Neuronal assembly dynamics in supervised and unsupervised learning scenarios

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions

Insular carnivore biogeography: island area and mammalian optimal body size.

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A Moving Bump in a Continuous Manifold: A Comprehensive Study of the Tracking Dynamics of Continuous Attractor Neural Networks

Understanding how the dynamics of a neural network is shaped by the network structure, and consequently how the network structure facilitates the functions implemented by the neural system, is at the core of using mathematical models to elucidate brain functions. This study investigates the tracking dynamics of continuous attractor neural networks (CANNs). Due to the translational invariance of neuronal recurrent interactions, CANNs can hold a continuous family of stationary states. They form a continuous manifold in which the neural system is neutrally stable. We systematically explore how this property facilitates the tracking performance of a CANN, which is believed to have clear correspondence with brain functions. By using the wave functions of the quantum harmonic oscillator as the basis, we demonstrate how the dynamics of a CANN is decomposed into different motion modes, corresponding to distortions in the amplitude, position, width or skewness of the network state. We then develop a perturbative approach that utilizes the dominating movement of the network's stationary states in the state space. This method allows us to approximate the network dynamics up to an arbitrary accuracy depending on the order of perturbation used. We quantify the distortions of a Gaussian bump during tracking, and study their effects on the tracking performance. Results are obtained on the maximum speed for a moving stimulus to be trackable and the reaction time for the network to catch up with an abrupt change in the stimulus.Comment: 43 pages, 10 figure

Internal Representation of Task Rules by Recurrent Dynamics: The Importance of the Diversity of Neural Responses

Neural activity of behaving animals, especially in the prefrontal cortex, is highly heterogeneous, with selective responses to diverse aspects of the executed task. We propose a general model of recurrent neural networks that perform complex rule-based tasks, and we show that the diversity of neuronal responses plays a fundamental role when the behavioral responses are context-dependent. Specifically, we found that when the inner mental states encoding the task rules are represented by stable patterns of neural activity (attractors of the neural dynamics), the neurons must be selective for combinations of sensory stimuli and inner mental states. Such mixed selectivity is easily obtained by neurons that connect with random synaptic strengths both to the recurrent network and to neurons encoding sensory inputs. The number of randomly connected neurons needed to solve a task is on average only three times as large as the number of neurons needed in a network designed ad hoc. Moreover, the number of needed neurons grows only linearly with the number of task-relevant events and mental states, provided that each neuron responds to a large proportion of events (dense/distributed coding). A biologically realistic implementation of the model captures several aspects of the activity recorded from monkeys performing context-dependent tasks. Our findings explain the importance of the diversity of neural responses and provide us with simple and general principles for designing attractor neural networks that perform complex computation

Learning by message-passing in networks of discrete synapses

We show that a message-passing process allows to store in binary "material" synapses a number of random patterns which almost saturates the information theoretic bounds. We apply the learning algorithm to networks characterized by a wide range of different connection topologies and of size comparable with that of biological systems (e.g. $n\simeq10^{5}-10^{6}$). The algorithm can be turned into an on-line --fault tolerant-- learning protocol of potential interest in modeling aspects of synaptic plasticity and in building neuromorphic devices.Comment: 4 pages, 3 figures; references updated and minor corrections; accepted in PR

Current state of antimicrobial stewardship in children’s hospital emergency departments

BACKGROUND Antimicrobial stewardship programs (ASPs) effectively optimize antibiotic use for inpatients; however, the extent of emergency department (ED) involvement in ASPs has not been described. OBJECTIVE To determine current ED involvement in children's hospital ASPs and to assess beliefs and preferred methods of implementation for ED-based ASPs. METHODS A cross-sectional survey of 37 children's hospitals participating in the Sharing Antimicrobial Resistance Practices collaboration was conducted. Surveys were distributed to ASP leaders and ED medical directors at each institution. Items assessed included beliefs regarding ED antibiotic prescribing, ED prescribing resources, ASP methods used in the ED such as clinical decision support and clinical care guidelines, ED participation in ASP activities, and preferred methods for ED-based ASP implementation. RESULTS A total of 36 ASP leaders (97.3%) and 32 ED directors (86.5%) responded; the overall response rate was 91.9%. Most ASP leaders (97.8%) and ED directors (93.7%) agreed that creation of ED-based ASPs was necessary. ED resources for antibiotic prescribing were obtained via the Internet or electronic health records (EHRs) for 29 hospitals (81.3%). The main ASP activities for the ED included production of antibiograms (77.8%) and creation of clinical care guidelines for pneumonia (83.3%). The ED was represented on 3 hospital ASP committees (8.3%). No hospital ASPs actively monitored outpatient ED prescribing. Most ASP leaders (77.8%) and ED directors (81.3%) preferred implementation of ED-based ASPs using clinical decision support integrated into the EHR. CONCLUSIONS Although ED involvement in ASPs is limited, both ASP and ED leaders believe that ED-based ASPs are necessary. Many children's hospitals have the capability to implement ED-based ASPs via the preferred method: EHR clinical decision support. Infect Control Hosp Epidemiol 2017;38:469-475