Power laws, memory capacity, and self-tuned critical branching in an LIF model with binary synapses

Both fluctuations and distributions of spontaneous neural spiking activity have been observed to closely follow a variety of power laws. Multiple explanations have been offered for each observation, but few lead to mechanisms that encompass their widespread occurrence. A canonical, leaky integrate-and-fire model is presented in which synapses are updated based on the timing of pre- and post-synaptic spikes in order to maintain a state of critical branching. Results showed that 1) the self-tuning algorithm maintained critical branching under a range of parameters; 2) power laws were obtained in spiking activity fluctuations (1/f scaling), size distributions of network bursts (neural avalanches), and temporal correlations in interspike intervals (Allan factor); 3) power laws disappeared once the self-tuning algorithm was disabled; and 4) critical branching was adaptive in that it maximized the network’s memory capacity when assessed as a liquid state machine

Academic Placement Data and Analysis: 2016 Final Report

Academic Placement Data and Analysis (APDA), a project funded by the American Philosophical Association (APA) and headed by Carolyn Dicey Jennings (UC Merced), aims “to make information on academic job placement useful to prospective graduate students in philosophy.” The project has just been updated to include new data, which Professor Jennings describes in a post at New APPS. She also announces a new interactive data tool with which one can sift through and sort information. (from Daily Nous

The Role of Spatial Structure and Memory in Human Foraging

Foraging is an essential process for all mobile organisms. It allows organisms to locate resources such as food and mates. There is a long history of research on animal foraging in the ecology literature and recent work in cognitive science has revealed similarities between cognitive search behaviors and animal foraging behaviors. This gives rise to the possibility of bringing the rich animal foraging literature to bear on cognitive search processes. Historically there have been several major approaches to the study and modeling of foraging animals. One approach is known as optimal foraging theory which is focused on optimizing the amount of time an organism spends foraging a single location before moving on to another location. Another is an expansion on that approach which operationalizes those ideas into a spatial model known as area-restricted search. A third approach is known as the Lévy flight hypothesis. It focuses on the longer term distributional properties of foraging animals, and optimizes coverage of a given search space. These approaches all make assumptions about environmental conditions faced by foraging organisms. The degree of resource sparsity and clustering in a foraging environment are believed to be important, but it is unclear how they affect foraging behaviors. Additionally spatial memory is a key concept important to animal search strategies, but is frequently ignored in the existing literature. For this dissertation, a series of experiments utilized a web-based foraging game to test how these variables affect foraging behaviors. The first experiment demonstrated that the degree of clustering in the environment had a significant impact on search strategies, and provided qualitative evidence that memory played a role in people’s search behaviors. The experiments revealed distributional patterns very similar to those predicted by the Lévy flight approach. The second experiment refined the method and directly tested memory cues and a broader range of resource densities. This experiment revealed results similar to the first with the addition of significant effects of both memory cues and resource density. This dissertation then discusses a model that combined key concepts from both optimal foraging and Lévy foraging to produce results very similar to those produced by human participants, but with significantly higher performance. Experiment 3 examined how human performance changes when specific advantages are provided that can be found in our model, including perfect memory and accuracy. Finally a continuation of the model is discussed that explores the dynamics of multiple foragers searching the same space. Overall, I demonstrate that people will generate Lévy-like search distributions in a wide variety of environmental conditions, but that search strategies will alter based on the current environment. I also demonstrate that spatial memory is a key factor in foraging, and provide a simple memory-based model that produces foraging behavior very similar to those utilized by people

Critical Branching Neural Computation, Neural Avalanches, and 1/f Scaling

It is now well-established that intrinsic fluctuations in human behavior tend to exhibit long-range correlations in the form of 1/f scaling. Their meaning is an ongoing matter of debate, and some researchers argue they reflect the tendency for neural and bodily systems to poise themselves near critical states. A spiking neural network model is presented that self-tunes to a critical point in terms of its spike branching ratio (i.e. critical branching). The model is shown to exhibit 1/f scaling near critical branching, as well neural avalanches, and critical branching is associated with maximal computational capacity when assessed in terms of reservoir computing. The model provides a basis for connecting neural and behavioral activity and function via criticality

Distributional and Temporal Properties of Eye Movement Trajectories in Scene Perception

Eye movements gather visual information from the environment for various purposes and goals. Spatial patterns of eye movements vary depending on the layout of visual information, and intentions of the observer. However, despite this variability, basic principles of visual information gathering may be reflected in lawful properties of eye movement trajectories that hold across various stimulus and intentional conditions. Two experiments are presented analyzing eye movement trajectories during scene perception across pictures with varying spatial frequency distributions (Expt 1), and across two different task conditions, "finding" versus "counting " tasks (Expt 2). Results show that, in all conditions, distributions of saccade amplitudes are heavytailed and nearly identical in shape, and fixation fluctuation series are long-range correlated with nearly identical spectral slopes. While a small effect of task intention was found, the broader conclusion is that eye movements during scene perception exhibit general statistical characteristics that models have yet to address