Spiking Activity Of Neurons Research Articles

SpiLinC: Spiking Liquid-Ensemble Computing for Unsupervised Speech and Image Recognition.

In this work, we propose a Spiking Neural Network (SNN) consisting of input neurons sparsely connected by plastic synapses to a randomly interlinked liquid, referred to as Liquid-SNN, for unsupervised speech and image recognition. We adapt the strength of the synapses interconnecting the input and liquid using Spike Timing Dependent Plasticity (STDP), which enables the neurons to self-learn a general representation of unique classes of input patterns. The presented unsupervised learning methodology makes it possible to infer the class of a test input directly using the liquid neuronal spiking activity. This is in contrast to standard Liquid State Machines (LSMs) that have fixed synaptic connections between the input and liquid followed by a readout layer (trained in a supervised manner) to extract the liquid states and infer the class of the input patterns. Moreover, the utility of LSMs has primarily been demonstrated for speech recognition. We find that training such LSMs is challenging for complex pattern recognition tasks because of the information loss incurred by using fixed input to liquid synaptic connections. We show that our Liquid-SNN is capable of efficiently recognizing both speech and image patterns by learning the rich temporal information contained in the respective input patterns. However, the need to enlarge the liquid for improving the accuracy introduces scalability challenges and training inefficiencies. We propose SpiLinC that is composed of an ensemble of multiple liquids operating in parallel. We use a “divide and learn” strategy for SpiLinC, where each liquid is trained on a unique segment of the input patterns that causes the neurons to self-learn distinctive input features. SpiLinC effectively recognizes a test pattern by combining the spiking activity of the constituent liquids, each of which identifies characteristic input features. As a result, SpiLinC offers competitive classification accuracy compared to the Liquid-SNN with added sparsity in synaptic connectivity and faster training convergence, both of which lead to improved energy efficiency in neuromorphic hardware implementations. We validate the efficacy of the proposed Liquid-SNN and SpiLinC on the entire digit subset of the TI46 speech corpus and handwritten digits from the MNIST dataset.

Area-specific Modulation of Functional Cortical Activity During Block-based and Trial-based Proactive Inhibition

Animals can suppress their behavioral response in advance according to changes in environmental context (proactive inhibition: delaying the start of response), a process in which several cortical areas may participate. However, it remains unclear how this process is adaptively regulated according to contextual changes on different timescales. To address the issue, we used an improved stop-signal task paradigm to behaviorally and electrophysiologically characterize the temporal aspect of proactive inhibition in head-fixed rats. In the task, they must respond to a go cue as quickly as possible (go trial), but did not have to respond if a stop cue followed the go cue (stop trial). The task alternated between a block of only go trials (G-block) and a block of go-and-stop trials (GS-block). We observed block-based and trial-based proactive inhibition (emerging in GS-block and after stop trial, respectively) by behaviorally evaluating the delay in reaction time in correct go trials depending on contextual changes on different timescales. We electrophysiologically analyzed task-related neuronal activity in the primary and secondary motor, posterior parietal, and orbitofrontal cortices (M1, M2, PPC, and OFC, respectively). Under block-based proactive inhibition, spike activity of cue-preferring OFC neurons was attenuated continuously, while M1 and M2 activity was enhanced during motor preparation. Subsequently, M1 activity was attenuated during motor decision/execution. Under trial-based proactive inhibition, the OFC activity was continuously enhanced, and PPC and M1 activity was also enhanced shortly during motor decision/execution. These results suggest that different cortical mechanisms underlie the two types of proactive inhibition in rodents.

Structuring of Abstract Working Memory Content by Fronto-parietal Synchrony in Primate Cortex

How is neuronal activity across distant brain regions orchestrated to allow multiple stimuli to be stored together in working memory, yet maintained separate for individual readout and protection from distractors? Using paired recordings in the prefrontal and parietal cortex of monkeys discriminating numbers of items (numerosities), we found that working memory content is structured by frequency-specific oscillatory synchrony. Parieto-frontal signaling in the beta band carried information about the most recent numerical input. Fronto-parietal coupling in the theta band differentiated between multiple memorized numerosities. Task-relevant and distracting stimuli were nested in spiking activity of single prefrontal neurons, but could be separated by reading out spikes at distinct phases of parietal theta oscillations. The strength of phase-locked, cross-regional memory coding predicted task performance. Frequency-specific communication channels in the fronto-parietal network could enable serial bottom-up and parallel top-down information transmission, providing an important mechanism to protect working memory from interference.

Auditory motion does not modulate spiking activity in the middle temporal and medial superior temporal visual areas.

The integration of multiple sensory modalities is a key aspect of brain function, allowing animals to take advantage of concurrent sources of information to make more accurate perceptual judgments. For many years, multisensory integration in the cerebral cortex was deemed to occur only in high-level "polysensory" association areas. However, more recent studies have suggested that cross-modal stimulation can also influence neural activity in areas traditionally considered to be unimodal. In particular, several human neuroimaging studies have reported that extrastriate areas involved in visual motion perception are also activated by auditory motion, and may integrate audiovisual motion cues. However, the exact nature and extent of the effects of auditory motion on the visual cortex have not been studied at the single neuron level. We recorded the spiking activity of neurons in the middle temporal (MT) and medial superior temporal (MST) areas of anesthetized marmoset monkeys upon presentation of unimodal stimuli (moving auditory or visual patterns), as well as bimodal stimuli (concurrent audiovisual motion). Despite robust, direction selective responses to visual motion, none of the sampled neurons responded to auditory motion stimuli. Moreover, concurrent moving auditory stimuli had no significant effect on the ability of single MT and MST neurons, or populations of simultaneously recorded neurons, to discriminate the direction of motion of visual stimuli (moving random dot patterns with varying levels of motion noise). Our findings do not support the hypothesis that direct interactions between MT, MST and areas low in the hierarchy of auditory areas underlie audiovisual motion integration.

Calcium-activated chloride channels clamp odor-evoked spike activity in olfactory receptor neurons

The calcium-activated chloride channel anoctamin-2 (Ano2) is thought to amplify transduction currents in olfactory receptor neurons (ORNs), a hypothesis supported by previous studies in dissociated neurons from Ano2−/− mice. Paradoxically, despite a reduction in transduction currents in Ano2−/− ORNs, their spike output for odor stimuli may be higher. We examined the role of Ano2 in ORNs in their native environment in freely breathing mice by imaging activity in ORN axons as they arrive in the olfactory bulb glomeruli. Odor-evoked responses in ORN axons of Ano2−/− animals were consistently larger for a variety of odorants and concentrations. In an open arena, Ano2−/− animals took longer to approach a localized odor source than Ano2+/+ animals, revealing clear olfactory behavioral deficits. Our studies provide the first in vivo evidence toward an alternative or additional role for Ano2 in the olfactory transduction cascade, where it may serve as a feedback mechanism to clamp ORN spike output.

Statistical Characteristics of the Spike Activity of Neurons in the Midbrain Auditory Center in Frogs on Exposure to Tones Modulated by Low-Frequency Noise

The time parameters of the spike activity of single neurons in the brainstem part of the auditory system carry basic information on the properties of perceived signals. To characterize these parameters, the activity of single neurons in the midbrain center (semicircular torus) of the common frog (Rana temporaria) was studied on exposure to prolonged tones amplitude-modulated with repeating bursts of low-frequency noise. Time-shuffled autocorrelation functions were calculated, these corresponding to the sum of the correlation functions between responses to all bursts presented with the exception of responses evoked by one of the bursts. This approach excludes the effects of the refractory properties of the neuron being studied, allowing the responses of a single neuron to be used to evaluate the nature of the spike activity in the local population of cells with presumptively identical properties but which are statistically independent of each other. Significant variation in the statistical characteristics of output spike activity in different cells in the semicircular toroid was found, such that signals could be characterized in terms of different time properties. Neurons with highly specific reactions to the time characteristics of the envelope were observed.

An Information-Theoretic Measure of Multiscale Causality for Spike-Field Activity.

Simultaneous recordings of spikes and fields could enable analyses of functional connectivity in the brain at multiple spatiotemporal scales. However, these analyses require developing novel methods to assess causality between binary-valued spikes and continuous-valued fields, which have fundamentally different statistical profiles and time-scales. Thus classical measures of causality cannot be directly applied in multiscale networks. We develop a novel parametric method to assess causality for multiscale spike-field activities by computing directed information. Directed information is an information theoretic measure of causality but is in general hard to estimate. Our method estimates the causality in two steps. First, we construct point process generalized linear models (GLM) for each neuron's spiking activity to estimate its firing rate using the history of both spikes and fields and compute the directed information to spike nodes from any node. Second, we construct regression models for fields using the history of the estimated firing rates and the history of fields, and then compute the directed information to each field node from any node. In both steps, we estimate model parameters using maximum likelihood and devise statistical tests to assess the significance of the causality. Using simulated data from basic three-node structures and a ten-node network, we show that our method can asymptotically identify the true causality. This method could help uncover functional connectivity in the brain at multiple spatiotemporal scales.

Long-term recording performance and biocompatibility of chronically implanted cylindrically-shaped, polymer-based neural interfaces.

Stereo-electroencephalography depth electrodes, regularly implanted into drug-resistant patients with focal epilepsy to localize the epileptic focus, have a low channel count (6-12 macro- or microelectrodes), limited spatial resolution (0.5-1 cm) and large contact area of the recording sites (~mm2). Thus, they are not suited for high-density local field potential and multiunit recordings. In this paper, we evaluated the long-term electrophysiological recording performance and histocompatibility of a neural interface consisting of 32 microelectrodes providing a physical shape similar to clinical devices. The cylindrically-shaped depth probes made of polyimide (PI) were chronically implanted for 13 weeks into the brain of rats, while cortical or thalamic activity (local field potentials, single-unit and multi-unit activity) was recorded regularly to monitor the temporal change of several features of the electrophysiological performance. To examine the tissue reaction around the probe, neuron-selective and astroglia-selective immunostaining methods were applied. Stable single-unit and multi-unit activity were recorded for several weeks with the implanted depth probes and a weak or moderate tissue reaction was found around the probe track. Our data on biocompatibility presented here and in vivo experiments in non-human primates provide a strong indication that this type of neural probe can be applied in stereo-electroencephalography recordings of up to 2 weeks in humans targeting the localization of epileptic foci providing an increased spatial resolution and the ability to monitor local field potentials and neuronal spiking activity.

Spiking networks as efficient distributed controllers

In the brain, networks of neurons produce activity that is decoded into perceptions and actions. How the dynamics of neural networks support this decoding is a major scientific question. That is, while we understand the basic mechanisms by which neurons produce activity in the form of spikes, whether these dynamics reflect an overlying functional objective is not understood. In this paper, we examine neuronal dynamics from a first-principles control-theoretic viewpoint. Specifically, we postulate an objective wherein neuronal spiking activity is decoded into a control signal that subsequently drives a linear system. Then, using a recently proposed principle from theoretical neuroscience, we optimize the production of spikes so that the linear system in question achieves reference tracking. It turns out that such optimization leads to a recurrent network architecture wherein each neuron possess integrative dynamics. The network amounts to an efficient, distributed event-based controller where each neuron (node) produces a spike if doing so improves tracking performance. Moreover, the dynamics provide inherent robustness properties, so that if some neurons fail, others will compensate by increasing their activity so that the tracking objective is met.

Auditory modulation of spiking activity and local field potentials in area MT does not appear to underlie an audiovisual temporal illusion.

The timing of brief stationary sounds has been shown to alter the perceived speed of visual apparent motion (AM), presumably by altering the perceived timing of the individual frames of the AM stimuli and/or the duration of the interstimulus intervals (ISIs) between those frames. To investigate the neural correlates of this "temporal ventriloquism" illusion, we recorded spiking and local field potential (LFP) activity from the middle temporal area (area MT) in awake, fixating macaques. We found that the spiking activity of most MT neurons (but not the LFP) was tuned for the ISI/speed (these parameters covaried) of our AM stimuli but that auditory timing had no effect on that tuning. We next asked whether the predicted changes in perceived timing were reflected in the timing of neuronal responses to the individual frames of the AM stimuli. Although spiking dynamics were significantly, if weakly, affected by auditory timing in a minority of neurons, the timing of spike responses did not systematically mirror the predicted perception of stimuli. Conversely, the duration of LFP responses in β- and γ-frequency bands was qualitatively consistent with human perceptual reports. We discovered, however, that LFP responses to auditory stimuli presented alone were robust and that responses to audiovisual stimuli were predicted by the linear sum of responses to auditory and visual stimuli presented individually. In conclusion, we find evidence of auditory input into area MT but not of the nonlinear audiovisual interactions we had hypothesized to underlie the illusion. NEW & NOTEWORTHY We utilized a set of audiovisual stimuli that elicit an illusion demonstrating "temporal ventriloquism" in visual motion and that have spatiotemporal intervals for which neurons within the middle temporal area are selective. We found evidence of auditory input into the middle temporal area but not of the nonlinear audiovisual interactions underlying this illusion. Our findings suggest that either the illusion was absent in our nonhuman primate subjects or the neuronal correlates of this illusion lie within other areas.

Information Processing in Neuron with Exponential Distributed Delay

Artificial intelligence (AI) has been become the primary need in nearly all sectors namely engineering, services, banking, finance, defense, space etc [3], [33]. Artificial intelligence in these sectors can be implemented in two ways: (i) hardware level implementation (ii) software level implementation. Both kinds of AI implementation require neuron models which mimic the minimal set of real neuron functionality. To this end, Leaky Integrate-and-Fire (LIF) model is performing as the backbone for both kinds of AI implementation. At hardware level implementation, it’s a variant, called as neuristors, is used at chip level implementation, whereas a number of variants LIF model are used to implement AI at software level. In this work, the extended LIF model in distributed delay kernel regime is analyzed. The impact of exponentially distributed delay (EDD) memory kernel on spiking activity and steady state membrane potential distribution (SVD) of LIF neuron is investigated. Fokker-Planck equation associated with the considered model is solved to investigate SVD of the neuron in sub-threshold regime, which results Gaussian distribution. In order to study the information processing, spiking activity of the model is investigated, which is further extended to neuronal rate-code scheme. These finding have been compared with simple LIF model with stochastic input. It is evident that steady state membrane potential distribution of the LIF neuron is invariant due to the presence of EDD. Such kinds of neuron models are useful to implement artificial neural networks. To this end, the proposed model can used to implement recurrent neural networks (RNN) with comparatively more accuracy. Similarly, this model can also be investigated in term of chip level implementation of AI.

Local field potential decoding of the onset and intensity of acute pain in rats

Pain is a complex sensory and affective experience. The current definition for pain relies on verbal reports in clinical settings and behavioral assays in animal models. These definitions can be subjective and do not take into consideration signals in the neural system. Local field potentials (LFPs) represent summed electrical currents from multiple neurons in a defined brain area. Although single neuronal spike activity has been shown to modulate the acute pain, it is not yet clear how ensemble activities in the form of LFPs can be used to decode the precise timing and intensity of pain. The anterior cingulate cortex (ACC) is known to play a role in the affective-aversive component of pain in human and animal studies. Few studies, however, have examined how neural activities in the ACC can be used to interpret or predict acute noxious inputs. Here, we recorded in vivo extracellular activity in the ACC from freely behaving rats after stimulus with non-noxious, low-intensity noxious, and high-intensity noxious stimuli, both in the absence and chronic pain. Using a supervised machine learning classifier with selected LFP features, we predicted the intensity and the onset of acute nociceptive signals with high degree of precision. These results suggest the potential to use LFPs to decode acute pain.

Topological Schemas of Memory Spaces.

Hippocampal cognitive map—a neuronal representation of the spatial environment—is widely discussed in the computational neuroscience literature for decades. However, more recent studies point out that hippocampus plays a major role in producing yet another cognitive framework—the memory space—that incorporates not only spatial, but also non-spatial memories. Unlike the cognitive maps, the memory spaces, broadly understood as “networks of interconnections among the representations of events,” have not yet been studied from a theoretical perspective. Here we propose a mathematical approach that allows modeling memory spaces constructively, as epiphenomena of neuronal spiking activity and thus to interlink several important notions of cognitive neurophysiology. First, we suggest that memory spaces have a topological nature—a hypothesis that allows treating both spatial and non-spatial aspects of hippocampal function on equal footing. We then model the hippocampal memory spaces in different environments and demonstrate that the resulting constructions naturally incorporate the corresponding cognitive maps and provide a wider context for interpreting spatial information. Lastly, we propose a formal description of the memory consolidation process that connects memory spaces to the Morris' cognitive schemas-heuristic representations of the acquired memories, used to explain the dynamics of learning and memory consolidation in a given environment. The proposed approach allows evaluating these constructs as the most compact representations of the memory space's structure.

Taste sensitivity and divergence in host plant acceptance between adult females and larvae of Papilio hospiton.

On the island of Sardinia the lepidopteran Papilio hospiton uses Ferula communis as exclusive host plant. However, on the small island of Tavolara, adult females lay eggs on Seseli tortuosum, a plant confined to the island. When raised in captivity on Seseli only few larvae grew beyond the first-second instar. Host specificity of lepidopterans is determined by female oviposition preferences, but also by larval food acceptance, and adult and larval taste sensitivity may be related to host selection in both cases. Aim of this work was: (i) to study the taste sensitivity of larvae and ovipositing females to saps of Ferula and Seseli; (ii) to cross-compare the spike activity of gustatory receptor neurons (GRNs) to both taste stimuli; (iii) to evaluate the discriminating capability between the two saps and determine which neural code/s is/are used. The results show that: (i) the spike responses of the tarsal GRNs of adult females to both plant saps are not different and therefore they cannot discriminate the two plants; (ii) larval L-lat GRN shows a higher activity in response to Seseli than Ferula, while the opposite occurs for the phagostimulant neurons, and larvae may discriminate between the two saps by means of multiple neural codes; (iii) the number of eggs laid on the two plants is the same, but the larval growth performance is better on Ferula than Seseli. Taste sensitivity differences may explain the absence of a positive relationship between oviposition preferences by adult females and plant acceptance and growth performance by larvae.

27.3 DISCRETE AND COORDINATED ENCODING OF REWARDED ACTIONS BY PREFRONTAL CORTEX AND DOPAMINE NEURONS

BackgroundCo-morbidity of schizophrenia and drug use has been attribute to common pathophysiology of mesocortical circuit. We modeled a behavioral disruption to this circuit in rodents by using a task where actions were consistently rewarded but probabilistically punished. Our data reveal dynamic coding schemes of the VTA-mPFC neural circuit in representing risk of punishment and punishment-based modulation of rewarded actions.MethodsSpike activity and local field potentials were recorded during simultaneously from ventral tegmental area and medial prefrontal cortex (PFC), two reciprocally connected mesocortical regions, in rodents as they performed a task where actions were consistently rewarded but probabilistically punished. This model allowed us to reveal dynamic coding schemes of the VTA-mPFC neural circuit in representing risk of punishment and punishment-based modulation of rewarded actions.ResultsAt the single unit level (n=167 mPFC n=102 VTA units), we found that ensembles of VTA and mPFC neurons encode the contingency between action and punishment. At the network level, we found that coherent theta oscillations synchronize the VTA and mPFC in a bottom-up direction, effectively phase-modulating the neuronal spike activity in the two regions during punishment-free actions. This synchrony declined as a function of punishment contingencyDiscussionDuring reward-seeking actions, risk of an aversive outcome and anxiety disrupts dopamine neuron-driven synchrony between PFC and VTA

An integrate-and-fire model to generate spike trains with long-range dependence.

Long-range dependence (LRD) has been observed in a variety of phenomena in nature, and for several years also in the spiking activity of neurons. Often, this is interpreted as originating from a non-Markovian system. Here we show that a purely Markovian integrate-and-fire (IF) model, with a noisy slow adaptation term, can generate interspike intervals (ISIs) that appear as having LRD. However a proper analysis shows that this is not the case asymptotically. For comparison, we also consider a new model of individual IF neuron with fractional (non-Markovian) noise. The correlations of its spike trains are studied and proven to have LRD, unlike classical IF models. On the other hand, to correctly measure long-range dependence, it is usually necessary to know if the data are stationary. Thus, a methodology to evaluate stationarity of the ISIs is presented and applied to the various IF models. We explain that Markovian IF models may seem to have LRD because of non-stationarities.

Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks

Network oscillations across and within brain areas are critical for learning and performance of memory tasks. While a large amount of work has focused on the generation of neural oscillations, their effect on neuronal populations' spiking activity and information encoding is less known. Here, we use computational modeling to demonstrate that a shift in resonance responses can interact with oscillating input to ensure that networks of neurons properly encode new information represented in external inputs to the weights of recurrent synaptic connections. Using a neuronal network model, we find that due to an input current-dependent shift in their resonance response, individual neurons in a network will arrange their phases of firing to represent varying strengths of their respective inputs. As networks encode information, neurons fire more synchronously, and this effect limits the extent to which further "learning" (in the form of changes in synaptic strength) can occur. We also demonstrate that sequential patterns of neuronal firing can be accurately stored in the network; these sequences are later reproduced without external input (in the context of subthreshold oscillations) in both the forward and reverse directions (as has been observed following learning in vivo). To test whether a similar mechanism could act in vivo, we show that periodic stimulation of hippocampal neurons coordinates network activity and functional connectivity in a frequency-dependent manner. We conclude that resonance with subthreshold oscillations provides a plausible network-level mechanism to accurately encode and retrieve information without overstrengthening connections between neurons.

Bilateral Tactile Input Patterns Decoded at Comparable Levels But Different Time Scales in Neocortical Neurons.

The presence of contralateral tactile input can profoundly affect ipsilateral tactile perception, and unilateral stroke in somatosensory areas can result in bilateral tactile deficits, suggesting that bilateral tactile integration is an important part of brain function. Although previous studies have shown that bilateral tactile inputs exist and that there are neural interactions between inputs from the two sides, no previous study explored to what extent the local neuronal circuitry processing contains detailed information about the nature of the tactile input from the two sides. To address this question, we used a recently introduced approach to deliver a set of electrical, reproducible, tactile afferent, spatiotemporal activation patterns, which permits a high-resolution analysis of the neuronal decoding capacity, to the skin of the second forepaw digits of the anesthetized male rat. Surprisingly, we found that individual neurons of the primary somatosensory can decode contralateral and ipsilateral input patterns to comparable extents. Although the contralateral input was stronger and more rapidly decoded, given sufficient poststimulus processing time, ipsilateral decoding levels essentially caught up to contralateral levels. Moreover, there was a weak but significant correlation for neurons with high decoding performance for contralateral tactile input to also perform well on decoding ipsilateral input. Our findings shed new light on the brain mechanisms underlying bimanual haptic integration.SIGNIFICANCE STATEMENT Here we demonstrate that the spiking activity of single neocortical neurons in the somatosensory cortex of the rat can be used to decode patterned tactile stimuli delivered to the distal ventral skin of the second forepaw digits on both sides of the body. Even though comparable levels of decoding of the tactile input were achieved faster for contralateral input, given sufficient integration time each neuron was found to decode ipsilateral input with a comparable level of accuracy. Given that the neocortical neurons could decode ipsilateral inputs with such small differences between the patterns suggests that S1 cortex has access to very precise information about ipsilateral events. The findings shed new light on possible network mechanisms underlying bimanual haptic processing.

Coordinated Ramping of Dorsal Striatal Pathways preceding Food Approach and Consumption.

The striatum controls food-related actions and consumption and is linked to feeding disorders, including obesity and anorexia nervosa. Two populations of neurons project from the striatum: direct pathway medium spiny neurons and indirect pathway medium spiny neurons. The selective contribution of direct pathway medium spiny neurons and indirect pathway medium spiny neurons to food-related actions and consumption remains unknown. Here, we used in vivo electrophysiology and fiber photometry in mice (of both sexes) to record both spiking activity and pathway-specific calcium activity of dorsal striatal neurons during approach to and consumption of food pellets. While electrophysiology revealed complex task-related dynamics across neurons, population calcium was enhanced during approach and inhibited during consumption in both pathways. We also observed ramping changes in activity that preceded both pellet-directed actions and spontaneous movements. These signals were heterogeneous in the spiking units, with neurons exhibiting either increasing or decreasing ramps. In contrast, the population calcium signals were homogeneous, with both pathways having increasing ramps of activity for several seconds before actions were initiated. An analysis comparing population firing rates to population calcium signals also revealed stronger ramping dynamics in the calcium signals than in the spiking data. In a second experiment, we trained the mice to perform an action sequence to evaluate when the ramping signals terminated. We found that the ramping signals terminated at the beginning of the action sequence, suggesting they may reflect upcoming actions and not preconsumption activity. Plasticity of such mechanisms may underlie disorders that alter action selection, such as drug addiction or obesity.SIGNIFICANCE STATEMENT Alterations in striatal function have been linked to pathological consumption in disorders, such as obesity and drug addiction. We recorded spiking and population calcium activity from the dorsal striatum during ad libitum feeding and an operant task that resulted in mice obtaining food pellets. Dorsal striatal neurons exhibited long ramps in activity that preceded actions by several seconds, and may reflect upcoming actions. Understanding how the striatum controls the preparation and generation of actions may lead to improved therapies for disorders, such as drug addiction or obesity.

Control of sampling rate in map-based models of spiking neurons

The discrete-time (map-based) approach to modeling nonlinear dynamics of spiking activity in neurons enables highly efficient numerical simulations for capturing realistic neurobiological behavior by utilizing a large time interval between computed states (samples) of neuron activity. The design and parameter tuning of these models assumes a fixed and preset sampling rate. When change of the time step is needed, it requires revisiting stages of the model design and parameter tuning. This paper presents an approach to the design of map-models in a new form where time step is added as a control parameter and can be easily changed to vary the time scale of the model behavior, i.e. sampling rate, essentially preserving the model behavior. It also discusses modification of the noise generator models needed to support simulation of map-based neurons with the modified sampling rate. The effects caused by direct control of time scale on model dynamics and limitations of this approach are discussed.