Spatiotemporal Firing Patterns Research Articles

Electrical coupling regulated by GABAergic nucleo-olivary afferent fibres facilitates cerebellar sensory–motor adaptation

The inferior olivary (IO) nucleus makes up the signal gateway for several organs to the cerebellar cortex. Located within the sensory–motor-cerebellum pathway, the IO axons, i.e., climbing fibres (CFs), massively synapse onto the cerebellar Purkinje cells (PCs) regulating motor learning whilst the olivary nucleus receives negative feedback through the GABAergic nucleo-olivary​ (NO) pathway. The NO pathway regulates the electrical coupling (EC) amongst the olivary cells thus facilitating synchrony and timing. However, the involvement of this EC regulation on cerebellar adaptive behaviour is still under debate. In our study we have used a spiking cerebellar model to assess the role of the NO pathway in regulating vestibulo-ocular-reflex (VOR) adaptation. The model incorporates spike-based synaptic plasticity at multiple cerebellar sites and an electrically-coupled olivary system. The olivary system plays a central role in regulating the CF spike-firing patterns that drive the PCs, whose axons ultimately shape the cerebellar output. Our results suggest that a systematic GABAergic NO deactivation decreases the spatio-temporal complexity of the IO firing patterns thereby worsening the temporal resolution of the olivary system. Conversely, properly coded IO spatio-temporal firing patterns, thanks to NO modulation, finely shape the balance between long-term depression and potentiation, which optimises VOR adaptation. Significantly, the NO connectivity pattern constrained to the same micro-zone helps maintain the spatio-temporal complexity of the IO firing patterns through time. Moreover, the temporal alignment between the latencies found in the NO fibres and the sensory–motor pathway delay appears to be crucial for facilitating the VOR. When we consider all the above points we believe that these results predict that the NO pathway is instrumental in modulating the olivary coupling and relevant to VOR adaptation.

Cortical Ripples during NREM Sleep and Waking in Humans.

Hippocampal ripples index the reconstruction of spatiotemporal neuronal firing patterns essential for the consolidation of memories in the cortex during non-rapid eye movement sleep (NREM). Recently, cortical ripples in humans have been shown to enfold the replay of neuron firing patterns during cued recall. Here, using intracranial recordings from 18 patients (12 female), we show that cortical ripples also occur during NREM in humans, with similar density, oscillation frequency (∼90 Hz), duration, and amplitude to waking. Ripples occurred in all cortical regions with similar characteristics, unrelated to putative hippocampal connectivity, and were less dense and robust in higher association areas. Putative pyramidal and interneuron spiking phase-locked to cortical ripples during NREM, with phase delays consistent with ripple generation through pyramidal-interneuron feedback. Cortical ripples were smaller in amplitude than hippocampal ripples but were similar in density, frequency, and duration. Cortical ripples during NREM typically occurred just before the upstate peak, often during spindles. Upstates and spindles have previously been associated with memory consolidation, and we found that cortical ripples grouped cofiring between units within the window of spike timing-dependent plasticity. Thus, human NREM cortical ripples are as follows: ubiquitous and stereotyped with a tightly focused oscillation frequency; similar to hippocampal ripples; associated with upstates and spindles; and associated with unit cofiring. These properties are consistent with cortical ripples possibly contributing to memory consolidation and other functions during NREM in humans.SIGNIFICANCE STATEMENT In rodents, hippocampal ripples organize replay during sleep to promote memory consolidation in the cortex, where ripples also occur. However, evidence for cortical ripples in human sleep is limited, and their anatomic distribution and physiological properties are unexplored. Here, using human intracranial recordings, we demonstrate that ripples occur throughout the cortex during waking and sleep with highly stereotyped characteristics. During sleep, cortical ripples tend to occur during spindles on the down-to-upstate transition, and thus participate in a sequence of sleep waves that is important for consolidation. Furthermore, cortical ripples organize single-unit spiking with timing optimal to facilitate plasticity. Therefore, cortical ripples in humans possess essential physiological properties to support memory and other cognitive functions.

Basal and Stress-Induced Network Activity in the Adrenal Medulla In Vivo.

The adrenal medulla plays a critical role in mammalian homeostasis and the stress response. It is populated by clustered chromaffin cells that secrete epinephrine or norepinephrine along with peptides into the bloodstream affecting distant target organs. Despite been heavily studied, the central control of adrenal medulla and in-situ spatiotemporal responsiveness remains poorly understood. For this work, we continuously monitored the electrical activity of individual adrenomedullary chromaffin cells in the living anesthetized rat using multielectrode arrays. We measured the chromaffin cell activity under basal and physiological stress conditions and characterized the functional micro-architecture of the adrenal medulla. Under basal conditions, chromaffin cells fired action potentials with frequencies between ~0.2 and 4 Hz. Activity was almost completely driven by sympathetic inputs coming through the splanchnic nerve. Chromaffin cells were organized into independent local networks in which cells fired in a specific order, with latencies from hundreds of microseconds to a few milliseconds. Electrical stimulation of the splanchnic nerve evoked almost exactly the same spatiotemporal firing patterns that occurred spontaneously. Hypoglycemic stress, induced by insulin administration resulted in increased activity of a subset of the chromaffin cells. In contrast, respiratory arrest induced by lethal anesthesia resulted in an increase in the activity of virtually all chromaffin cells before cessation of all activity. These results suggest a stressor-specific activation of adrenomedullary chromaffin cell networks and revealed a surprisingly complex electrical organization that likely reflects the dynamic nature of the adrenal medulla’s neuroendocrine output during basal conditions and during different types of physiological stress.

An in silico model for determining the influence of neuronal co-activity on rodent spatial behavior

BackgroundNeuropsychological and neurophysiological analyses focus on understanding how neuronal activity and co-activity predict behavior. Experimental techniques allow for modulation of neuronal activity, but do not control neuronal ensemble spatiotemporal firing patterns, and there are few, if any, sophisticated in silico techniques which accurately reconstruct physiological neural spike trains and behavior using unit co-activity as an input parameter. New MethodOur approach to simulation of neuronal spike trains is based on using state space modeling to estimate a weighted graph of interaction strengths between pairs of neurons along with separate estimations of spiking threshold voltage and neuronal membrane leakage. These parameters allow us to tune a biophysical model which is then employed to accurately reconstruct spike trains from freely behaving animals and then use these spike trains to estimate an animal’s spatial behavior. The reconstructed spatial behavior allows us to confirm the same information is present in both the recorded and simulated spike trains. ResultsOur method reconstructs spike trains (98 ± 0.0013% like original spike trains, mean ± SEM) and animal position (9.468 ± 0.240 cm, mean ± SEM) with high fidelity. Comparison with Existing Method(s)To our knowledge, this is the first method that uses empirically derived network connectivity to constrain biophysical parameters and predict spatial behavior. Together, these methods allow in silico quantification of the contribution of specific unit activity and co-activity to animal spatial behavior. ConclusionsOur novel approach provides a flexible, robust in silico technique for determining the contribution of specific neuronal activity and co-activity to spatial behavior.

Mechano-neurophysiological model of the fingertip for spatiotemporal firing pattern prediction of SA1 mechanoreceptors during embossed letter scanning

Numerical simulation can be used to observe spatiotemporal firing responses of tactile receptors during dynamic tactile exploration, and it provides a more understanding of the mechanism of tactile perception. In this study, we developed an improved mechano-neurophysiological model of the fingertip that employs a realistic fingertip structure and accurate contact mechanics while scanning embossed letters. To confirm the potential of the model, we simulated the spatiotemporal firing patterns of slowly adapting type-1 (SA1) mechanoreceptors while scanning the embossed letter “G” and compared the simulation result with the existing experimental data in neurophysiology. Although the experimental data were reconstructed from a single nerve fiber, the simulation simultaneously observed the responses of multiple SA1 receptors, which resulted in a more obscure “G” spatiotemporal firing pattern than that in the previous experiment. This result supports existing data from another psychophysical experiment that demonstrates that it is harder to recognize embossed letter “G” accurately during letter scanning. This finding suggests that the spatiotemporal firing pattern from multiple SA1 receptors may show an obscure “G” pattern while scanning the embossed letter “G”.

Unsupervised learning of temporal features for word categorization in a spiking neural network model of the auditory brain.

The nature of the code used in the auditory cortex to represent complex auditory stimuli, such as naturally spoken words, remains a matter of debate. Here we argue that such representations are encoded by stable spatio-temporal patterns of firing within cell assemblies known as polychronous groups, or PGs. We develop a physiologically grounded, unsupervised spiking neural network model of the auditory brain with local, biologically realistic, spike-time dependent plasticity (STDP) learning, and show that the plastic cortical layers of the network develop PGs which convey substantially more information about the speaker independent identity of two naturally spoken word stimuli than does rate encoding that ignores the precise spike timings. We furthermore demonstrate that such informative PGs can only develop if the input spatio-temporal spike patterns to the plastic cortical areas of the model are relatively stable.

Cell Type-Specific Differences in Spike Timing and Spike Shape in the Rat Parasubiculum and Superficial Medial Entorhinal Cortex

SummaryThe medial entorhinal cortex (MEC) and the adjacent parasubiculum are known for their elaborate spatial discharges (grid cells, border cells, etc.) and the precessing of spikes relative to the local field potential. We know little, however, about how spatio-temporal firing patterns map onto cell types. We find that cell type is a major determinant of spatio-temporal discharge properties. Parasubicular neurons and MEC layer 2 (L2) pyramids have shorter spikes, discharge spikes in bursts, and are theta-modulated (rhythmic, locking, skipping), but spikes phase-precess only weakly. MEC L2 stellates and layer 3 (L3) neurons have longer spikes, do not discharge in bursts, and are weakly theta-modulated (non-rhythmic, weakly locking, rarely skipping), but spikes steeply phase-precess. The similarities between MEC L3 neurons and MEC L2 stellates on one hand and parasubicular neurons and MEC L2 pyramids on the other hand suggest two distinct streams of temporal coding in the parahippocampal cortex.

A computational model of cell culture dynamics: the role of connectivity and synaptic receptors in the appearance of synchronized bursting events

How an ensemble of neurons wires together to form a functional unit is a fundamental problem in neuroscience. The architecture of neuronal wiring, in fact, determines how neurons communicate and may be important for information processing performed by neuronal networks. However current knowledge is mainly limited to networks consisting of a small number of neurons, while the topological structure of biological networks remains still unknown. Primary neuronal cultures represent an ideal preparation to investigate the basic principles of network dynamics. At the mature stage, they display network-wide synchronous bursting events (SBEs) sharing similar spatiotemporal patterns of firing [1]. Interestingly, high-density MEA recordings have shown that SBEs actually correspond to propagating activities through the network. Typically, the simulated SBEs originated from a few and specific sites as in experiments [2], but the nature and the role of such events is still under debate. In order to investigate the determinants of such dynamics, we developed a computational model that mimics the main features of the recordings obtained by a high density multi-electrode-array device (4096 electrodes inter-spaced by 20um, [3]). With only a few topological constraints, the model expressed realistic SBEs along time that can be well clustered into only a few groups differing for their ignition sites and propagation directions, similarly to what it is observed experimentally. Furthermore, we used the model together with experimental datasets to investigate the effects of synaptic blockers of the AMPA, NMDA and GABA synapses on the network activity. In particular, we showed that NMDA receptors can be among the principal mechanisms involved in triggering a sequence of SBEs, a firing regime that is typically observed in for mature neuronal cultures. Such regime is characterized by a principal SBE recruiting a great percentage of neurons and followed by a sequence of several weaker SBEs interleaved by hundreds of milliseconds. Altogether, the results obtained with our neural network computational model show that this model can replicate most of the salient firing properties observed experimentally in cultured neuronal networks and that it can serve for exploring the properties of signals and responses observed in neuronal networks properties.

Improved discriminability of spatiotemporal neural patterns in rat motor cortical areas as directional choice learning progresses.

Animals learn to choose a proper action among alternatives to improve their odds of success in food foraging and other activities critical for survival. Through trial-and-error, they learn correct associations between their choices and external stimuli. While a neural network that underlies such learning process has been identified at a high level, it is still unclear how individual neurons and a neural ensemble adapt as learning progresses. In this study, we monitored the activity of single units in the rat medial and lateral agranular (AGm and AGl, respectively) areas as rats learned to make a left or right side lever press in response to a left or right side light cue. We noticed that rat movement parameters during the performance of the directional choice task quickly became stereotyped during the first 2–3 days or sessions. But learning the directional choice problem took weeks to occur. Accompanying rats' behavioral performance adaptation, we observed neural modulation by directional choice in recorded single units. Our analysis shows that ensemble mean firing rates in the cue-on period did not change significantly as learning progressed, and the ensemble mean rate difference between left and right side choices did not show a clear trend of change either. However, the spatiotemporal firing patterns of the neural ensemble exhibited improved discriminability between the two directional choices through learning. These results suggest a spatiotemporal neural coding scheme in a motor cortical neural ensemble that may be responsible for and contributing to learning the directional choice task.

Detection of 5-hydroxytryptamine (5-HT) in vitro using a hippocampal neuronal network-based biosensor with extracellular potential analysis of neurons

5-hydroxytryptamine (5-HT) is an important neurotransmitter in regulating emotions and related behaviors in mammals. To detect and monitor the 5-HT, effective and convenient methods are demanded in investigation of neuronal network. In this study, hippocampal neuronal networks (HNNs) endogenously expressing 5-HT receptors were employed as sensing elements to build an in vitro neuronal network-based biosensor. The electrophysiological characteristics were analyzed in both neuron and network levels. The firing rates and amplitudes were derived from signal to determine the biosensor response characteristics. The experimental results demonstrate a dose-dependent inhibitory effect of 5-HT on hippocampal neuron activities, indicating the effectiveness of this hybrid biosensor in detecting 5-HT with a response range from 0.01μmol/L to 10μmol/L. In addition, the cross-correlation analysis of HNNs activities suggests 5-HT could weaken HNN connectivity reversibly, providing more specificity of this biosensor in detecting 5-HT. Moreover, 5-HT induced spatiotemporal firing pattern alterations could be monitored in neuron and network levels simultaneously by this hybrid biosensor in a convenient and direct way. With those merits, this neuronal network-based biosensor will be promising to be a valuable and utility platform for the study of neurotransmitter in vitro.

Interneuron firing precedes sequential activation of neuronal ensembles in hippocampal slices

Neuronal firing sequences that occur during behavioral tasks are precisely reactivated in the neocortex and the hippocampus during rest and sleep. These precise firing sequences are likely to reflect latent memory traces, and their reactivation is believed to be essential for memory consolidation and working memory maintenance. However, how the organized repeating patterns emerge through the coordinated interplay of distinct types of neurons remains unclear. In this study, we monitored ongoing spatiotemporal firing patterns using a multi-neuron calcium imaging technique and examined how the activity of individual neurons is associated with repeated ensembles in hippocampal slice cultures. To determine the cell types of the imaged neurons, we applied an optical synapse mapping method that identifies network connectivity among dozens of neurons. We observed that inhibitory interneurons exhibited an increase in their firing rates prior to the onset of repeating sequences, while the overall activity level of excitatory neurons remained unchanged. A specific repeating sequence emerged preferentially after the firing of a specific interneuron that was located close to the neuron first activated in the sequence. The times of repeating sequences could be more precisely predicted based on the activity patterns of inhibitory cells than excitatory cells. In line with these observations, stimulation of a single interneuron could trigger the emergence of repeating sequences. These findings provide a conceptual framework that interneurons serve as a key regulator of initiating sequential spike activity.

Facilitation of memory encoding in primate hippocampus by a neuroprosthesis that promotes task-specific neural firing

Objective. Memory accuracy is a major problem in human disease and is the primary factor that defines Alzheimer’s, ageing and dementia resulting from impaired hippocampal function in the medial temporal lobe. Development of a hippocampal memory neuroprosthesis that facilitates normal memory encoding in nonhuman primates (NHPs) could provide the basis for improving memory in human disease states. Approach. NHPs trained to perform a short-term delayed match-to-sample (DMS) memory task were examined with multi-neuron recordings from synaptically connected hippocampal cell fields, CA1 and CA3. Recordings were analyzed utilizing a previously developed nonlinear multi-input multi-output (MIMO) neuroprosthetic model, capable of extracting CA3-to-CA1 spatiotemporal firing patterns during DMS performance. Main results. The MIMO model verified that specific CA3-to-CA1 firing patterns were critical for the successful encoding of sample phase information on more difficult DMS trials. This was validated by the delivery of successful MIMO-derived encoding patterns via electrical stimulation to the same CA1 recording locations during the sample phase which facilitated task performance in the subsequent, delayed match phase, on difficult trials that required more precise encoding of sample information. Significance. These findings provide the first successful application of a neuroprosthesis designed to enhance and/or repair memory encoding in primate brain.

Prediction of Rat Behavior Outcomes in Memory Tasks Using Functional Connections among Neurons

BackgroundAnalyzing the neuronal organizational structures and studying the changes in the behavior of the organism is key to understanding cognitive functions of the brain. Although some studies have indicated that spatiotemporal firing patterns of neuronal populations have a certain relationship with the behavioral responses, the issues of whether there are any relationships between the functional networks comprised of these cortical neurons and behavioral tasks and whether it is possible to take advantage of these networks to predict correct and incorrect outcomes of single trials of animals are still unresolved.Methodology/Principal FindingsThis paper presents a new method of analyzing the structures of whole-recorded neuronal functional networks (WNFNs) and local neuronal circuit groups (LNCGs). The activity of these neurons was recorded in several rats. The rats performed two different behavioral tasks, the Y-maze task and the U-maze task. Using the results of the assessment of the WNFNs and LNCGs, this paper describes a realization procedure for predicting the behavioral outcomes of single trials. The methodology consists of four main parts: construction of WNFNs from recorded neuronal spike trains, partitioning the WNFNs into the optimal LNCGs using social community analysis, unsupervised clustering of all trials from each dataset into two different clusters, and predicting the behavioral outcomes of single trials. The results show that WNFNs and LNCGs correlate with the behavior of the animal. The U-maze datasets show higher accuracy for unsupervised clustering results than those from the Y-maze task, and these datasets can be used to predict behavioral responses effectively.Conclusions/SignificanceThe results of the present study suggest that a methodology proposed in this paper is suitable for analysis of the characteristics of neuronal functional networks and the prediction of rat behavior. These types of structures in cortical ensemble activity may be critical to information representation during the execution of behavior.

Visual pattern discrimination by population retinal ganglion cells' activities during natural movie stimulation.

In the visual system, neurons often fire in synchrony, and it is believed that synchronous activities of group neurons are more efficient than single cell response in transmitting neural signals to down-stream neurons. However, whether dynamic natural stimuli are encoded by dynamic spatiotemporal firing patterns of synchronous group neurons still needs to be investigated. In this paper we recorded the activities of population ganglion cells in bullfrog retina in response to time-varying natural images (natural scene movie) using multi-electrode arrays. In response to some different brief section pairs of the movie, synchronous groups of retinal ganglion cells (RGCs) fired with similar but different spike events. We attempted to discriminate the movie sections based on temporal firing patterns of single cells and spatiotemporal firing patterns of the synchronous groups of RGCs characterized by a measurement of subsequence distribution discrepancy. The discrimination performance was assessed by a classification method based on Support Vector Machines. Our results show that different movie sections of the natural movie elicited reliable dynamic spatiotemporal activity patterns of the synchronous RGCs, which are more efficient in discriminating different movie sections than the temporal patterns of the single cells' spike events. These results suggest that, during natural vision, the down-stream neurons may decode the visual information from the dynamic spatiotemporal patterns of the synchronous group of RGCs' activities.

Nested theta to gamma oscillations and precise spatiotemporal firing during memory retrieval in a simulated attractor network

Nested oscillations, where the phase of the underlying slow rhythm modulates the power of faster oscillations, have recently attracted considerable research attention as the increased phase-coupling of cross-frequency oscillations has been shown to relate to memory processes. Here we investigate the hypothesis that reactivations of memory patterns, induced by either external stimuli or internal dynamics, are manifested as distributed cell assemblies oscillating at gamma-like frequencies with life-times on a theta scale. For this purpose, we study the spatiotemporal oscillatory dynamics of a previously developed meso-scale attractor network model as a correlate of its memory function. The focus is on a hierarchical nested organization of neural oscillations in delta/theta (2–5Hz) and gamma frequency bands (25–35Hz), and in some conditions even in lower alpha band (8–12Hz), which emerge in the synthesized field potentials during attractor memory retrieval. We also examine spiking behavior of the network in close relation to oscillations. Despite highly irregular firing during memory retrieval and random connectivity within each cell assembly, we observe precise spatiotemporal firing patterns that repeat across memory activations at a rate higher than expected from random firing. In contrast to earlier studies aimed at modeling neural oscillations, our attractor memory network allows us to elaborate on the functional context of emerging rhythms and discuss their relevance. We provide support for the hypothesis that the dynamics of coherent delta/theta oscillations constitute an important aspect of the formation and replay of neuronal assemblies.This article is part of a Special Issue entitled Neural Coding 2012.

Multi-unit Recording Methods to Characterize Neural Activity in the Locust (<em>Schistocerca Americana</em>) Olfactory Circuits

Detection and interpretation of olfactory cues are critical for the survival of many organisms. Remarkably, species across phyla have strikingly similar olfactory systems suggesting that the biological approach to chemical sensing has been optimized over evolutionary time. In the insect olfactory system, odorants are transduced by olfactory receptor neurons (ORN) in the antenna, which convert chemical stimuli into trains of action potentials. Sensory input from the ORNs is then relayed to the antennal lobe (AL; a structure analogous to the vertebrate olfactory bulb). In the AL, neural representations for odors take the form of spatiotemporal firing patterns distributed across ensembles of principal neurons (PNs; also referred to as projection neurons). The AL output is subsequently processed by Kenyon cells (KCs) in the downstream mushroom body (MB), a structure associated with olfactory memory and learning. Here, we present electrophysiological recording techniques to monitor odor-evoked neural responses in these olfactory circuits. First, we present a single sensillum recording method to study odor-evoked responses at the level of populations of ORNs. We discuss the use of saline filled sharpened glass pipettes as electrodes to extracellularly monitor ORN responses. Next, we present a method to extracellularly monitor PN responses using a commercial 16-channel electrode. A similar approach using a custom-made 8-channel twisted wire tetrode is demonstrated for Kenyon cell recordings. We provide details of our experimental setup and present representative recording traces for each of these techniques.

Multi-unit Recording Methods to Characterize Neural Activity in the Locust (<em>Schistocerca Americana</em>) Olfactory Circuits

Detection and interpretation of olfactory cues are critical for the survival of many organisms. Remarkably, species across phyla have strikingly similar olfactory systems suggesting that the biological approach to chemical sensing has been optimized over evolutionary time1. In the insect olfactory system, odorants are transduced by olfactory receptor neurons (ORN) in the antenna, which convert chemical stimuli into trains of action potentials. Sensory input from the ORNs is then relayed to the antennal lobe (AL; a structure analogous to the vertebrate olfactory bulb). In the AL, neural representations for odors take the form of spatiotemporal firing patterns distributed across ensembles of principal neurons (PNs; also referred to as projection neurons)2,3. The AL output is subsequently processed by Kenyon cells (KCs) in the downstream mushroom body (MB), a structure associated with olfactory memory and learning4,5. Here, we present electrophysiological recording techniques to monitor odor-evoked neural responses in these olfactory circuits. First, we present a single sensillum recording method to study odor-evoked responses at the level of populations of ORNs6,7. We discuss the use of saline filled sharpened glass pipettes as electrodes to extracellularly monitor ORN responses. Next, we present a method to extracellularly monitor PN responses using a commercial 16-channel electrode3. A similar approach using a custom-made 8-channel twisted wire tetrode is demonstrated for Kenyon cell recordings8. We provide details of our experimental setup and present representative recording traces for each of these techniques.

Modeling the dynamics of neural codes in the olfaction of the Manduca-sexta moth

The Anntenal Lobe (AL) is the olfactory processing unit in insects, composed of projection neurons (PNs) and local neurons (LNs) [1]. It has been demonstrated that the AL reformats the sensory input information that it receives into spatiotemporal firing patterns exhibited by the PNs [2,3]. In many insects the LNs are mainly inhibitory which suggests that the inhibition is responsible for shaping the input into a robust pattern [4]. The robustness of the pattern is expressed as follows: over several applications of the same odor the projection of the data recorded from the PNs onto a few dominant firing patterns results in a robust low dimensional trajectory. This trajectory appears to be similar to a trajectory that converges to a stable unique fixed point [5]. In this work, we resolve several open questions raised by [5,6]. Specifically, we study how interactions between the LNs and PNs permit creation of robust and spatiotemporal codes. We further propose a simple model that mimics the dynamics of the AL and the dynamics of mixtures of odors. Our work is a combination of theoretical analysis and experimental studies. Theoretically we consider a network of excitatory-inhibitory firing units that has a similar structure to the AL. By proposing to project the dynamical equations of the network onto given orthogonal spatial patterns, we derive the conditions on the interactions between the inhibitory and excitatory population such that the network will support a unique stable fixed point. The analysis is based on requiring that the inhibitory neurons will suppress inputs that are not associated to specific odors (noise and unknown patterns) but being neutral to the given spatial patterns. These conditions allow us to prescribe the connections between PNs and LNs and do not require specific symmetries (in contrast to Hopfield networks). Experimentally we record the dynamics of PNs in the AL of the Manduca Sexta moth both for inputs that are single odor or mixtures of two odors. Extracting the spatial patterns (first PCA mode) obtained from experiments with single odor inputs we calibrate the network. We are able to establish similarity between the model dynamics and experimental projections and thus validate our theoretical construction. Once the model is calibrated we test it against experiments with inputs that are different ratios of two odors revealing similar dynamics. To test the importance of inhibition, conjectured to be responsible for existence of a stable fixed point, we repeat the experiments in which the inhibition is blocked by a drug. Results demonstrate that the robustness of the dynamics is destroyed.

Closing the Loop for Memory Prosthesis: Detecting the Role of Hippocampal Neural Ensembles Using Nonlinear Models

A major factor involved in providing closed loop feedback for control of neural function is to understand how neural ensembles encode online information critical to the final behavioral endpoint. This issue was directly assessed in rats performing a short-term delay memory task in which successful encoding of task information is dependent upon specific spatio-temporal firing patterns recorded from ensembles of CA3 and CA1 hippocampal neurons. Such patterns, extracted by a specially designed nonlinear multi-input multi-output (MIMO) nonlinear mathematical model, were used to predict successful performance online via a closed loop paradigm which regulated trial difficulty (time of retention) as a function of the "strength" of stimulus encoding. The significance of the MIMO model as a neural prosthesis has been demonstrated by substituting trains of electrical stimulation pulses to mimic these same ensemble firing patterns. This feature was used repeatedly to vary "normal" encoding as a means of understanding how neural ensembles can be "tuned" to mimic the inherent process of selecting codes of different strength and functional specificity. The capacity to enhance and tune hippocampal encoding via MIMO model detection and insertion of critical ensemble firing patterns shown here provides the basis for possible extension to other disrupted brain circuitry.

A Computational Model to Investigate Astrocytic Glutamate Uptake Influence on Synaptic Transmission and Neuronal Spiking

Over the past decades, our view of astrocytes has switched from passive support cells to active processing elements in the brain. The current view is that astrocytes shape neuronal communication and also play an important role in many neurodegenerative diseases. Despite the growing awareness of the importance of astrocytes, the exact mechanisms underlying neuron-astrocyte communication and the physiological consequences of astrocytic-neuronal interactions remain largely unclear. In this work, we define a modeling framework that will permit to address unanswered questions regarding the role of astrocytes. Our computational model of a detailed glutamatergic synapse facilitates the analysis of neural system responses to various stimuli and conditions that are otherwise difficult to obtain experimentally, in particular the readouts at the sub-cellular level. In this paper, we extend a detailed glutamatergic synaptic model, to include astrocytic glutamate transporters. We demonstrate how these glial transporters, responsible for the majority of glutamate uptake, modulate synaptic transmission mediated by ionotropic AMPA and NMDA receptors at glutamatergic synapses. Furthermore, we investigate how these local signaling effects at the synaptic level are translated into varying spatio-temporal patterns of neuron firing. Paired pulse stimulation results reveal that the effect of astrocytic glutamate uptake is more apparent when the input inter-spike interval is sufficiently long to allow the receptors to recover from desensitization. These results suggest an important functional role of astrocytes in spike timing dependent processes and demand further investigation of the molecular basis of certain neurological diseases specifically related to alterations in astrocytic glutamate uptake, such as epilepsy.