Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session R67: Physics of Neural Systems II |
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Sponsoring Units: DBIO Chair: Vijay Singh, University of Pennsylvania Room: BCEC 050 |
Thursday, March 7, 2019 8:00AM - 8:12AM |
R67.00001: Progress on functional connectivity measurement and modeling in C. elegans Francesco Randi, Anuj K Sharma, Andrew M Leifer Advances in microscopy and optogenetics now permit "functional connectivity" experiments in which some neurons are optogenetically stimulated while the responses of other neurons in the network are simultaneously measured, ideally giving access to the strengths and the dynamical properties of the interactions between neurons. Currently, the nematode C. elegans is an ideal candidate in which to perform such experiments, because of its small nervous system, the known anatomical connectivity map between the neurons, and the graded nature of their activity. I will present progress on such measurements on the whole brain of the worm. |
Thursday, March 7, 2019 8:12AM - 8:24AM |
R67.00002: Bridging severed nerves in a mouse using Carbon Nanotubes (CNTs): Identifying artifact vs. neural signals suggests transmission was partially restored Vineet Mathur, Zachariah Hennighausen, Swastik Kar Stimulation and recording of neural tissue are hallmarks of investigation of neural activity, especially the detection of action potentials. In this context, accurate analysis of curve-shapes holds significant value in distinguishing between neural activity compared to background noise and instrument artifacts. We report the remarkable observation that when probed using an electric field stimulation technique (EFS) in an in-vitro setting, control experiments that contain no neural tissue reproducibly produce curves in-distinguishable from experiments containing sources of neuronal activity. We additionally provide a physical model to explain the origin and behavior of such false-positive signals in relation to the buffer and neural circuits in the system. Lastly, we present data and analysis on in-vivo experiments using a mouse and carbon nanotubes (CNTs). Our experiments detected a previously unreported dominant secondary pulse delayed over 16ms after the artifact signal. Comparing the intact, severed, and bridged (with CNTs) spinal cord suggests transmission of neural signals was partially restored. |
Thursday, March 7, 2019 8:24AM - 8:36AM |
R67.00003: Nonlinear synchronization of neuronal firing on a random graph: Application to breathing rhythm formation in the preBötzinger complex. Valentin Slepukhin, Alexander Jacob Levine, Jack L Feldman, Sufyan Ashhad The preBötzinger complex is a network of a few thousands of neurons that produces the rhythmic signal controlling mammalian breathing (inspiration). In vitro experiments demonstrated that the activation of small group of neurons in this network results in a “burstlet” of neuronal firing that propagates through the network after some delay (Kam et al., 2013). We consider a simple model burstlet dynamics based on the nonlinear synchronization of imprecise neuronal oscillators interacting on a random graph. The numerical simulations based on this model reproduce features observed in the experiment, such as (i) the probability of systemic synchronization in response to a given number of stimulated neurons, and (ii) the time lag between stimulation and systemic synchronization as a function of the number of stimulated neurons. We conclude by discussing how the topology of the neuronal connectivity affects the robustness of the emergent rhythm dynamics of the network in response to local damage (cell death). |
Thursday, March 7, 2019 8:36AM - 8:48AM |
R67.00004: WITHDRAWN ABSTRACT
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Thursday, March 7, 2019 8:48AM - 9:00AM |
R67.00005: Global synchronization due to cluster size heterogeneity in balanced neural networks Abhijit Chakraborty, Greg Morrison Synchronized brain rhythms have been associated with various cognitive functions as well as neurological disorders, and comprehending how the network connectivities give rise to these behaviors is essential for a better understanding of the importance of network topology on functional brain dynamics. Previous studies have shown that cortical networks with clustered connections give rise to correlated dynamics in individual clusters. However, this same model applied to a network with highly heterogeneous cluster sizes leads to a clear breakdown of the balanced state. In this talk, using a formal definition of the balance matrix, we show why the balance condition breaks and propose a solution to restore balance in the heterogeneous networks. In doing so, we also observe that global synchronization in firing dynamics appears due to cluster size heterogeneity. This modified balance matrix applied to a homogeneous network results in the disappearance of the synchronization. Diversity in cluster sizes has not previously been shown to produce such a global effect in a network of excitatory and inhibitory leaky integrate and fire neurons, and may have important implications in real world cortical networks. |
Thursday, March 7, 2019 9:00AM - 9:12AM |
R67.00006: Statistical properties of the optimal sensitivity matrix for compressed sensing with nonlinear sensors Shanshan Qin, Qianyi Li, Chao Tang, Yuhai Tu Natural odors are typically sparse mixtures of a few types of odorants each with a wide range of concentrations. How to encode a large number of sparse odor mixtures with a relatively small number of nonlinear olfactory receptor neurons (ORNs) – the nonlinear compressed sensing problem – remains a puzzle. Here, by using an information theory approach, we study the optimal coding strategies that enable ORNs to best represent olfactory information. Our results show that the optimal odor-receptor sensitivity matrix is sparse and the nonzero sensitivities follow roughly a log-normal distribution, both of which are consistent with existing experiments. We also show that odor-evoked inhibition increases coding capacity, providing a plausible explanation for experimental observation in the fly olfactory system. Furthermore, we show that the optimal sensitivity matrix can enhance accuracy of the downstream decoding tasks. Our results may shed light on understanding the peripheral olfactory sensory system and improving performance of artificial neural networks. |
Thursday, March 7, 2019 9:12AM - 9:24AM |
R67.00007: Biologically-plausible neural models for time-series clustering Tiberiu Tesileanu, Dmitri Chklovskii, Anirvan M Sengupta The brain processes sensory data in an online fashion, incorporating information as it is being received. Typically this data does not come in independent samples, but follows some stochastic dynamics with parameters that are subject to change. For instance, the output of an olfactory sensory neuron might fluctuate around a mean that varies depending on the relative motion between the animal and an odor source. An important problem in this context is to find when a change in the dynamics occurs. In biological terms, this might correspond to a significant change in the environment that requires an animal to change its behavior. Here we build biologically-plausible algorithms for performing online clustering of time series data. These models can be implemented as neural networks with local learning rules, and can be derived from principled objective functions. We test these algorithms on data generated from several alternating autoregressive-moving-average (ARMA) models and find very good performance in detecting changes in the generating process within just dozens of samples from the transition point. We compare this to similar methods from control theory that are not directly interpretable as neural models. |
Thursday, March 7, 2019 9:24AM - 9:36AM |
R67.00008: Chaotic itinerancy in reservoir computing Hiromichi Suetani Recently, the paradigm of reservoir computing (RC) has attracted attention as a new way of recurrent neural network (RNN) training [1]. Especially, Sussillo and Abbott proposed a version of RC, called FORCE-learning[2] and they showed how chaotic activity in a RNN is useful for generating various temporal patterns. |
Thursday, March 7, 2019 9:36AM - 9:48AM |
R67.00009: The role of neural excitability and coupling in the formation of social bonds Itai Pinkoviezky, Ahmed Roman, Elizabeth Amadei, Robert C Liu, Gordon Berman Social behavior is an important aspect of life for humans and many other animals. Yet, our understanding of the biological mechanisms giving rise to it is limited. Although extensive research has been done on the neural basis of social impairment, few studies have attempted to explain the formation of positive behaviors. Here, we report results on the mechanisms responsible for a positive social behavior, pair bonding in prarie voles (Microtus ochrogaster). Key brain areas in this process are the medial prefrontal cortex (mPFC) and the nucleus accumbens (NAcc). It was shown that the phase-amplitude-coupling (PAC) between mPFC drive to NAcc is pivotal for the formation of a bond in a voles pair. Experimental measurements from NAcc suggest non-trivial relations between the input to the network and the resulting PAC features. Using simulations and analytical methods we study the emerging coupling in networks of model neurons driven by oscillatory input. Our results show that changes in the input to the network and in the excitability of the neurons play an important role in the bond formation process. Furthermore, our results suggest the role of oxytocin, a neurotransmitter often associated with social behavior, in pair bonding is to modulate the excitability of certain neurons. |
Thursday, March 7, 2019 9:48AM - 10:00AM |
R67.00010: Precise Spatial Memory in Local Random Networks Joseph Natale, H G E Hentschel, Ilya Nemenman Self-sustained, elevated neuronal activity persisting on time scales of ten seconds or longer is vital for working memory. The most prevalent models for persistent activity, known as attractor networks, have come under criticism for their severe reliance on fine-tuning of synaptic architectures. While alternative frameworks exist, many of these invoke fine-tuning implicitly. Here we elaborate a model with local connectivity which, when combined with a global regulation of the mean firing rate, produces localized, finely spaced discrete attractors that persist in time and effectively span a planar manifold. Synaptic strengths are drawn randomly, so that the model remains minimally structured, requires no training or low-level fine-tuning to store memories, and may be of interest in modeling such biological phenomena as visuospatial working memory in two dimensions. |
Thursday, March 7, 2019 10:00AM - 10:12AM |
R67.00011: A physical model for pattern completion of highly overlapping patterns for Human Episodic Memory Zahra Ghasemi Esfahani, Marc Howard Patterns of neural activity in the hippocampus change very slowly, perhaps in a scale-invariant manner. |
Thursday, March 7, 2019 10:12AM - 10:24AM |
R67.00012: Novel strategies for holographic optical activation of neurons Samira Aghayee, Patrick Kanold, Wolfgang Losert In-vivo two photon imaging of neuronal activity in the higher sensory cortical layers has revealed that the spatial arrangements of neurons involved in processing sensory stimuli is complex. Recent advances in optogenetics have allowed for optical manipulation of neurons, and holographic microscopy has enabled us to selectively target neurons for activation. Here we introduce the use of extended holographic patterns with high planar selectivity for photo activation of neurons. Our approach takes advantage of a lens function to reduce off-target speckles. We find that in addition to reducing speckles, our approach also generates target patterns with a higher degree of uniformity. We demonstrate the feasibility of this method for in vivo studies of the auditory cortex. |
Thursday, March 7, 2019 10:24AM - 10:36AM |
R67.00013: Wiring economy constrains olfactory glomerulus placement in fly larva Guangwei Si, Matthew E Berck, Yu Feng, Aravinthan Samuel Olfactory sensory neurons expressing the same receptor converge their axons to a common locus called a glomerulus. The glomeruli placement are largely stereotyped. A plausible theory to explain the placement is the wiring economy principle, where neuronal placement is an optimal solution to minimizing the wiring cost given a synaptic connectivity pattern. Recently, the complete wiring diagram of a glomerular olfactory system, the insect antennal lobe, has been reconstructed using serial section electron microscopy and the fly larva. The reconstruction provides a detailed map of glomerulus placement as well as the underlying synaptic connectivity. Interneuron synaptic connectivity patterns within the antennal lobe are strikingly and consistently heterogeneous in their glomerular innervations. We developed a coarse-grained model to describe the glomeruli placement and calculate the wiring cost to generate this synaptic connectivity. The wiring cost of observed glomerular position is significantly smaller than random glomerular arrangements. We also searched for the theoretical minimal wiring cost, which compares well with the observed glomerular arrangement. Wiring economy provides an explanation in terms of physical optimization for the organization of an olfactory system. |
Thursday, March 7, 2019 10:36AM - 10:48AM |
R67.00014: Generation of scale-invariant sequential activity in recurrent neural circuits Yue Liu, Marc W Howard Sequential neural activity has been observed in many parts of the brain. Sequential activity can be generated by recurrent neural networks, which have been extensively studied (White and Sompolinsky, 1996, Goldman, 2009, Rajan et al., 2016, Chaudhuri et al., 2016). |
Thursday, March 7, 2019 10:48AM - 11:00AM |
R67.00015: Exploring the energy landscape of C. elegans neural activities Xiaowen Chen, Francesco Randi, Andrew M Leifer, William Bialek Recent advances in experimental techniques and application of the maximum entropy principle have allowed us to build models for joint probability distribution of activity in groups of up to 50 neurons in Caenorhabditis elegans, a nematode with 302 neurons. These models, which are equivalent to the Boltzmann distribution for a family of Potts glasses, successfully predict the static observables of the network. The energy landscape defined by these models exhibits curious signatures of collective behavior, including a large number of energy minima, as in models for memory, and a clustering of energy barriers that is reminiscent of the dynamical transitions in disordered systems. While these models describe the distribution of network states at a single time, the observed neural dynamics are not consistent with a simple Brownian-like motion on the energy landscape. In particular, the real dynamics exhibit much longer correlation times than predicted from the heights of energy barriers alone. We will show progress towards understanding how the nematode actually explores the energy landscape of its neural network. |
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