cortex selectively modulated non-classically responsive cells in the auditory cortex and impaired reversal learning by preventing the remapping of a previously learned stimulus-reward association. Top-down inputs orchestrated highly correlated non-classically responsive ensembles in sensory cortex providing a unique task-relevant manifold for learning. Thus, non-classically responsive cells in sensory cortex are preferentially recruited by top-down inputs to enable neural and behavioral flexibility.

Contributions of cortical neuron firing patterns, synaptic connectivity, and plasticity to task performance

Neuronal responses during behavior are diverse, ranging from highly reliable ‘classical’ responses to irregular ‘non-classically responsive’ firing. While a continuum of response properties is observed across neural systems, little is known about the synaptic origins and contributions of diverse responses to network function, perception, and behavior. To capture the heterogeneous responses measured from auditory cortex of rodents performing a frequency recognition task, we use a novel task-performing spiking recurrent neural network incorporating spike-timing-dependent plasticity. Reliable and irregular units contribute differentially to task performance via output and recurrent connections, respectively. Excitatory plasticity shifts the response distribution while inhibition constrains its diversity. Together both improve task performance with full network engagement. The same local patterns of synaptic inputs predict spiking response properties of network units and auditory cortical neurons from in vivo whole-cell recordings during behavior. Thus, diverse neural responses contribute to network function and emerge from synaptic plasticity rules.

Constraints on the subsecond modulation of striatal dynamics by physiological dopamine signaling.

Dopaminergic neurons play a crucial role in associative learning, but their capacity to regulate behavior on subsecond timescales remains debated. It is thought that dopaminergic neurons drive certain behaviors by rapidly modulating striatal spiking activity; however, a view has emerged that only artificially high (that is, supra-physiological) dopamine signals alter behavior on fast timescales. This raises the possibility that moment-to-moment striatal spiking activity is not strongly shaped by dopamine signals in the physiological range. To test this, we transiently altered dopamine levels while monitoring spiking responses in the ventral striatum of behaving mice. These manipulations led to only weak changes in striatal activity, except when dopamine release exceeded reward-matched levels. These findings suggest that dopaminergic neurons normally play a minor role in the subsecond modulation of striatal dynamics in relation to other inputs and demonstrate the importance of discerning dopaminergic neuron contributions to brain function under physiological and potentially nonphysiological conditions.

Local field potential sharp waves with diversified impact on cortical neuronal encoding of haptic input

Cortical sensory processing is greatly impacted by internally generated activity. But controlling for that activity is difficult since the thalamocortical network is a high-dimensional system with rapid state changes. Therefore, to unwind the cortical computational architecture there is a need for physiological ‘landmarks’ that can be used as frames of reference for computational state. Here we use a waveshape transform method to identify conspicuous local field potential sharp waves (LFP-SPWs) in the somatosensory cortex (S1). LFP-SPW events triggered short-lasting but massive neuronal activation in all recorded neurons with a subset of neurons initiating their activation up to 20 ms before the LFP-SPW onset. In contrast, LFP-SPWs differentially impacted the neuronal spike responses to ensuing tactile inputs, depressing the tactile responses in some neurons and enhancing them in others. When LFP-SPWs coactivated with more distant cortical surface (ECoG)-SPWs, suggesting an involvement of these SPWs in global cortical signaling, the impact of the LFP-SPW on the neuronal tactile response could change substantially, including inverting its impact to the opposite. These cortical SPWs shared many signal fingerprint characteristics as reported for hippocampal SPWs and may be a biomarker for a particular type of state change that is possibly shared byboth hippocampus and neocortex.

An Ion-Mediated Spiking Chemical Neuron based on Mott Memristor.

Artificial spiking neurons capable of interpreting ionic information into electrical spikes are critical to mimic biological signaling systems. Mott memristors are attractive for constructing artificial spiking neurons due to their simple structure, low energy consumption, and rich neural dynamics. However, challenges remain in achieving ion-mediated spiking and biohybrid-interfacing in Mott neurons. Here, a biomimetic spiking chemical neuron (SCN) utilizing an NbOx Mott memristor and oxide field-effect transistor-type chemical sensor is introduced. The SCN exhibits both excitation and inhibition spiking behaviors toward ionic concentrations akin to biological neural systems. It demonstrates spiking responses across physiological and pathological Na+ concentrations (1-200× 10-3 m). The Na+-mediated SCN enables both frequency encoding and time-to-first-spike coding schemes, illustrating the rich neural dynamics of Mott neuron. In addition, the SCN interfaced with L929 cells facilitates real-time modulation of ion-mediated spiking under both normal and salty cellular microenvironments.

Dynamics of Leaky Integrate‐and‐Fire Neurons Based on Oxyvanite Memristors for Spiking Neural Networks

Neuromorphic computing implemented with spiking neural networks (SNNs) based on volatile threshold switching is an energy‐efficient computing paradigm that may overcome future limitations of the von Neumann architecture. Herein, threshold switching in oxyvanite (V3O5) memristors and their application as a leaky integrate‐and‐fire (LIF) neuron are explored. The spiking response of individual neurons is examined as a function of circuit parameters, input pulse train, and temperature and reveals a pulse height‐dependent spike rate in which devices exhibit excitatory spiking behavior under low input voltages and protective inhibition spiking under high voltages. Resistively coupled LIF neurons are shown to exhibit additional neural functionalities (i.e., phasic, regular and adaptation, etc.) depending on the input voltage and circuit parameters. The behavior of both individual and coupled neurons is shown to be described by a physics‐based lumped element circuit model, which therefore provides a solid foundation for exploring more complex systems. Finally, the performance of a perceptron SNN employing these LIF neurons is assessed by simulating the classification of image recognition algorithm. These results advance the development of robust solid‐state neurons with low power consumption for neuromorphic computing.

Membrane depolarization mediates both the inhibition of neural activity and cell-type-differences in response to high-frequency stimulation

Neuromodulation using high frequency (>1 kHz) electric stimulation (HFS) enables preferential activation or inhibition of individual neural types, offering the possibility of more effective treatments across a broad spectrum of neurological diseases. To improve effectiveness, it is important to better understand the mechanisms governing activation and inhibition with HFS so that selectivity can be optimized. In this study, we measure the membrane potential (Vm) and spiking responses of ON and OFF α-sustained retinal ganglion cells (RGCs) to a wide range of stimulus frequencies (100–2500 Hz) and amplitudes (10–100 µA). Our findings indicate that HFS induces shifts in Vm, with both the strength and polarity of the shifts dependent on the stimulus conditions. Spiking responses in each cell directly correlate with the shifts in Vm, where strong depolarization leads to spiking suppression. Comparisons between the two cell types reveal that ON cells are more depolarized by a given amplitude of HFS than OFF cells—this sensitivity difference enables the selective targeting. Computational modeling indicates that ion-channel dynamics largely account for the shifts in Vm, suggesting that a better understanding of the differences in ion-channel properties across cell types may improve the selectivity and ultimately, enhance HFS-based neurostimulation strategies.

H7 modulation of the L3 auditory neuron and phonotaxis in the cricket Acheta domesticus

AbstractSeveral studies have implicated the L3 auditory interneuron in the regulation of syllable period selective phonotaxis in female cricket Acheta domesticus. The L3's response to model calls of conspecific males comprises of an immediate and a prolonged response. The kinetics of activation of these electrical activities are consistent with sequential activation of ionotropic and metabotropic mechanisms. In this study, we used electrophysiological and pharmacological tools to investigate the cellular mechanisms underlying L3's response. Bath application of the synthetic protein kinase inhibitor 1‐(5‐isoquinolinesul‐fonyl)‐2‐methylpiperazine (H7), results in the suppression of L3's spiking response, and this effect can be reversed by saline wash. Additionally, when female A. domesticus that were previously nano‐injected with H7 were tested for phonotaxis on a non‐compensating treadmill, they demonstrated suppression of syllable period‐dependent phonotaxis. These findings implicate protein kinase in the regulation of L3's spiking rhythm and the associated phonotaxis in A. domesticus.

Biorealistic Neuronal Temperature-Sensitive Dynamics within Threshold Switching Memristors: Toward Neuromorphic Thermosensation.

Neuromorphic nanoelectronic devices that can emulate the temperature-sensitive dynamics of biological neurons are of great interest for bioinspired robotics and advanced applications such as in silico neuroscience. In this work, we demonstrate the biomimetic thermosensitive properties of two-terminal V3O5 memristive devices and showcase their similarity to the firing characteristics of thermosensitive biological neurons. The temperature-dependent electrical characteristics of V3O5-based memristors are used to understand the spiking response of a simple relaxation oscillator. The temperature-dependent dynamics of these oscillators are then compared with those of biological neurons through numerical simulations of a conductance-based neuron model, the Morris-Lecar neuron model. Finally, we demonstrate a robust neuromorphic thermosensation system inspired by biological thermoreceptors for bioinspired thermal perception and representation. These results not only demonstrate the biorealistic emulative potential of threshold-switching memristors but also establish V3O5 as a functional material for realizing solid-state neurons for neuromorphic computing and sensing applications.

Characterizing the aggregated encoding method utilizing bursts activated by a VCSEL-neuron with a feedback structure

The rapid advancement of photonic technologies has facilitated the development of photonic neurons that emulate neuronal functionalities akin to those observed in the human brain. Neuronal bursts frequently occur in behaviors where information is encoded and transmitted. Here, we present the demonstration of the bursting response activated by an artificial photonic neuron. This neuron utilizes a single vertical-cavity surface-emitting laser (VCSEL) and encodes multiple stimuli effectively by varying the spike count during a burst based on the polarization competition in the VCSEL. By virtue of the modulated optical injection in the VCSEL employed to trigger the spiking response, we activate bursts output in the VCSEL with a feedback structure in this scheme. The bursting response activated by the VCSEL-neuron exhibits neural signal characteristics, promising an excitation threshold and the refractory period. Significantly, this marks the inaugural implementation of a controllable integrated encoding scheme predicated on bursts within photonic neurons. There are two remarkable merits; on the one hand, the interspike interval of bursts is distinctly diminished, amounting to merely one twenty-fourth compared to that observed in optoelectronic oscillators. Moreover, the interspike period of bursts is about 70.8% shorter than the period of spikes activated by a VCSEL neuron without optical feedback. Our results may shed light on the analogy between optical and biological neurons and open the door to fast burst encoding-based optical systems with a speed several orders of magnitude faster than their biological counterparts.

Auditory Competition and Coding of Relative Stimulus Strength across Midbrain Space Maps of Barn Owls.

The natural environment challenges the brain to prioritize the processing of salient stimuli. The barn owl, a sound localization specialist, exhibits a circuit called the midbrain stimulus selection network, dedicated to representing locations of the most salient stimulus in circumstances of concurrent stimuli. Previous competition studies using unimodal (visual) and bimodal (visual and auditory) stimuli have shown that relative strength is encoded in spike response rates. However, open questions remain concerning auditory-auditory competition on coding. To this end, we present diverse auditory competitors (concurrent flat noise and amplitude-modulated noise) and record neural responses of awake barn owls of both sexes in subsequent midbrain space maps, the external nucleus of the inferior colliculus (ICx) and optic tectum (OT). While both ICx and OT exhibit a topographic map of auditory space, OT also integrates visual input and is part of the global-inhibitory midbrain stimulus selection network. Through comparative investigation of these regions, we show that while increasing strength of a competitor sound decreases spike response rates of spatially distant neurons in both regions, relative strength determines spike train synchrony of nearby units only in the OT. Furthermore, changes in synchrony by sound competition in the OT are correlated to gamma range oscillations of local field potentials associated with input from the midbrain stimulus selection network. The results of this investigation suggest that modulations in spiking synchrony between units by gamma oscillations are an emergent coding scheme representing relative strength of concurrent stimuli, which may have relevant implications for downstream readout.

On-chip spiking neural networks based on add-drop ring microresonators and electrically reconfigurable phase-change material photonic switches

We propose and numerically demonstrate a photonic computing primitive designed for integrated spiking neural networks (SNNs) based on add-drop ring microresonators (ADRMRs) and electrically reconfigurable phase-change material (PCM) photonic switches. In this neuromorphic system, the passive silicon-based ADRMR, equipped with a power-tunable auxiliary light, effectively demonstrates nonlinearity-induced dual neural dynamics encompassing spiking response and synaptic plasticity that can generate single-wavelength optical neural spikes with synaptic weight. By cascading these ADRMRs with different resonant wavelengths, weighted multiple-wavelength spikes can be feasibly output from the ADRMR-based hardware arrays when external wavelength-addressable optical pulses are injected; subsequently, the cumulative power of these weighted output spikes is utilized to ascertain the activation status of the reconfigurable PCM photonic switches. Moreover, the reconfigurable mechanism driving the interconversion of the PCMs between the resonant-bonded crystalline states and the covalent-bonded amorphous states is achieved through precise thermal modulation. Drawing from the thermal properties, an innovative thermodynamic leaky integrate-and-firing (TLIF) neuron system is proposed. With the TLIF neuron system as the fundamental unit, a fully connected SNN is constructed to complete a classic deep learning task: the recognition of handwritten digit patterns. The simulation results reveal that the exemplary SNN can effectively recognize 10 numbers directly in the optical domain by employing the surrogate gradient algorithm. The theoretical verification of our architecture paves a whole new path for integrated photonic SNNs, with the potential to advance the field of neuromorphic photonic systems and enable more efficient spiking information processing.