Enhancement of synchronization in a hybrid neural circuit by spike timing dependent plasticity

Synchronization of neural activity is fundamental for many functions of the brain. We demonstrate that spike-timing dependent plasticity (STDP) enhances synchronization (entrainment) in a hybrid circuit composed of a spike generator, a dynamic clamp emulating an excitatory plastic synapse, and a chemically isolated neuron from the Aplysia abdominal ganglion. Fixed-phase entrainment of the Aplysia neuron to the spike generator is possible for a much wider range of frequency ratios and is more precise and more robust with the plastic synapse than with a nonplastic synapse of comparable strength. Further analysis in a computational model of HodgkinHuxley-type neurons reveals the mechanism behind this significant enhancement in synchronization. The experimentally observed STDP plasticity curve appears to be designed to adjust synaptic strength to a value suitable for stable entrainment of the postsynaptic neuron. One functional role of STDP might therefore be to facilitate synchronization or entrainment of nonidentical neurons

Spatial processing of conspecific signals in weakly electric fish: from sensory image to neural population coding

In this dissertation, I examine how an animal’s nervous system encodes spatially realistic conspecific signals in their environment and how the encoding mechanisms support behavioral sensitivity. I begin by modeling changes in the electrosensory signals exchanged by weakly electric fish in a social context. During this behavior, I estimate how the spatial structure of conspecific stimuli influences sensory responses at the electroreceptive periphery. I then quantify how space is represented in the hindbrain, specifically in the primary sensory area called the electrosensory lateral line lobe. I show that behavioral sensitivity is influenced by the heterogeneous properties of the pyramidal cell population. I further demonstrate that this heterogeneity serves to start segregating spatial and temporal information early in the sensory pathway. Lastly, I characterize the accuracy of spatial coding in this network and predict the role of network elements, such as correlated noise and feedback, in shaping the spatial information. My research provides a comprehensive understanding of spatial coding in the first stages of sensory processing in this system and allows us to better understand how network dynamics shape coding accuracy

On the Role of Sensory Cancellation and Corollary Discharge in Neural Coding and Behavior

Studies of cerebellum-like circuits in fish have demonstrated that synaptic plasticity shapes the motor corollary discharge responses of granule cells into highly-specific predictions of self- generated sensory input. However, the functional significance of such predictions, known as negative images, has not been directly tested. Here we provide evidence for improvements in neural coding and behavioral detection of prey-like stimuli due to negative images. In addition, we find that manipulating synaptic plasticity leads to specific changes in circuit output that disrupt neural coding and detection of prey-like stimuli. These results link synaptic plasticity, neural coding, and behavior and also provide a circuit-level account of how combining external sensory input with internally-generated predictions enhances sensory processing. In addition, the mammalian dorsal cochlear nucleus (DCN) integrates auditory nerve input with a diverse array of sensory and motor signals processed within circuity similar to the cerebellum. Yet how the DCN contributes to early auditory processing has been a longstanding puzzle. Using electrophysiological recordings in mice during licking behavior we show that DCN neurons are largely unaffected by self-generated sounds while remaining sensitive to external acoustic stimuli. Recordings in deafened mice, together with neural activity manipulations, indicate that self-generated sounds are cancelled by non-auditory signals conveyed by mossy fibers. In addition, DCN neurons exhibit gradual reductions in their responses to acoustic stimuli that are temporally correlated with licking. Together, these findings suggest that DCN may act as an adaptive filter for cancelling self-generated sounds. Adaptive filtering has been established previously for cerebellum-like sensory structures in fish suggesting a conserved function for such structures across vertebrates

The Spatial Structure of Stimuli Shapes the Timescale of Correlations in Population Spiking Activity

Throughout the central nervous system, the timescale over which pairs of neural spike trains are correlated is shaped by stimulus structure and behavioral context. Such shaping is thought to underlie important changes in the neural code, but the neural circuitry responsible is largely unknown. In this study, we investigate a stimulus-induced shaping of pairwise spike train correlations in the electrosensory system of weakly electric fish. Simultaneous single unit recordings of principal electrosensory cells show that an increase in the spatial extent of stimuli increases correlations at short (~10 ms) timescales while simultaneously reducing correlations at long (~100 ms) timescales. A spiking network model of the first two stages of electrosensory processing replicates this correlation shaping, under the assumptions that spatially broad stimuli both saturate feedforward afferent input and recruit an open-loop inhibitory feedback pathway. Our model predictions are experimentally verified using both the natural heterogeneity of the electrosensory system and pharmacological blockade of descending feedback projections. For weak stimuli, linear response analysis of the spiking network shows that the reduction of long timescale correlation for spatially broad stimuli is similar to correlation cancellation mechanisms previously suggested to be operative in mammalian cortex. The mechanism for correlation shaping supports population-level filtering of irrelevant distractor stimuli, thereby enhancing the population response to relevant prey and conspecific communication inputs. © 2012 Litwin-Kumar et al

Neuronal synchrony: peculiarity and generality

Synchronization in neuronal systems is a new and intriguing application of dynamical systems theory. Why are neuronal systems different as a subject for synchronization? (1) Neurons in themselves are multidimensional nonlinear systems that are able to exhibit a wide variety of different activity patterns. Their “dynamical repertoire” includes regular or chaotic spiking, regular or chaotic bursting, multistability, and complex transient regimes. (2) Usually, neuronal oscillations are the result of the cooperative activity of many synaptically connected neurons (a neuronal circuit). Thus, it is necessary to consider synchronization between different neuronal circuits as well. (3) The synapses that implement the coupling between neurons are also dynamical elements and their intrinsic dynamics influences the process of synchronization or entrainment significantly. In this review we will focus on four new problems: (i) the synchronization in minimal neuronal networks with plastic synapses (synchronization with activity dependent coupling), (ii) synchronization of bursts that are generated by a group of nonsymmetrically coupled inhibitory neurons (heteroclinic synchronization), (iii) the coordination of activities of two coupled neuronal networks (partial synchronization of small composite structures), and (iv) coarse grained synchronization in larger systems (synchronization on a mesoscopic scale

Peripheral and Central Mechanisms of Temporal Pattern Recognition

Encoding information into the timing patterns of action potentials, or spikes, is a strategy used broadly in neural circuits. This type of coding scheme requires downstream neurons to be sensitive to the temporal patterns of presynaptic inputs. Indeed, neurons with temporal filtering properties have been found in a wide range of sensory pathways. However, how such response properties arise was previously not well understood. The goal of my dissertation research has been to elucidate how temporal filtering by single neurons contributes to the behavioral ability to recognize timing patterns in communication signals. I have addressed this question using mormyrid weakly electric fish, which vary the time intervals between successive electric pulses to communicate. Fish detect these signals with sensory receptors in their skin. In the majority of species, these receptors fire a single spike in response to each electric pulse. Spiking receptors faithfully encode the interpulse intervals in communication signals into interspike intervals, which are then decoded by interval-selective midbrain neurons. Using in vivo intracellular recordings from awake fish during sensory stimulation, I found that short-term depression and temporal summation play important roles in establishing single-neuron interval selectivity. Moreover, the combination of short-term depression and temporal summation in the circuit resulted in greater diversity of interval tuning properties across the population of neurons, which would increase the population’s ability to detect temporally patterned communication signals. Indeed, I found that the responses of single interval-selective neurons were sensitive to subtle variation in the timing patterns of a specific communication display produced by different individuals. A subset of mormyrid species has sensory receptors that produce spontaneously oscillating potentials. How the electrosensory system of these species established sensitivity to temporally patterned communication signals was completely unknown. Using in vivo extracellular recordings, I demonstrated that these receptors encode sensory stimuli into phase resets, which is the first clear instance of information coding by oscillatory phase reset. Furthermore, the ongoing oscillations conferred enhanced sensitivity to fast temporal patterns that are only found in the communication signals of a large group of fish. Behavioral playback experiments provided further support for the hypothesis that oscillating receptors are specialized for detecting communication signals produced by a group of conspecifics, which is a novel role for a sensory receptor. These findings demonstrate that temporal pattern sensitivity, which was previously thought to be a central processing problem, can also arise from peripheral filtering through a novel oscillatory phase reset mechanism

Spike-Timing-Dependent Plasticity Alters Sensory Network Connectivity

A fundamental question in neuroscience is: how does a sensory system optimize detection of behaviorally relevant stimuli, when those stimuli and the sensory environment are constantly changing? Spike-timing-dependent plasticity (STDP), in which synapse strength changes based on the relative timing of pre- and post-synaptic spiking, has been implicated in changes in neuronal connectivity thought to underlie learning and memory. Synaptic strength changes caused by STDP have been shown in optic tectum, visual cortex, hippocampus, and other brain regions in vitro across many organisms like fish, frogs, and mice. Although it is possible that STDP mechanisms underlie changes in sensory neuron connectivity, the relationship between sensory stimulation and central sensory neuronal response is complex and often involves populations of neurons that differ in the timing and frequency of spiking, resulting in complex spatiotemporal patterns of synaptic input to postsynaptic neurons. The organism I studied, weakly electric fish, produce and receive electric organ discharges (EODs) used to electrolocate and communicate. Taking advantage of the electrosensory system, weakly electric fishes are a system in which spiking patterns are themselves the behaviorally relevant stimulus. Previous work showed that STDP predictably altered synaptic xi responses and inter-pulse interval tuning in vitro (Ma and Carlson, unpublished). Using whole-cell intracellular recordings to repetitively pair sensory stimulation with intracellular spiking in vivo, I manipulated the relative timing of pre- and post-synaptic spiking in central sensory neurons in awake, behaving animals. I found that STDP alters sensory responses of central electrosensory neurons in vivo but there was more variability in the changes in sensory responses in vivo relative to the in vitro changes in synaptic responses (Chapter 2). Whether the in vivo data “fit” or “did not fit” the pattern predicted by the in vitro results was correlated with variations in synaptic potential landmarks. That variations in synaptic potential landmarks correlated with deviations from the pattern shown in vitro results suggest that whether the data “fit” or “did not fit” the in vitro hypothesis is influenced by polysynaptic activity, including inhibitory interneurons (Chapter 2). I now asked whether STDP could alter sensory tuning to behaviorally relevant stimuli in vivo. Using whole-cell intracellular recordings, I recorded postsynaptic potential responses to two different sensory stimuli before and after pairing postsynaptic spiking with only one of those sensory stimuli. I found that some in vivo responses followed the pattern predicted by STDP sensory tuning experiments done in vitro and some in vivo responses that did not. Whether the in vivo sensory tuning data “fit” or “did not fit” the pattern predicted by the in vitro sensory tuning changes was correlated with variations in synaptic potential landmarks. That variations in the synaptic potential landmarks correlated with differences in the in vitro and in vivo sensory tuning suggest that whether the in vivo tuning results did or did not “fit” the in vitro tuning prediction is influenced by polysynaptic activity, including inhibitory interneurons (Chapter 3). Next, I wanted to ask whether intrinsic network activity could alter sensory tuning based solely on the input of behaviorally relevant stimuli. Using extracellular evoked potential recordings and a freely behaving paradigm, I recorded postsynaptic potential responses and behavioral output to two xii different sensory stimuli before and after repeating only one of those sensory stimuli, with no pairing of postsynaptic spiking. I did not find any significant differences in the evoked potentials or behavior as a result of repetition of a sensory stimulus (Chapter 4). Thus, in this dissertation I showed that STDP can alter the sensory responses of central electrosensory neurons, but that STDP rules operating at identified synapses may not drive predictable changes in sensory responses and sensory tuning at the circuit or behavioral level. In conclusion, for altering sensory tuning in adult organisms in a changing sensory environment in vivo, the role of STDP is more complex than had been predicted from previous work in vitr

Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring

Over the last 2 decades, a large number of neurophysiological and neuroimaging studies of patients with schizophrenia have furnished in vivo evidence for dysconnectivity, ie, abnormal functional integration of brain processes. While the evidence for dysconnectivity in schizophrenia is strong, its etiology, pathophysiological mechanisms, and significance for clinical symptoms are unclear. First, dysconnectivity could result from aberrant wiring of connections during development, from aberrant synaptic plasticity, or from both. Second, it is not clear how schizophrenic symptoms can be understood mechanistically as a consequence of dysconnectivity. Third, if dysconnectivity is the primary pathophysiology, and not just an epiphenomenon, then it should provide a mechanistic explanation for known empirical facts about schizophrenia. This article addresses these 3 issues in the framework of the dysconnection hypothesis. This theory postulates that the core pathology in schizophrenia resides in aberrant N-methyl-D-aspartate receptor (NMDAR)–mediated synaptic plasticity due to abnormal regulation of NMDARs by neuromodulatory transmitters like dopamine, serotonin, or acetylcholine. We argue that this neurobiological mechanism can explain failures of self-monitoring, leading to a mechanistic explanation for first-rank symptoms as pathognomonic features of schizophrenia, and may provide a basis for future diagnostic classifications with physiologically defined patient subgroups. Finally, we test the explanatory power of our theory against a list of empirical facts about schizophrenia