Evolution of recollection and prediction in neural networks

Abstract — A large number of neural network models are based on a feedforward topology (perceptrons, backpropagation networks, radial basis functions, support vector machines, etc.), thus lacking dynamics. In such networks, the order of input presentation is meaningless (i.e., it does not affect the behavior) since the behavior is largely reactive. That is, such neural networks can only operate in the present, having no access to the past or the future. However, biological neural networks are mostly constructed with a recurrent topology, and recurrent (artificial) neural network models are able to exhibit rich temporal dynamics, thus time becomes an essential factor in their operation. In this paper, we will investigate the emergence of recollection and prediction in evolving neural networks. First, we will show how reactive, feedforward networks can evolve a memory-like function (recollection) through utilizing external markers dropped and detected in the environment. Second, we will investigate how recurrent networks with more predictable internal state trajectory can emerge as an eventual winner in evolutionary struggle when competing networks with less predictable trajectory show the same level of behavioral performance. We expect our results to help us better understand the evolutionary origin of recollection and prediction in neuronal networks, and better appreciate the role of time in brain function. I

Evolution of Memory in Reactive Artificial Neural Networks

In the neuronal circuits of natural and artificial agents, memory is usually implemented with recurrent connections, since recurrence allows past agent state to affect the present, on-going behavior. Here, an interesting question arises in the context of evolution: how reactive agents could have evolved into cognitive ones with internalized memory? This study strives to find an answer to the question by simulating neuroevolution on artificial neural networks, with the hypothesis that internalization of external material interaction can be a plausible evolutionary path leading to a fully internalized memory system. A series of computational experiments were performed to gradually verify the above hypothesis. The first experiment demonstrated the possibility that external materials can be used as memory-aids for a memoryless reactive artificial agents in a simple 1-dimensional environment. Here, the reactive artificial agents used environmental markers as memory references to be successful in the ball-catching task that requires memory. Motivated by the result of the first experiment, an extended experiment was conducted to tackle a more complex memory problem using the same principle of external material interaction. This time, the reactive artificial agents are tasked to remember the locations of food items and the nest in a 2-dimensional environment. Such path-following behavior is a trivial foraging strategy of various lower animals such as ants and fish. The final experiment was designed to show the evolution of internal recurrence. In this experiment, I showed the evolutionary advantage of external material interaction by comparing the results from neural network topology evolution algorithms with and without the material interaction mechanism. The result confirmed that the agents with external material interaction learned to solve the memory task faster and more accurately. The results of the experiments provide insights on the possible evolutionary route to an internalized memory. The use of external material interaction can help reactive artificial agents to go beyond the functionality restricted by their simple network structure. Moreover, it allows much faster convergence with higher accuracy than the topological evolution of the artificial agents. These results suggest one plausible evolutionary path from reactive, through external material interaction, to recurrent structure

Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM). KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). KESM data can contribute greatly to connectomics research, since they fill the gap between lower resolution, large volume imaging methods (such as diffusion MRI) and higher resolution, small volume methods (e.g., serial sectioning electron microscopy). Furthermore, KESM data are by their nature multiscale, ranging from the subcellular to the whole organ scale. Due to this, visualization alone is a huge challenge, before we even start worrying about quantitative connectivity analysis. To solve this issue, we developed a web-based neuroinformatics framework for efficient visualization and analysis of the multiscale KESM data sets. In this paper, we will first provide an overview of KESM, then discuss in detail the KESM data sets and the web-based neuroinformatics framework, which is called the KESM brain atlas (KESMBA). Finally, we will discuss the relevance of the KESMBA to connectomics research, and identify challenges and future directions

Specimen Preparation, Imaging, and Analysis Protocols for Knife-edge Scanning Microscopy

Major advances in high-throughput, high-resolution, 3D microscopy techniques have enabled the acquisition of large volumes of neuroanatomical data at submicrometer resolution. One of the first such instruments producing whole-brain-scale data is the Knife-Edge Scanning Microscope (KESM)7, 5, 9, developed and hosted in the authors' lab. KESM has been used to section and image whole mouse brains at submicrometer resolution, revealing the intricate details of the neuronal networks (Golgi)1, 4, 8, vascular networks (India ink)1, 4, and cell body distribution (Nissl)3. The use of KESM is not restricted to the mouse nor the brain. We have successfully imaged the octopus brain6, mouse lung, and rat brain. We are currently working on whole zebra fish embryos. Data like these can greatly contribute to connectomics research10; to microcirculation and hemodynamic research; and to stereology research by providing an exact ground-truth

Lower leg compartment syndrome following prolonged orthopedic surgery in the lithotomy position -A case report-

Surgical procedures necessitating the prolonged use of the lithotomy position can be associated with neuromuscular dysfunction. Compartment syndrome of the lower leg is a grave complication which, if unrecognized, can lead to either permanent neuromuscular dysfunction or limb loss. We report a case of compartment syndrome of lower leg that occurred in male patient aged 20 years after 380 minutes arthroscopic surgery in the lithotomy position

Cross-National Differences in Victimization : Disentangling the Impact of Composition and Context

Varying rates of criminal victimization across countries are assumed to be the outcome of countrylevel structural constraints that determine the supply ofmotivated o¡enders, as well as the differential composition within countries of suitable targets and capable guardianship. However, previous empirical tests of these ‘compositional’ and ‘contextual’ explanations of cross-national di¡erences have been performed upon macro-level crime data due to the unavailability of comparable individual-level data across countries. This limitation has had two important consequences for cross-national crime research. First, micro-/meso-level mechanisms underlying cross-national differences cannot be truly inferred from macro-level data. Secondly, the e¡ects of contextual measures (e.g. income inequality) on crime are uncontrolled for compositional heterogeneity. In this paper, these limitations are overcome by analysing individual-level victimization data across 18 countries from the International CrimeVictims Survey. Results from multi-level analyses on theft and violent victimization indicate that the national level of income inequality is positively related to risk, independent of compositional (i.e. micro- and meso-level) di¡erences. Furthermore, crossnational variation in victimization rates is not only shaped by di¡erences in national context, but also by varying composition. More speci¢cally, countries had higher crime rates the more they consisted of urban residents and regions with lowaverage social cohesion.

Global Norms, Local Activism, and Social Movement Outcomes: Global Human Rights and Resident Koreans in Japan

The authors integrate social movement outcomes research and the world society approach to build a theoretical model to examine the impact of global and local factors on movement outcomes. Challenging the current research on policy change, which rarely examines the effects of global norms and local activism in one analysis, they argue (1) that global regimes empower and embolden local social movements and increase pressure on target governments from below, and (2) that local activists appeal to international forums with help from international activists to pressure the governments from above. When the pressures from the top and the bottom converge, social movements are more likely to succeed. Furthermore, these pressures are stronger in countries integrated into global society and on issues with strong global norms. The empirical analysis of social movements by resident Koreans in Japan advocating for four types of human rights—civil, political, social/economic, and cultural—demonstrates that the movements produced more successes as Japan\u27s involvement in the international human rights regime expanded since the late 1970s, and that activism on issues with strong global norms achieved greater successes. The analysis also shows that lack of cohesive domestic activism can undercut the chances of social movements\u27 success even with strong global norms on the issue

The JCMT BISTRO Survey: A Spiral Magnetic Field in a Hub-filament Structure, Monoceros R2

We present and analyze observations of polarized dust emission at 850 μm toward the central 1 7 1 pc hub-filament structure of Monoceros R2 (Mon R2). The data are obtained with SCUBA-2/POL-2 on the James Clerk Maxwell Telescope (JCMT) as part of the B-fields in Star-forming Region Observations survey. The orientations of the magnetic field follow the spiral structure of Mon R2, which are well described by an axisymmetric magnetic field model. We estimate the turbulent component of the magnetic field using the angle difference between our observations and the best-fit model of the underlying large-scale mean magnetic field. This estimate is used to calculate the magnetic field strength using the Davis–Chandrasekhar–Fermi method, for which we also obtain the distribution of volume density and velocity dispersion using a column density map derived from Herschel data and the C18O (J = 3 - 2) data taken with HARP on the JCMT, respectively. We make maps of magnetic field strengths and mass-to-flux ratios, finding that magnetic field strengths vary from 0.02 to 3.64 mG with a mean value of 1.0 \ub1 0.06 mG, and the mean critical mass-to-flux ratio is 0.47 \ub1 0.02. Additionally, the mean Alfv\ue9n Mach number is 0.35 \ub1 0.01. This suggests that, in Mon R2, the magnetic fields provide resistance against large-scale gravitational collapse, and the magnetic pressure exceeds the turbulent pressure. We also investigate the properties of each filament in Mon R2. Most of the filaments are aligned along the magnetic field direction and are magnetically subcritical