Neural Firing Patterns Research Articles

Network influence determines the impact of cortical ensembles on stimulus detection.

Observation of neural firing patterns can constrain theories for the types of activity patterns that the brain uses to guide behavior. However, directly perturbing these patterns, ideally with great specificity, is required to causally test any particular theory. We combined two-photon imaging and cellular resolution optogenetic photo-stimulation to causally test how neural activity in the mouse visual cortex is read out to detect visual stimuli. Contrary to expectations, targeted activation of highly sensitive neural ensembles did not preferentially modify behavior compared to random ensembles, contradicting a longstanding hypothesis for how neural activity drives stimulus detection. Instead, the main predictor of a targeted neural ensemble's impact on perception was its effect on network activity. This argues that downstream regions summate visual cortex activity without preferentially weighting more informative neurons to make sensory detection decisions. Comparing mouse behavioral performance to decoding models of neural activity implies that mice employ this simple, albeit suboptimal strategy to solve the task. This work challenges conventional notions for how sensory representations mediate perception and demonstrates that specific neural perturbations are critical for determining which features of neural activity drive behavior.

Effect of neural firing pattern on NbOx/Al2O3 memristor-based reservoir computing system

The implementation of reservoir computing using resistive random-access memory as a physical reservoir has attracted attention due to its low training cost and high energy efficiency during parallel data processing. In this work, a NbOx/Al2O3-based memristor device was fabricated through a sputter and atomic layer deposition process to realize reservoir computing. The proposed device exhibits favorable resistive switching properties (>103 cycle endurance) and demonstrates short-term memory characteristics with current decay. Utilizing the controllability of the resistance state and its variability during cycle repetition, electrical pulses are applied to investigate the synapse-emulating properties of the device. The results showcase the functions of potentiation and depression, the coexistence of short-term and long-term plasticity, excitatory post-synaptic current, and spike-rate dependent plasticity. Building upon the functionalities of an artificial synapse, pulse spikes are categorized into three distinct neural firing patterns (normal, adapt, and boost) to implement 4-bit reservoir computing, enabling a significant distinction between “0” and “1.”

High‐Performance GdNiO3 Epitaxial Film Memristor for Neuralactivity Analysis

AbstractPerovskite‐type rare earth nickelates based memristor have recently attracted extensive attention in the field of novel storage computing due to their special electronic structure and exotic physical properties. However, there is still a shortage of memristors with ultra‐high stability performance, which will provide a solid foundation for future neural network computing with high accuracy recognition rates. Here, a GdNiO3‐based interfacial memristor is presented, which possesses ultra‐high stable performance, such as electroforming‐free, low device‐to‐device variation, reliable cyclic switching, high on/off ratio (≈104) and stable pulse modulation of conduction. Combined with the comprehensive microstructure results, this behavior is ascribed to the interface Schottky barrier variation caused by the 1D oxygen vacancy channel conduction according to the transmission electron microscopy results. In particular, based on the device's stable pulse modulation plasticity performance, the study also succeeds in achieving highly accurate neural firing pattern recognition up to ≈99.75% accuracy and monitoring of pattern transitions by implementing a reservoir computing system based on the device. This research advances the progress of nickelates in novel storage computing and paves the way for future efficient memristor‐based reservoir computing systems to handle more complex temporal tasks.

Unveiling transient current response in bilayer oxide-based physical reservoirs for time-series data analysis.

Physical reservoirs employed to map time-series data and analyze extracted features have attracted interest owing to their low training cost and mitigated interconnection complexity. This study reports a physical reservoir based on a bilayer oxide-based dynamic memristor. The proposed device exhibits a nonlinear current response and short-term memory (STM), satisfying the requirements of reservoir computing (RC). These characteristics are validated using a compact model to account for resistive switching (RS) via the dynamic evolution of the internal state variable and the relocation of oxygen vacancies. Mathematically, the transient current response can be quantitatively described according to a simple set of equations to correlate the theoretical framework with experimental results. Furthermore, the device shows significant reliability and ability to distinguish 4-bit inputs and four diverse neural firing patterns. Therefore, this work shows the feasibility of implementing physical reservoirs in hardware and advances the understanding of the dynamic response.

Distribution Patterns of Subgroupsof Inhibitory Neurons Divided by Calbindin 1.

The inhibitory neurons in the brain play an essential role in neural network firing patterns by releasing γ-aminobutyric acid (GABA) as the neurotransmitter. In the mouse brain, based on the protein molecular markers, inhibitory neurons are usually to be divided into three non-overlapping groups: parvalbumin (PV), neuropeptide somatostatin (SST), and vasoactive intestinal peptide (VIP)-expressing neurons. Each neuronal group exhibited unique properties in molecule, electrophysiology, circuitry, and function. Calbindin 1 (Calb1), a ubiquitous calcium-binding protein, often acts as a "divider" in excitatory neuronal classification. Based on Calb1 expression, the excitatory neurons from the same brain region can be classified into two subgroups with distinct properties. Besides excitatory neurons, Calb1 also expresses in part of inhibitory neurons. But, to date, little research focused on the intersectional relationship between inhibitory neuronal subtypes and Calb1. In this study, we genetically targeted Calb1-expression (Calb1+) and Calb1-lacking (Calb1-) subgroups of PV and SST neurons throughout the mouse brain by flexibly crossing transgenic mice relying on multi-recombinant systems, and the distribution patterns and electrophysiological properties of each subgroup were further demonstrated. Thus, this study provided novel insights and strategies into inhibitory neuronal classification.

T-type Ca2+ and persistent Na+ currents synergistically elevate ventral, not dorsal, entorhinal cortical stellate cell excitability

Dorsal and ventral medial entorhinal cortex (mEC) regions have distinct neural network firing patterns to differentially support functions such as spatial memory. Accordingly, mEC layer II dorsal stellate neurons are less excitable than ventral neurons. This is partly because the densities of inhibitory conductances are higher in dorsal than ventral neurons. Here, we report that T-type Ca2+ currents increase 3-fold along the dorsal-ventral axis in mEC layer II stellate neurons, with twice as much CaV3.2 mRNA in ventral mEC compared with dorsal mEC. Long depolarizing stimuli trigger T-type Ca2+ currents, which interact with persistent Na+ currents to elevate the membrane voltage and spike firing in ventral, not dorsal, neurons. T-type Ca2+ currents themselves prolong excitatory postsynaptic potentials (EPSPs) to enhance their summation and spike coupling in ventral neurons only. These findings indicate that T-type Ca2+ currents critically influence the dorsal-ventral mEC stellate neuron excitability gradient and, thereby, mEC dorsal-ventral circuit activity.

Optogenetic Generation of Neural Firing Patterns with Temporal Shaping of Light Pulses

The fundamental process of information processing and memory formation in the brain is associated with complex neuron firing patterns, which can occur spontaneously or be triggered by sensory inputs. Optogenetics has revolutionized neuroscience by enabling precise manipulation of neuronal activity patterns in specified neural populations using light. However, the light pulses used in optogenetics have been primarily restricted to square waveforms. Here, we present a detailed theoretical analysis of the temporal shaping of light pulses in optogenetic excitation of hippocampal neurons and neocortical fast-spiking interneurons expressed with ultrafast (Chronos), fast (ChR2), and slow (ChRmine) channelrhodopsins. Optogenetic excitation has been studied with light pulses of different temporal shapes that include square, forward-/backward ramps, triangular, left-/right-triangular, Gaussian, left-/right-Gaussian, positive-sinusoidal, and left-/right-positive sinusoidal. Different light shapes result in significantly different photocurrent amplitudes and kinetics, spike-timing, and spontaneous firing rate. For short duration stimulations, left-Gaussian pulse results in larger photocurrent in ChR2 and Chronos than square pulse of the same energy density. Time to peak photocurrent in each opsin is minimum at right-Gaussian pulse. The optimal pulse width to achieve peak photocurrent for non-square pulses is 10 ms for Chronos, and 50 ms for ChR2 and ChRmine. The pulse energy to evoke spike in hippocampal neurons can be minimized on choosing square pulse with Chronos, Gaussian pulse with ChR2, and positive-sinusoidal pulse with ChRmine. The results demonstrate that non-square waveforms generate more naturalistic spiking patterns compared to traditional square pulses. These findings provide valuable insights for the development of new optogenetic strategies to better simulate and manipulate neural activity patterns in the brain, with the potential to improve our understanding of cognitive processes and the treatment of neurological disorders.

Neural ensemble dynamics in trunk and hindlimb sensorimotor cortex encode for the control of postural stability

The cortex has a disputed role in monitoring postural equilibrium and intervening in cases of major postural disturbances. Here, we investigate the patterns of neural activity in the cortex that underlie neural dynamics during unexpected perturbations. In both the primary sensory (S1) and motor (M1) cortices of the rat, unique neuronal classes differentially covary their responses to distinguish different characteristics of applied postural perturbations; however, there is substantial information gain in M1, demonstrating a role for higher-order computations in motor control. A dynamical systems model of M1 activity and forces generated by the limbs reveals that these neuronal classes contribute to a low-dimensional manifold comprised of separate subspaces enabled by congruent and incongruent neural firing patterns that define different computations depending on the postural responses. These results inform how the cortex engages in postural control, directing work aiming to understand postural instability after neurological disease.

Foreground and Background in Mental Models.

Foreground and Background in Mental Models.

Short-term plasticity as a mechanism to regulate and retain multistability

Short-term synaptic plasticity of recurrent synapses has previously been shown to control and tune the balance of excitation and inhibition. Still, the precise mechanisms whereby short-term plasticity contributes to the coexistence and stabilization of neural firing are subtle and remain elusive. Here, we present a Wang–Buzsáki neural network to investigate the combined effect of short-term plasticity and recurrent inhibition on generating various firing patterns and dynamical retention. The resulting statistical analysis of both voltage trajectories and spike trains reveals two functional roles of short-term plasticity to neuronal dynamics. First, we find that recurrent inhibitory connections can be a possible mechanism to regulate neural firing at the given excitatory level. Secondly, we further find that neural networks of the excitatory population possess the ability to retain and stabilize various neural firing patterns with a broader range of excitation. Our findings pave the way to understanding the role of short-term plasticity in producing and maintaining multiple firing patterns.

Neural network based successor representations to form cognitive maps of space and language

How does the mind organize thoughts? The hippocampal-entorhinal complex is thought to support domain-general representation and processing of structural knowledge of arbitrary state, feature and concept spaces. In particular, it enables the formation of cognitive maps, and navigation on these maps, thereby broadly contributing to cognition. It has been proposed that the concept of multi-scale successor representations provides an explanation of the underlying computations performed by place and grid cells. Here, we present a neural network based approach to learn such representations, and its application to different scenarios: a spatial exploration task based on supervised learning, a spatial navigation task based on reinforcement learning, and a non-spatial task where linguistic constructions have to be inferred by observing sample sentences. In all scenarios, the neural network correctly learns and approximates the underlying structure by building successor representations. Furthermore, the resulting neural firing patterns are strikingly similar to experimentally observed place and grid cell firing patterns. We conclude that cognitive maps and neural network-based successor representations of structured knowledge provide a promising way to overcome some of the short comings of deep learning towards artificial general intelligence.

ID:15767 Differentiating Neural Activity Patterns in Deep Structures During Human Seizures

Epilepsy surgery efficacy is dependent on accurate seizure localization, but whether seizures are the result of networks or focal phenomenon is debated. Previous human and animal studies from our group have demonstrated dual seizure territories with contrasting neural firing patterns in limited data sets. In this study, we hypothesized that ictal neural activity markers would segregate into two contrasting patterns in limbic structures, with similar dynamics to neocortical structures.

In Materia Neuron Spiking Plasticity for Sequential Event Processing Based on Dual‐Mode Memristor

Artificial neurons are the fundamental elements in neuromorphic computing systems. Studies have revealed neuronal spike‐rate adaptation owing to intrinsic plasticity that neurons will adapt to the spiking patterns and store the events in the background spiking through clustered neuronal spiking. The event can be reactivated by specific retrieval clues instead of solely relying on synaptic plasticity. However, the neural adaptation, as well as the interactive adaptations of neuronal activity for information processing, have not been implemented. Herein, an artificial adaptive neuron via in materia modulation of the VO2/HfO2 based dual‐mode memristor is demonstrated. By changing the conductance of the HfO2 layer, the firing threshold can be modulated, thus the excitability and inhibition can be adjusted according to the previous stimuli without any complex peripherals, showing an adaptive firing rate even under the same stimuli. The artificial neuron clusters can emulate the concept of neuronal memory and neural adaptation, demonstrating spatiotemporal encoding capabilities via the correlated neural firing patterns. This conceptual work provides an alternative way to expand the computation power of spiking neural networks by exploiting the neural adaptation and could be enlightenment to maximize the synergy across both synapse and neuron in neuromorphic computing systems.

Subicular neurons represent multiple variables of a hippocampal-dependent task by using theta rhythm.

The subiculum is positioned at a critical juncture at the interface of the hippocampus with the rest of the brain. However, the exact roles of the subiculum in most hippocampal-dependent memory tasks remain largely unknown. One obstacle to make comparisons of neural firing patterns between the subiculum and hippocampus is the broad firing fields of the subicular cells. Here, we used spiking phases in relation to theta rhythm to parse the broad firing field of a subicular neuron into multiple subfields to find the unique functional contribution of the subiculum while male rats performed a hippocampal-dependent visual scene memory task. Some of the broad firing fields of the subicular neurons were successfully divided into multiple subfields similar to those in the CA1 by using the theta phase precession cycle. The new paradigm significantly improved the detection of task-relevant information in subicular cells without affecting the information content represented by CA1 cells. Notably, we found that multiple fields of a single subicular neuron, unlike those in the CA1, carried heterogeneous task-related information such as visual context and choice response. Our findings suggest that the subicular cells integrate multiple task-related factors by using theta rhythm to associate environmental context with action.

The influence of autapse on synchronous firing in small-world neural networks

The synchronization of the nervous system is strongly related to diseases such as Parkinson’s, epilepsy, and schizophrenia. Given that the existence of autapse has been proved experimentally, the influence of autapse on the synchronization in a neural network is studied numerically. The results show that increasing coupling intensity could destroy the synchronization of the neural firing pattern, and reduce the firing rate in the network. Especially, an inhibition zone, in which the neural firing is inhibited completely, exists for changes of both coupling intensity and time delay in all types of autapses. As a key factor for different types of autapses, the transmission time delay influences the synchronization complicatedly, i.e., increasing time delay could modulate synchronization for different types of autapse and parameter regions. The theoretical results in this paper shed some light on the study about the mechanism of neural synchronization.

Transition of Chimera States and Synchronization in Two-Layer Networks of Coupled Hindmarsh–Rose Neurons

The present work focuses on the existence of chimera states in two-layer networks of locally, nonlocally, and globally coupled Hindmarsh–Rose neurons. We show that chimera states occur in all three coupled neuronal networks by changing synaptic coupling strength and the number of coupled neighbors, and traveling chimera state exists in networks by using local and nonlocal couplings. Particularly, we find that in the nonlocally coupled network, there exist regions of coherent state, which can produce a wave such that the period becomes longer with the increase of the number of coupled neighbors. Interestingly, we observe a new chimera state with the coexistence of interlayer synchronous and asynchronous chimera states, which we named as the interlayer semi-synchronous chimera state. In addition, the results indicate that the interaction between layers in neuronal networks can induce different types of chimera states and firing patterns, which could be useful to control neural firing patterns and deepen the understanding of neuronal evolution where coherent and incoherent dynamics coexist.

Oscillation suppression effects of intermittent noisy deep brain stimulation induced by coordinated reset pattern based on a computational model

Noisy stimulation (NS) has been proposed to alleviate the pathological state in a computational model of Parkinson’s disease. Based on a resonance effect, NS modulates the neural firing pattern so as to suppress the excessive beta (12–35 Hz) synchronization and improve the relay reliability of the thalamus. However, in common with clinically-used pulsed high-frequency stimulation, the use of a continuous stimulation waveform for NS still may require an energy expenditure that is greater than necessary. A coordinated reset (CR) method to optimize NS is explored in this work that considers that the neural nature of the Parkinsonian condition is the enhancement of synchronization. CR methods has an advantage in disturbing the abnormal synchronous rhythm. Thesimulation resultsshow that coordinated reset noisy stimulation (CRNS) can further inhibit the enhanced pathological synchronization in a basal ganglia-thalamus network model with lower energy expenditure, as compared to basic NS. Moreover, two kinds of intermittent CRNS strategies are further derived by adding and adjusting the stimulation-off phases in the CRNS strategy. It is found that m:n ON-OFF CRNS strategies and delayed CRNS may further reduce the stimulation energy cost by 21.2% and 15.3% without compromising control performance, respectively. This work provides a new insight into the optimization of deep brain stimulation and may guide a new approach to treating Parkinson’s disease by optimizing the noise-induced improvement of the basal ganglia dysfunction.

Electrophysiological investigation of intact retina with soft printed organic neural interface

Objective. Understanding how the retina converts a natural image or an electrically stimulated one into neural firing patterns is the focus of on-going research activities. Ex vivo, the retina can be readily investigated using multi electrode arrays (MEAs). However, MEA recording and stimulation from an intact retina (in the eye) has been so far insufficient. Approach. In the present study, we report new soft carbon electrode arrays suitable for recording and stimulating neural activity in an intact retina. Screen-printing of carbon ink on 20 µm polyurethane (PU) film was used to realize electrode arrays with electrodes as small as 40 µm in diameter. Passivation was achieved with a holey membrane, realized using laser drilling in a thin (50 µm) PU film. Plasma polymerized 3.4-ethylenedioxythiophene was used to coat the electrode array to improve the electrode specific capacitance. Chick retinas, embryonic stage day 13, both explanted and intact inside an enucleated eye, were used. Main results. A novel fabrication process based on printed carbon electrodes was developed and yielded high capacitance electrodes on a soft substrate. Ex vivo electrical recording of retina activity with carbon electrodes is demonstrated. With the addition of organic photo-capacitors, simultaneous photo-electrical stimulation and electrical recording was achieved. Finally, electrical activity recordings from an intact chick retina (inside enucleated eyes) were demonstrated. Both photosensitive retinal ganglion cell responses and spontaneous retina waves were recorded and their features analyzed. Significance. Results of this study demonstrated soft electrode arrays with unique properties, suitable for simultaneous recording and photo-electrical stimulation of the retina at high fidelity. This novel electrode technology opens up new frontiers in the study of neural tissue in vivo.

The limbic memory circuit and the neural basis of contextual memory

The hippocampus, retrosplenial cortex and anterior thalamus are key components of a neural circuit known to be involved in a variety of memory functions, including spatial, contextual and episodic memory. In this review, we focus on the role of this circuit in contextual memory processes. The background environment, or context, is a powerful cue for memory retrieval, and neural representations of the context provide a mechanism for efficiently retrieving relevant memories while avoiding interference from memories that belong to other contexts. Data from experimental lesions and neural manipulation techniques indicate that each of these regions is critical for contextual memory. Neurophysiological evidence from the hippocampus and retrosplenial cortex suggest that contextual information is represented within this circuit by population-level neural firing patterns that reliably differentiate each context a subject encounters. These findings indicate that encoding contextual information to support context-dependent memory retrieval is a key function of this circuit.

Identification of SSRI-evoked antidepressant sensory signals by decoding vagus nerve activity

The vagus nerve relays mood-altering signals originating in the gut lumen to the brain. In mice, an intact vagus is required to mediate the behavioural effects of both intraluminally applied selective serotonin reuptake inhibitors and a strain of Lactobacillus with antidepressant-like activity. Similarly, the prodepressant effect of lipopolysaccharide is vagus nerve dependent. Single vagal fibres are broadly tuned to respond by excitation to both anti- and prodepressant agents, but it remains unclear how neural responses encode behaviour-specific information. Here we demonstrate using ex vivo experiments that for single vagal fibres within the mesenteric neurovascular bundle supplying the mouse small intestine, a unique neural firing pattern code is common to both chemical and bacterial vagus-dependent antidepressant luminal stimuli. This code is qualitatively and statistically discernible from that evoked by lipopolysaccharide, a non-vagus-dependent antidepressant or control non-antidepressant Lactobacillus strain and are not affected by sex status. We found that all vagus dependent antidepressants evoked a decrease in mean spike interval, increase in spike burst duration, decrease in gap duration between bursts and increase in intra-burst spike intervals. Our results offer a novel neuronal electrical perspective as one explanation for mechanisms of action of gut-derived vagal dependent antidepressants. We expect that our ex vivo individual vagal fibre recording model will improve the design and operation of new, extant electroceutical vagal stimulation devices currently used to treat major depression. Furthermore, use of this vagal antidepressant code should provide a valuable screening tool for novel potential oral antidepressant candidates in preclinical animal models.