Firing rate models are dynamical systems widely used in applied and theoretical neuroscience to describe local cortical dynamics in neuronal populations. By providing a macroscopic perspective of neuronal activity, these models are essential for investigating oscillatory phenomena, chaotic behavior, and associative memory processes. Despite their widespread use, the application of firing rate models to associative memory networks has received limited mathematical exploration, and most existing studies are focused on specific models. Conversely, well-established associative memory designs, such as Hopfield networks, lack key biologically relevant features intrinsic to firing rate models, including positivity and interpretable synaptic matrices reflecting the action of long-term potentiation and long-term depression. To address this gap, we propose a general framework that ensures the emergence of rescaled memory patterns as stable equilibria in the firing rate dynamics. Furthermore, we analyze the conditions under which the memories are locally and globally asymptotically stable, providing insights into constructing biologically plausible and robust systems for associative memory retrieval.

In this paper, we consider the problem of estimating the state of a dynamical system from distributed noisy measurements. Each agent constructs a local estimate based on its own measurements and on the estimates from its neighbors. Estimation is performed via a two stage strategy, the first being a Kalman-like measurement update which does not require communication, and the second being an estimate fusion using a consensus matrix. In particular we study the interaction between the consensus matrix, the number of messages exchanged per sampling time, and the Kalman gain for scalar systems. We prove that optimizing the consensus matrix for fastest convergence and using the centralized optimal gain is not necessarily the optimal strategy if the number of exchanged messages per sampling time is small. Moreover, we show that although the joint optimization of the consensus matrix and the Kalman gain is in general a non-convex problem, it is possible to compute them under some relevant scenarios. We also provide some numerical examples to clarify some of the analytical results and compare them with alternative estimation strategies.

Various randomized consensus algorithms have been proposed in the literature. In some case randomness is due to the choice of a randomized network communication protocol. In other cases, randomness is simply caused by the potential unpredictability of the environment in which the distributed consensus algorithm is implemented. Conditions ensuring the convergence of these algorithms have already been proposed in the literature. As far as the rate of convergence of such algorithms, two approaches can be proposed. One is based on a mean square analysis, while a second is based on the concept of Lyapunov exponent. In this paper, by some concentration results, we prove that the mean square convergence analysis is the right approach when the number of agents is large. Differently from the existing literature, in this paper we do not stick to average preserving algorithms. Instead, we allow to reach consensus at a point which may differ from the average of the initial states. The advantage of such algorithms is that they do not require bidirectional communication among agents and thus they apply to more general contexts. Moreover, in many important contexts it is possible to prove that the displacement from the initial average tends to zero, when the number of agents goes to infinity.

The emerging area of control with limited data rates incorporates ideas from both control and information theory. The data rate constraint introduces quantization into the feedback loop and gives the interconnected system a two-fold nature, continuous and symbolic. In this paper, we review the results available in the literature on data-rate-limited control. For linear systems, we show how fundamental tradeoffs between the data rate and control goals, such as stability, mean entry times, and asymptotic state norms, emerge naturally. While many classical tools from both control and information theory can still be used in this context, it turns out that the deepest results necessitate a novel, integrated view of both disciplines.

Convolutional codes over rings are particularly suitable for representing codes over phase-modulation signals. In order to develop a complete structural analysis of this class of codes, it is necessary to study rational matrices over rings, which constitutes the generator matrices (encoders) for such convolutional codes. Noncatastrophic, minimal, systematic, and basic generator matrices are introduced and characterized by using a canonical form for polynomial matrices over rings. Finally, some classes of convolutional codes, defined according to the generator matrix they admit, are introduced and analyzed from a system-theoretic point of view.

Given a controllable group code, it has been shown by Forney (1970, 1973), Trott, and Loeliger (1994) that it is possible to construct a canonical encoder whose state space coincides with the canonical state space of the code, that is the essential element determining the minimal trellis of the code. The construction of such an encoder is based on the concept of controllability granule. In this paper the construction of a syndrome former for an observable group code is proposed. This syndrome former exhibits analogous properties of the above mentioned canonical encoder and, in particular, its state space coincides with the canonical state space of the code. The proposed construction is based on the concept of observability granule, introduced by Forney and Trott (1993), which dualizes the concept of controllability granule. Similarly to what happens for the encoders, each observability granule produces a map and all these maps together provide the syndrome former. The main difference is that here, to achieve the right state-space dimension, the automata associated with these maps do not evolve independently from each other, but are coupled according to a triangular structure.

Polynomial algebraic techniques have always played a central role in linear systems theory and also in the theory of convolutional codes. We show how such techniques can be generalized to study systems and codes defined over Abelian groups. The systems are considered from the "behavioral" point of view as developed by Willems in the 1980s, and some of our results can be seen as extensions of Willems' results to group systems. We also address a certain number of coding-oriented questions, and we propose concrete methods based on these algebraic techniques for the synthesis of encoders, inverters, and syndrome formers for codes over finite Abelian groups.