Patterns Of Spike Activity Research Articles

Stochastic control of spiking activity bump expansion: Monotonic and resonant phenomena.

We consider spatially localized spiking activity patterns, so-called bumps, in ensembles of bistable spiking oscillators. The bistability consists in the coexistence of self-sustained spiking dynamics and a quiescent steady-state regime. We show numerically that the processes of growth or contraction of such patterns can be controlled by varying the intensity of multiplicative noise. In particular, the effect of noise is monotonic in an ensemble of coupled Hindmarsh-Rose oscillators. On the other hand, in another model proposed by Semenov et al. [Semenov et al., Phys. Rev. E 93, 052210 (2016)], a resonant noise effect is observed. In that model, stabilization of activity bump expansion is achieved at an appropriate noise level, and the noise effect reverses with a further increase in noise intensity. Moreover, we show the constructive role of nonlocal coupling that allows us to save domains and fronts being totally destroyed due to the action of noise in the case of local coupling.

Brain state limits propagation of neural signals in laminar cortical circuits

Our perception of the environment relies on the efficient propagation of neural signals across cortical networks. During the time course of a day, neural responses fluctuate dramatically as the state of the brain changes to possibly influence how electrical signals propagate across neural circuits. Despite the importance of this issue, how patterns of spiking activity propagate within neuronal circuits in different brain states remains unknown. Here, we used multielectrode laminar arrays to reveal that brain state strongly modulates the propagation of neural activity across the layers of early visual cortex (V1). We optogenetically induced synchronized state transitions within a group of neurons and examined how far electrical signals travel during wakefulness and rest. Although optogenetic stimulation elicits stronger neural responses during wakefulness relative to rest, signals propagate only weakly across the cortical column during wakefulness, and the extent of spread is inversely related to arousal level. In contrast, the light-induced population activity vigorously propagates throughout the entire cortical column during rest, even when neurons are in a desynchronized wake-like state prior to light stimulation. Mechanistically, the influence of global brain state on the propagation of spiking activity across laminar circuits can be explained by state-dependent changes in the coupling between neurons. Our results impose constraints on the conclusions of causal manipulation studies attempting to influence neural function and behavior, as well as on previous computational models of perception assuming robust signal propagation across cortical layers and areas.

Cerebellar Dysfunction as a Source of Dystonic Phenotypes in Mice

There is now a substantial amount of compelling evidence demonstrating that the cerebellum may be a central locus in dystonia pathogenesis. Studies using spontaneous genetic mutations in rats and mice, engineered genetic alleles in mice, shRNA knockdown in mice, and conditional genetic silencing of fast neurotransmission in mice have all uncovered a common set of behavioral and electrophysiological defects that point to cerebellar cortical and cerebellar nuclei dysfunction as a source of dystonic phenotypes. Here, we revisit the Ptf1aCre/+;Vglut2flox/flox mutant mouse to define fundamental phenotypes and measures that are valuable for testing the cellular, circuit, and behavioral mechanisms that drive dystonia. In this model, excitatory neurotransmission from climbing fibers is genetically eliminated and, as a consequence, Purkinje cell and cerebellar nuclei firing are altered in vivo, with a prominent and lasting irregular burst pattern of spike activity in cerebellar nuclei neurons. The resulting impact on behavior is that the mice have developmental abnormalities, including twisting of the limbs and torso. These behaviors continue into adulthood along with a tremor, which can be measured with a tremor monitor or EMG. Importantly, expression of dystonic behavior is reduced upon cerebellar-targeted deep brain stimulation. The presence of specific combinations of disease-like features and therapeutic responses could reveal the causative mechanisms of different types of dystonia and related conditions. Ultimately, an emerging theme places cerebellar dysfunction at the center of a broader dystonia brain network.

Fast-slow analysis as a technique for understanding the neuronal response to current ramps.

The standard protocol for studying the spiking properties of single neurons is the application of current steps while monitoring the voltage response. Although this is informative, the jump in applied current is artificial. A more physiological input is where the applied current is ramped up, reflecting chemosensory input. Unsurprisingly, neurons can respond differently to the two protocols, since ion channel activation and inactivation are affected differently. Understanding the effects of current ramps, and changes in their slopes, is facilitated by mathematical models. However, techniques for analyzing current ramps are under-developed. In this article, we demonstrate how current ramps can be analyzed in single neuron models. The primary issue is the presence of gating variables that activate on slow time scales and are therefore far from equilibrium throughout the ramp. The use of an appropriate fast-slow analysis technique allows one to fully understand the neural response to ramps of different slopes. This study is motivated by data from olfactory bulb dopamine neurons, where both fast ramp (tens of milliseconds) and slow ramp (tens of seconds) protocols are used to understand the spiking profiles of the cells. The slow ramps generate experimental bifurcation diagrams with the applied current as a bifurcation parameter, thereby establishing asymptotic spiking activity patterns. The faster ramps elicit purely transient behavior that is of relevance to most physiological inputs, which are short in duration. The two protocols together provide a broader understanding of the neuron's spiking profile and the role that slowly activating ion channels can play.

Optogenetic fUSI for brain-wide mapping of neural activity mediating collicular-dependent behaviors

Neuronal cell types are arranged in brain-wide circuits that guide behavior. In mice, the superior colliculus innervates a set of targets that direct orienting and defensive actions. We combined functional ultrasound imaging (fUSI) with optogenetics to reveal the network of brain regions functionally activated by four collicular cell types. Stimulating each neuronal group triggered different behaviors and activated distinct sets of brain nuclei. This included regions not previously thought to mediate defensive behaviors, for example, the posterior paralaminar nuclei of the thalamus (PPnT), which we show to play a role in suppressing habituation. Neuronal recordings with Neuropixels probes show that (1) patterns of spiking activity and fUSI signals correlate well in space and (2) neurons in downstream nuclei preferentially respond to innately threatening visual stimuli. This work provides insight into the functional organization of the networks governing innate behaviors and demonstrates an experimental approach to explore the whole-brain neuronal activity downstream of targeted cell types.

Regional White Matter Atrophy Correlates with Spike Activity in Encephalopathy Related to Status Epilepticus During Slow Sleep (ESES) After Early Thalamic Lesions.

Encephalopathy related to Status Epilepticus during slow Sleep (ESES) is an age-related, epileptic syndrome, which associates cognitive/behavioral disturbances with a peculiar pattern of spike activity. One promising line of research is the study of ESES in cases of early thalamic lesions. We studied 7 ESES patients with unilateral thalamic lesions using magnetic resonance imaging to assess regional white matter (WM) and thalamic nuclei volume differences, and long-term electroencephalogram recordings to localize the epileptogenic cortex. N170 event-related potentials were used to demonstrate the dysfunctional character of the WM abnormalities. Diffusion-weighted images in a subset of 4 patients were used to parcellate the thalamus and evaluate volume asymmetries, based on cortical connectivity. Large WM regional atrophy in the hemisphere with the thalamic lesion was associated with both cortical dysfunction and epileptic activity. A correlation was demonstrated between lesions in the pulvinar and the mediodorsal thalamic nuclei and WM atrophy of the corresponding cortical projection areas. We propose that these abnormalities are due to the widespread structural disconnection produced by the thalamic lesions associated to a yet unknown age-dependent factor. Further exploration of WM regional atrophy association with the spike activity in other etiologies could lend support to the cortical disconnection role in ESES genesis.

SPARC‐Network‐Scale Interactions Among Simultaneously Recorded Brainstem Neurons In The Dorsal Medulla, Ventrolateral Medulla, And Pons: A Substrate For Central Autonomic Control And Processing Of Cardiovascular Afferent Feedback

The central autonomic control system provides parasympathetic and sympathetic drive to peripheral ganglia that regulate a host of visceral functions. Detailed knowledge of the network scale organization of this central circuit is incomplete. We hypothesized that functional synaptic interactions between many simultaneously recorded neurons in and around the nucleus of the tractus solitarius (NTS), the retrotrapezoid nucleus (RTN), ventral respiratory column (VRC) and pons would yield information about the network‐scale strategy that is employed by this system to regulate autonomic drive. Experiments were conducted in 73 decerebrated, paralyzed, and artificially ventilated cats. Multi‐electrode arrays including up to 100 channels were placed in the areas of the NTS, RTN, and VRC. A total of 2,643 neurons were challenged with alterations in blood pressure (BP) and cardiac modulation was assessed in 3,531 neurons. The spike activity patterns of many simultaneously recorded single neurons were evaluated during breathing, the cardiac cycle, increased activation of baroreceptors by inflation of a balloon in the abdominal aorta and unloading of baroreceptors by inflation of a balloon in the vena cava. There was no apparent anatomical segregation of neurons in these areas with regard to cardiac or BP modulation based on stereotaxic coordinates. Cross correlation analysis revealed robust excitatory and inhibitory functional interactions between neurons in the areas of the NTS, VRC, RTN, and pons. Plots customized to reveal patterns of information transfer between neurons in different areas provided evidence that units in the RTN and VRC were predominantly exciting those in the NTS area, while these same NTS neurons were exciting and inhibiting different neurons in the RTN and VRC. Specific evidence was found for putative intermediate ventrolateral medulla (iVLM) neurons that were excited by BP and involved in inhibitory interactions with putative rostral ventrolateral medulla (RVLM) neurons that were inhibited by increased BP. However, this mechanism was a low frequency feature of the dataset. Evidence for synaptic inhibition between neurons that responded to increased BP was a predominant finding. Among neurons that responded to decreased BP, evidence for synaptic excitation was more common. These results support differential organization of brainstem networks that mediate autonomic responses to increased BP relative to those that mediate decreased BP. The brainstem network that mediates sympatho‐inhibition in response to increased blood pressure is sparsely excitatory and may depend on abundant inhibitory mechanisms to enhance signal/noise ratios in support of information transmission from baroreceptors.Support or Funding InformationSupported by 3OT2OD023854

Fractal spike dynamics and neuronal coupling in the primate visual system.

We measured fractal (self-similar) fluctuations in ongoing spiking activity in subcortical (lateral geniculate nucleus, LGN) and cortical (area MT) visual areas in anaesthetised marmosets. Cells in the evolutionary ancient koniocellular LGN pathway and in area MT show high-amplitude fractal fluctuations, whereas evolutionarily newer parvocellular and magnocellular LGN cells do not. Spiking activity in koniocellular cells and MT cells shows substantial correlation to the local population activity, whereas activity in parvocellular and magnocellular cells is less correlated with local activity. We develop a model consisting of a fractal process and a global rate modulation which can reproduce and explain the fundamental relationship between fractal fluctuations and population coupling in LGN and MT. The model provides a unified account of apparently disparate aspects of neural spiking activity and can improve our understanding of information processing in evolutionary ancient and modern visual pathways. The brain represents and processes information through patterns of spiking activity, which is influenced by local and widescale brain circuits as well as intrinsic neural dynamics. Whether these influences have independent or linked effects on spiking activity is, however, not known. Here we measured spiking activity in two visual centres, the lateral geniculate nucleus (LGN) and cortical area MT, in marmoset monkeys. By combining the Fano-factor time curve, power spectral analysis and rescaled range analysis, we reveal inherent fractal fluctuations of spiking activity in LGN and MT. We found that the evolutionary ancient koniocellular (K) pathway in LGN and area MT exhibits strong fractal fluctuations at short (<1s) time scales. Parvocellular (P) and magnocellular (M) LGN cells show weaker fractal fluctuations at longer (multi-second) time scales. In both LGN and MT, the amplitude and time scale of fractal fluctuations can explain short and long time scale spiking dynamics. We further show differential neuronal coupling of LGN and MT cells to local population spiking activity. The population coupling is intrinsically linked to fractal fluctuations: neurons showing stronger fluctuations are more strongly correlated to the local population activity. To understand this relationship, we modelled spiking activity using a fractal inhomogeneous Poisson process with dynamic rate, which is the product of an intrinsic stochastic fractal rate and a global modulatory gain. Our model explains the intrinsic links between neuronal spike rate and population coupling in LGN and MT, and establishes a unified account of dynamic spiking properties in afferent visual pathways.

Identification of midbrain dopamine neurons using features from spontaneous spike activity patterns.

Although dopamine neurons are considered essential in various brain functions, their specific roles are under debate, partially due to the difficulty to identify dopamine neurons among surrounding neurons deep in the brain based only on the extracellularly recorded electric activities. Thus, a handy method to identify dopamine and non-dopamine neurons based on the spontaneous activity patterns is desired. Here we tried to discriminate optogenetically-identified dopamine neurons from other types of neurons and obtained 86.0% success.

Synaptic Strengths Dominate Phasing of Motor Circuit: Intrinsic Conductances of Neuron Types Need Not Vary across Animals.

Identified neurons and the networks they compose produce stereotypical, albeit individually unique, activity across members of a species. We propose, for a motor circuit driven by a central pattern generator (CPG), that the uniqueness derives mainly from differences in synaptic strength rather than from differences in intrinsic membrane conductances. We studied a dataset of recordings from six leech (Hirudo sp.) heartbeat control networks, containing complete spiking activity patterns from inhibitory premotor interneurons, motor output spike patterns, and synaptic strength patterns to investigate the source of uniqueness. We used a conductance-based multicompartmental motor neuron model to construct a bilateral motor circuit model, and controlled it by playing recorded input spike trains from premotor interneurons to generate output inhibitory synaptic patterns similar to experimental measurements. By generating different synaptic conductance parameter sets of this circuit model, we found that relative premotor synaptic strengths impinging onto motor neurons must be different across individuals to produce animal-specific output burst phasing. Obtaining unique outputs from each individual's circuit model did not require different intrinsic ionic conductance parameters. Furthermore, changing intrinsic conductances failed to compensate for modified synaptic strength patterns. Thus, the pattern of synaptic strengths of motor neuron inputs is critical for the phasing of this motor circuit and can explain individual differences. When intrinsic conductances were allowed to vary, they exhibited the same conductance correlations across individuals, suggesting a motor neuron "type" required for proper network function. Our results are general and may translate to other systems and neuronal networks that control output phasing.

Dendritic Integration of Sensory Evidence in Perceptual Decision-Making

SummaryPerceptual decisions require the accumulation of sensory information to a response criterion. Most accounts of how the brain performs this process of temporal integration have focused on evolving patterns of spiking activity. We report that subthreshold changes in membrane voltage can represent accumulating evidence before a choice. αβ core Kenyon cells (αβc KCs) in the mushroom bodies of fruit flies integrate odor-evoked synaptic inputs to action potential threshold at timescales matching the speed of olfactory discrimination. The forkhead box P transcription factor (FoxP) sets neuronal integration and behavioral decision times by controlling the abundance of the voltage-gated potassium channel Shal (KV4) in αβc KC dendrites. αβc KCs thus tailor, through a particular constellation of biophysical properties, the generic process of synaptic integration to the demands of sequential sampling.

The Spinal Neurons Exhibit an ON-OFF and OFF-ON Firing Activity Around the Onset of Fictive Scratching Episodes in the Cat

In a previous report, we found neurons with ON-OFF and OFF-ON firing activity in the obex reticular formation during scratching. The aim of the present study was to examine whether the spinal neurons also exhibit this type of activity in relation to the “postural stage” of fictive scratching in the cat. We found that the extensor and intermediate scratching neurons exhibit an ON-OFF firing rate; conversely, the flexor neurons show an OFF-ON activity, relative to every scratching episode. These patterns of spiking activity are similar to those found in neurons from the obex reticular formation during scratching. Our findings provide support to the following hypotheses. First, there is a possible functional link between supraspinal and spinal, ON-OFF and OFF-ON neuronal groups. Second, the fictive goal-directed motor action to maintain the fictive “postural stage” of the hindlimb during fictive scratching is associated with the neuronal tonic activity of the OFF-ON spinal neurons, whereas the ON-OFF spinal neurons are associated with an extensor tone that occurred prior the postural stage.

Modulation of Context-Dependent Spatiotemporal Patterns within Packets of Spiking Activity.

Recent experiments have shown that stereotypical spatiotemporal patterns occur during brief packets of spiking activity in the cortex, and it has been suggested that top-down inputs can modulate these patterns according to the context. We propose a simple model that may explain important features of these experimental observations and is analytically tractable. The key mechanism underlying this model is that context-dependent top-down inputs can modulate the effective connection strengths between neurons because of short-term synaptic depression. As a result, the degree of synchrony and, in turn, the spatiotemporal patterns of spiking activity that occur during packets are modulated by the top-down inputs. This is shown using an analytical framework, based on avalanche dynamics, that allows calculating the probability that a given neuron spikes during a packet and numerical simulations. Finally, we show that the spatiotemporal patterns that replay previously experienced sequential stimuli and their binding with their corresponding context can be learned because of spike-timing-dependent plasticity.

Domoic acid disrupts the activity and connectivity of neuronal networks in organotypic brain slice cultures

Domoic acid is a neurotoxin produced by algae and is found in seafood during harmful algal blooms. As a glutamate agonist, domoic acid inappropriately stimulates excitatory activity in neurons. At high doses, this leads to seizures and brain lesions, but it is unclear how lower, asymptomatic exposures disrupt neuronal activity. Domoic acid has been detected in an increasing variety of species across a greater geographical range than ever before, making it critical to understand the potential health impacts of low-level exposure on vulnerable marine mammal and human populations. To determine whether prolonged domoic acid exposure altered neuronal activity in hippocampal networks, we used a custom-made 512 multi-electrode array with high spatial and temporal resolution to record extracellular potentials (spikes) in mouse organotypic brain slice cultures. We identified individual neurons based on spike waveform and location, and measured the activity and functional connectivity within the neuronal networks of brain slice cultures. Domoic acid exposure significantly altered neuronal spiking activity patterns, and increased functional connectivity within exposed cultures, in the absence of overt cellular or neuronal toxicity. While the overall spiking activity of neurons in domoic acid-exposed cultures was comparable to controls, exposed neurons spiked significantly more often in bursts. We also identified a subset of neurons that were electrophysiologically silenced in exposed cultures, and putatively identified those neurons as fast-spiking inhibitory neurons. These results provide evidence that domoic acid affects neuronal activity in the absence of cytotoxicity, and suggest that neurodevelopmental exposure to domoic acid may alter neurological function in the absence of clinical symptoms.

Coherent neuronal ensembles are rapidly recruited when making a look-reach decision.

SummarySelecting and planning actions recruits neurons across many areas of the brain but how ensembles of neurons work together to make decisions is unknown. Temporally-coherent neural activity may provide a mechanism by which neurons coordinate their activity in order to make decisions. If so, neurons that are part of coherent ensembles may predict movement choices before other ensembles of neurons. We recorded neuronal activity in the lateral and medial banks of the intraparietal sulcus (IPS) of the posterior parietal cortex, while monkeys made choices about where to look and reach and decoded the activity to predict the choices. Ensembles of neurons that displayed coherent patterns of spiking activity extending across the IPS, “dual coherent” ensembles, predicted movement choices substantially earlier than other neuronal ensembles. We propose that dual-coherent spike timing reflects interactions between groups of neurons that play an important role in how we make decisions.

Effect of synaptic plasticity on functional connectivity and global activity of a neocortical network model

Neocortex plays key role in diverse brain functions. Understanding this role involves the study of collective neural activity patterns under different situations, and how these patterns relate to the structural and functional organization of neocortex. Here we study the effect of synaptic plasticity on neural spiking activity patterns in a neocortical network model. We measure changes in neural spiking patterns due to changes in the strengths of the synapses connecting neurons and relate them to changes in the functional connectivity of the network as disclosed by graph-theoretic measures. Our neocortical network model was composed of excitatory and inhibitory neurons in the proportion of four excitatory cells for each inhibitory cell. Neurons were described by the Izhikevich model [1]. The parameters of the model were adjusted so that excitatory neurons were of the regular spiking (RS) type and inhibitory neurons were all of either the fast spiking (FS) or the low-threshold spiking (LTS) type. Synapses were modeled as event-based, and two types of synaptic dynamics were considered: one without synaptic plasticity in which the synaptic weight received a fixed increment after the pre-synaptic event and decayed exponentially after that, and one with synaptic plasticity in which the synapse obeyed an asymmetric spike-timing dependent plasticity (STDP) rule described by [2]. Neurons were organized into four layers (2/3, 4, 5 and 6) with layer- and cell-specific statistical connectivity rules based on [3]. The total number of neurons in the model was about 4,000. Two experiments were done: one with all synapses described by the model without synaptic plasticity, and the other with synapses between excitatory neurons described by the STDP rule while the remaining synapses were described by the model without synaptic plasticity. In both cases, the model was stimulated by a current injection of random amplitude applied to neurons of layer 4 (L4), which is the main input layer of the cortex. The spiking activity of the network was evaluated by measures extracted from the raster plot of the spikes produced by the neurons, e.g. layer-specific and network mean and time-dependent firing rates. The structural and functional connectivities of the network were represented by the respective structural and functional adjacency matrices. The functional adjacency matrix was constructed by taking in consideration neuron pairs with strength of their synaptic coupling above a specific threshold. The topology of the adjacency matrices was characterized by graph-theoretic measures, e.g. clustering coefficient. We determined a set of parameters for which the spiking activity generated in L4 by the external input propagated to the entire network. This network-wide activity was oscillatory, and we found that its mean frequency was higher for the network version with synaptic plasticity than for the version without synaptic plasticity. We also found that the formation of clusters of synchronous neural activity was facilitated in the case with LTS cells as inhibitory neurons. Our results suggest that synaptic plasticity may induce changes in the functional connectivity of the neocortical network with impact on its global activity.

Spatiotemporal Coding of Individual Chemicals by the Gustatory System.

Our results provide a new view of taste processing. Using a new, relatively simple model system and a new set of techniques to deliver taste stimuli and to examine gustatory receptor neurons and their immediate followers, we found no evidence for labeled line connectivity, or basic taste categories such as sweet, salty, bitter, and sour. Rather, individual tastant chemicals are represented as patterns of spiking activity distributed across populations of receptor neurons. These representations are transformed substantially as multiple types of receptor neurons converge upon follower neurons, leading to a combinatorial coding format that uniquely, rapidly, and efficiently represents individual taste chemicals. Finally, we found that the information content of these neurons can drive tastant-specific behavior.

Different propagation speeds of recalled sequences in plastic spiking neural networks

Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in experiments.

Operant Conditioning of Synaptic and Spiking Activity Patterns in Single Hippocampal Neurons

Learning is a process of plastic adaptation through which a neural circuit generates a more preferable outcome; however, at a microscopic level, little is known about how synaptic activity is patterned into a desired configuration. Here, we report that animals can generate a specific form of synaptic activity in a given neuron in the hippocampus. In awake, head-restricted mice, we applied electrical stimulation to the lateral hypothalamus, a reward-associated brain region, when whole-cell patch-clamped CA1 neurons exhibited spontaneous synaptic activity that met preset criteria. Within 15 min, the mice learned to generate frequently the excitatory synaptic input pattern that satisfied the criteria. This reinforcement learning of synaptic activity was not observed for inhibitory input patterns. When a burst unit activity pattern was conditioned in paired and nonpaired paradigms, the frequency of burst-spiking events increased and decreased, respectively. The burst reinforcement occurred in the conditioned neuron but not in other adjacent neurons; however, ripple field oscillations were concomitantly reinforced. Neural conditioning depended on activation of NMDA receptors and dopamine D1 receptors. Acutely stressed mice and depression model mice that were subjected to forced swimming failed to exhibit the neural conditioning. This learning deficit was rescued by repetitive treatment with fluoxetine, an antidepressant. Therefore, internally motivated animals are capable of routing an ongoing action potential series into a specific neural pathway of the hippocampal network.

A spatiotemporal coding mechanism for background independent recognition of odors

Sensory stimuli often evoke temporal patterns of spiking activity across a population of neurons in the early processing stages. These neural responses are considered a ‘temporal code’ if they change on a timescale that is different than the stimulus variations that caused them and when they convey useful information about the stimulus. A fundamental problem in sensory neuroscience is determining what stimulus-specific information is encoded by dynamic patterns of ensemble neural activity and whether this information is behaviorally relevant. Furthermore, since the same stimulus can be encountered in a variety of ways in natural environments, what attributes of the spatiotemporal population responses are invariant to any or all such variations in stimulus features? In this talk, I will describe how dynamic processing of odor signals allows an invertebrate olfactory system to recognize odorants in a background-independent manner. I will first discuss how freshly introduced odors are received by olfactory receptor neurons in the locust antenna (Schistocerca americana), and then show how the generated sensory input is transformed, step-by-step, into background-invariant neural representations. I will conclude with a brief discussion of correlations between temporally patterned neural activity and behavioral performance of locusts in an odor recognition task.