Jose A. Fernandez

A current challenge in neuroscience and systems biology is to better understand properties that allow organisms to exhibit and sustain appropriate behaviours despite the effects of perturbations (behavioural robustness). There are still significant theoretical difficulties in this endeavour, mainly due to the context-dependent nature of the problem. Biological robustness, in general, is considered in the literature as a property that emerges from the internal structure of organisms, rather than being a dynamical phenomenon involving agent-internal controls, the organism body, and the environment. Our hypothesis is that the capacity for behavioural robustness is rooted in dynamical processes that are distributed between agent ‘brain’, body, and environment, rather than warranted exclusively by organisms’ internal mechanisms. Distribution is operationally defined here based on perturbation analyses. Evolutionary Robotics (ER) techniques are used here to construct four computational models to study behavioural robustness from a systemic perspective. Dynamical systems theory provides the conceptual framework for these investigations. The first model evolves situated agents in a goal-seeking scenario in the presence of neural noise perturbations. Results suggest that evolution implicitly selects neural systems that are noise-resistant during coupling behaviour by concentrating search in regions of the fitness landscape that retain functionality for goal approaching.The second model evolves situated, dynamically limited agents exhibiting minimal cognitive behaviour (categorization task). Results indicate a small but significant tendency toward better performance under most types of perturbations by agents showing further cognitive behavioural dependency on their environments. The third model evolves experience-dependent robust behaviour in embodied, one-legged walking agents. Evidence suggests that robustness is rooted in both internal and external dynamics, but robust motion emerges always from the system-in-coupling. The fourth model implements a historically dependent, mobile-object tracking task under sensorimotor perturbations. Results indicate two different modes of distribution, one in which inner controls necessarily depend on a set of specific environmental factors to exhibit behaviour, then these controls will be more vulnerable to perturbations on that set, and another for which these factors are equally sufficient for behaviours. Vulnerability to perturbations depends on the particular distribution. In contrast to most existing approaches to the study of robustness, this thesis argues that behavioural robustness is better understood in the context of agent-environment dynamical couplings, not in terms of internal mechanisms. Such couplings, however, are not always the full determinants of robustness. Challenges and limitations of our approach are also identified for future studies.

Natural viewing often consists of sequences of brief fixations to image patches of different structure. Whether and how
briefly presented sequential stimuli are encoded in a temporal-position manner is poorly understood. Here, we performed
multiple-electrode recordings in the visual cortex (area V4) of nonhuman primates (Macaca mulatta) viewing a sequence of 7
briefly flashed natural images, and measured correlations between the cue-triggered population response in the presence
and absence of the stimulus. Surprisingly, we found significant correlations for images occurring at the beginning and the
end of a sequence, but not for those in the middle. The correlation strength increased with stimulus exposure and favored
the image position in the sequence rather than image identity. These results challenge the commonly held view that
images are represented in visual cortex exclusively based on their informational content, and indicate that, in the absence
of sensory information, neuronal populations exhibit reactivation of stimulus-evoked responses in a way that reflects
temporal position within a stimulus sequence.

Objective. Studying the brain in large animal models in a restrained laboratory rig severely limits our capacity to examine brain circuits in experimental and clinical applications. Approach. To overcome these limitations, we developed a high-fidelity 96-channel wireless system to record extracellular spikes and local field potentials from the neocortex. A removable, external case of the wireless device is attached to a titanium pedestal placed in the animal skull. Broadband neural signals are amplified, multiplexed, and continuously transmitted as TCP/IP data at a sustained rate of 24 Mbps. A Xilinx Spartan 6 FPGA assembles the digital signals into serial data frames for transmission at 20 kHz though an 802.11n wireless data link on a frequency-shift key-modulated signal at 5.7–5.8 GHz to a receiver up to 10 m away. The system is powered by two CR123A, 3 V batteries for 2 h of operation. Main results. We implanted a multi-electrode array in visual area V4 of one anesthetized monkey (Macaca fascicularis) and in the dorsolateral prefrontal cortex (dlPFC) of a freely moving monkey (Macaca mulatta). The implanted recording arrays were electrically stable and delivered broadband neural data over a year of testing. For the first time, we compared dlPFC neuronal responses to the same set of stimuli (food reward) in restrained and freely moving conditions. Although we did not find differences in neuronal responses as a function of reward type in the restrained and unrestrained conditions, there were significant differences in correlated activity. This demonstrates that measuring neural responses in freely moving animals can capture phenomena that are absent in the traditional head-fixed paradigm. Significance. We implemented a wireless neural interface for multi-electrode recordings in freely moving non-human primates, which can potentially move systems neuroscience to a new direction by allowing one to record neural signals while animals interact with their environment.

A current challenge in neuroscience, systems and theoretical biology is to understand what properties allow organisms to exhibit and sustain behaviours despite perturbations (behavioural robustness). Indeed, there are still significant theoretical difficulties in this endeavour due to the context-dependent nature of the problem. Contrary to the common view of biological robustness as a phenomenon that emerges internally, this article discusses the hypothesis that behavioural robustness is rooted in dynamical processes that distribute between internal controls, the organism body and the environment. This review highlights the varied perspectives and how they have led to the current focus on robustness as a relational phenomenon. A new perspective is proposed in which robustness is better understood in the context of agent-environment dynamical couplings, in which such couplings are not always the full determinants of robustness. The challenges and limitations of the proposed approach are identified.

Theoretical discussions and computational models of bio-inspired embodied and situated agents are introduced in this article capturing in simplified form the dynamical essence of robust, yet adaptive behavior. This article analyzes the general problem of how the dynamical coupling between internal control (brain), body and environment is used in the generation of specific behaviors. Based on the Evolutionary Robotics (ER) paradigm, four computational models are described to support discussions including descriptions on performance after a series of structural, sensorimotor or mutational perturbations, or are developed in the absence of them. Experimental results suggest that ‘dynamic determinacy’ – i.e. the continuous presence of a unique dynamical attractor that must be chased during functional behaviours – is a common dynamic phenomenon in the analyzed robust and adaptive agents. These agents show dynamical states that are definitely and unequivocally characterized via transient dynamics toward a unique, yet moving attractor at neural level for coherent actions. This determinacy emerges as a control strategy rooted on behavioral couplings and relies on mechanisms that are distributed on brain, body and environment. Different ways to induce further distribution of behavioral mechanisms are also discussed in this paper from a bio-inspired ER perspective.

This article investigates the emergence of robust behaviour in agents with dynamically limited controllers (monostable agents), and compares their performance to less limited ones (bistable agents). ‘Dynamically limited’ here refers to a reduced quantity of steady states that an agent controller exhibits when it does not receive stimulus from the environment. Agents are evolved for categorical perception, a minimal cognitive task, and must correlate approaching or avoiding movements based on (two) different types of objects. Results indicate a significant tendency to better behavioural robustness by monostable in contrast to bistable agents in the presence of sensorimotor, mutational, and structural perturbations. Discussions here focus on a further dependence to coupled dynamics by the former agents to explain such a tendency.

In this work, based on behavioural and dynamical evidence, a study of simulated agents with the capacity to change feedback from their bodies to accomplish a one-legged walking task is proposed to understand the emergence of coupled dynamics for robust behaviour. Agents evolve with evolutionary-defined biases that modify incoming body signals (sensory offsets). Analyses on whether these agents show further dependence to their environmental coupled dynamics than others with no feedback control is described in this article. The ability to sustain behaviours is tested during lifetime experiments with mutational and sensory perturbations after evolution. Using dynamical systems analysis, this work identifies conditions for the emergence of dynamical mechanisms that remain functional despite sensory perturbations. Results indicate that evolved agents with evolvable sensory offset depends not only on where in neural space the state of the neural system operates, but also on the transients to which the inner-system was being driven by sensory signals from its interactions with the environment, controller, and agent body. Experimental evidence here leads discussions on a dynamical systems perspective on behavioural robustness that goes beyond attractors of controller phase space.

The emergence of a unified cognitive behaviour relies on the coordination of specialized components that distribute across a ‘brain’, body and environment. Although a general dynamical mechanism involved in agent–environment integration is still largely unknown for behavioural robustness, discussions here are focussed on one of the most plausible candidate: the formation of distributed mechanisms working in transient during agent–environment coupling. This article provides discussions on this sort of coordination based on a mobile object-tracking task with situated, embodied and minimal agents, and tests for robust yet adaptive behaviour. The proposed scenario provides examples of behavioural mechanisms that counterbalance the functional organization of internal control activity and agents’ situatedness to enable the evolution of a two-agent interaction task. Discussions in this article suggest that future studies of distributed cognition should take into account that there are at least two possible modes of interpreting distributed mechanisms and that these have a qualitatively different effect on behavioural robustness.

RobotCup is an international competition designed to promote Artificial Intelligence (AI) and intelligent robotic research through a standard problem: a soccer game where a wide range of technologies can be integrated (12). This article shows, in a general way, an architecture proposed for controlling a robot soccer team called INCASoT. The team has been designed for its presentation in the