Latent dynamical systems have been widely used to characterize the dynamics of neural population activity in the brain. However, these models typically ignore the fact that the brain contains multiple cell types. This limits their ability to capture the functional roles of distinct cell classes, and to predict the effects of cell-specific perturbations on neural activity or behavior. To overcome these limitations, we introduce the `"cell-type dynamical systems" (CTDS) model. This model extends latent linear dynamical systems to contain distinct latent variables for each cell class, with biologically inspired constraints on both dynamics and emissions. To illustrate our approach, we consider neural recordings with distinct excitatory (E) and inhibitory (I) populations. The CTDS model defines separate latents for both cell types, and constrains the dynamics so that E (I) latents have a strictly positive (negative) effects on other latents. We applied CTDS to recordings from rat frontal orienting fields (FOF) and anterior dorsal striatum (ADS) during an auditory decision-making task. The model achieved higher accuracy than a standard linear dynamical system (LDS), and revealed that the animal's choice can be decoded from both E and I latents and thus is not restricted to a single cell-class. We also performed in-silico optogenetic perturbation experiments in the FOF and ADS, and found that CTDS was able to replicate the experimentally observed effects of different perturbations on behavior, whereas a standard LDS model---which does not differentiate between cell types---did not. Crucially, our model allowed us to understand the effects of these perturbations by revealing the dynamics of different cell-specific latents. Finally, CTDS can also be used to identify cell types for neurons whose class labels are unknown in electrophysiological recordings. These results illustrate the power of the CTDS model to provide more accurate and more biologically interpretable descriptions of neural population dynamics and their relationship to behavior.

A Sybil attack occurs when an adversary pretends to be multiple identities (IDs). Limiting the number of Sybil (bad) IDs to a minority is critical to the use of well-established tools for tolerating malicious behavior, such as Byzantine agreement and secure multiparty computation. A popular technique for enforcing a Sybil minority is resource burning: verifiable consumption of a network resource, such as computational power, bandwidth, or memory. Unfortunately, typical defenses based on resource burning require non-Sybil (good) IDs to consume at least as many resources as the adversary. Additionally, they have a high cost, even when the system membership is relatively stable. Here, we present a new Sybil defense, ERGO, that guarantees (1) there is always a minority of Sybil IDs; and (2) when the system is under significant attack, the good IDs consume asymptotically less than the bad. In particular, for churn rate that can vary exponentially, the resource burning rate of ERGO is, where is the resource burning rate of the adversary, and is the join rate of good IDs. We empirically evaluate ERGO alongside prior Sybil defenses. Unlike other Sybil defense, ERGO can be combined with machine learning techniques for identifying Sybil IDs, in a way that maintains its theoretical guarantees. Based on our experiments comparing ERGO with two state-of-the-art Sybil defenses, we show that ERGO improves by up to 2 orders of magnitude without machine learning, and up to 3 orders of magnitude using machine learning.

A common tool to defend against Sybil attacks is proof-of-work, whereby computational puzzles are used to limit the number of Sybil participants. Unfortunately, current Sybil defenses require significant computational effort to offset an attack. In particular, good participants must spend computationally at a rate that is proportional to the spending rate of an attacker. In this paper, we present the first Sybil defense algorithm which is asymmetric in the sense that good participants spend at a rate that is asymptotically less than an attacker. In particular, if T is the rate of the attacker's spending, and J is the rate of joining good participants, then our algorithm spends at a rate f O(√(TJ) + J). We provide empirical evidence that our algorithm can be significantly more efficient than previous defenses under various attack scenarios. Additionally, we prove a lower bound showing that our algorithm's spending rate is asymptotically optimal among a large family of algorithms.

Central-place foraging (CPF) is a canonical task in collective robotics with applications to planetary exploration, automated mining, warehousing, and search and rescue operations. We compare the performance of three Central-Place Foraging Algorithms (CPFAs), variants of which have been shown to work well in real robots: spiral-based, rotating-spoke, and random-ballistic. To understand the difference in performance between these CPFAs, we define the price of ignorance and show how this metric explains our previously published empirical results. We obtain upper-bounds for expected complete collection times for each algorithm and evaluate their performance in simulation. We show that site-fidelity (i.e. returning to the location of the last found target) and avoiding search redundancy are key-factors that determine the efficiency of CPFAs. Our formal analysis suggests the following efficiency ranking from best to worst: spiral, spoke, and the stochastic ballistic algorithm.

A key factor limiting the performance of central place foraging algorithms is the awareness of the agent(s) about the location of food items around the nest. We study the ratio of how much time an ignorant agent takes relative to an omniscient forager for complete collection of food items in the arena. This effectively quantifies the penalty each algorithm pays for not knowing (or choosing to ignore information gained about) where the resources are located. We model the effect of depletion of food items from the arena on the foraging efficiency over time and analytically verify that returning to the location of the last food item found strongly helps in counteracting this effect. To the best of our knowledge, these results have only been empirically argued so far.