If there is one rationality there must be a plurality of them. This conclusion follows, I argue, partly from the extreme and ineradicable vagueness of the fundamental concepts that every would‐be rational explanation must presuppose. Logicistic/scientistic assaults on this vagueness are doomed to fail partly because they are unable to acknowledge the imaginative dimension of rational thought. Being limited to the play of “outward appearances,” scientific investigations are also dependent on “inward imaginings” on their speculative side. The upshot is that (...) schools of philosophy should be characterized by the kind of imaginary they adopt rather than by their logics. In which case, every attempt to get and tell something right about the world is bound to incorporate mythopoeic elements in its explanations. (shrink)

If there is one rationality there must be a plurality of them. This conclusion follows, I argue, partly from the extreme and ineradicable vagueness of the fundamental concepts that every would‐be rational explanation must presuppose. Logicistic/scientistic assaults on this vagueness are doomed to fail partly because they are unable to acknowledge the imaginative dimension of rational thought. Being limited to the play of “outward appearances,” scientific investigations are also dependent on “inward imaginings” on their speculative side. The upshot is that (...) schools of philosophy should be characterized by the kind of imaginary they adopt rather than by their logics. In which case, every attempt to get and tell something right about the world is bound to incorporate mythopoeic elements in its explanations. (shrink)

The neural coding metaphor is so ubiquitous that we tend to forget its metaphorical nature. What do we mean when we assert that neurons encode and decode? What kind of causal and representational model of the brain does the metaphor entail? What lies beneath the neural coding metaphor, I argue, is a bureaucratic model of the brain.

Pointing to similarities between challenges encountered in today's neural coding and twentieth-century behaviorism, we draw attention to lessons learned from resolving the latter. In particular, Perceptual Control Theory posits behavior as a closed-loop control process with immediate and teleological causes. With two examples, we illustrate how these ideas may also address challenges facing current neural coding paradigms.

Brette presents arguments that query the existence of the neural code. However, he has neglected certain evidence that could be viewed as proof that a neural code operates in the brain. Albeit these proofs show a link between neural activity and cognition, we discuss why they fail to demonstrate the existence of an invariant neural code.

Brains are information processing systems whose operational principles ultimately cannot be understood without resource to information theory. We suggest that understanding how external signals are represented in the brain is a necessary step towards employing further engineering tools to understand the information processing performed by brain circuits during behaviour.

We argue that Brette's arguments, or some variation on them, work only against the immodest codes imputed by neuroscientists to the signals they study; they do not tell against “modest” codes, which may be learned by neurons themselves. Still, caution is warranted: modest neural codes likely lead to only modest explanatory gains.

Barlow's concept of the exploitation of environmental statistical regularities may be more plausibly related to brain mechanisms than Shepard's notion of internalisation. In our view, Barlow endorses a bottom-up approach to neural coding and processing, whereas we suggest that feedback interactions in the visual system, as well as chaotic correlation dynamics in the brain, are crucial in exploiting and assimilating environmental regularities. We also discuss the “conceptual tension” between Shepard's ideas of law internalisation and evolutionary adaptation. [Barlow; Shepard].

Susan Hurley's impressive article about the shared circuit model (SCM) raises two important issues. First, I suggest that the SCM presupposes relational coding rather than translational coding as neural code. Second, the SCM being the basis for self implies that the self may be characterized as format, relational, and embodied and embedded, rather than by specific and isolated higher-order cognitive contents.

The convincing argument that Brette makes for the neural coding metaphor as imposing one view of brain behavior can be further explained through discourse analysis. Instead of a unified view, we argue, the coding metaphor's plasticity, versatility, and robustness throughout time explain its success and conventionalization to the point that its rhetoric became overlooked.

Representation and computation are the best tools we have for explaining intelligent behavior. In our program, we explore the space of representations present in the mind by constraining them to explain data at multiple levels of analysis, from behavioral patterns to neural activity. We argue that this integrated program assuages Brette's worries about the study of the neural code.

Tsuda's article suggests several plausible concepts of neurodynamic representation and processing, with a thoughtful discussion of their neurobiological grounding and formal properties. However, Tsuda's theory leads to a holistic view of brain functions and to the controversial conclusion that the “binding problem” is a pseudo-problem. By contrast, we stress the role of chaotic patterns in solving the binding problem, in terms of flexible temporal coding of visual scenes through graded and intermittent synchrony.

In Posner & Raichle's (1994) book, two essential and strictly related limitations of cognitive neurophysiology are not sufficiently enhanced: (1) The problem of “coding,” namely the capability of a natural brain to redefine its own “basic symbols” as a function of a changing environment; (2) the inadequacy of a Hebbian rule to reckon with complex computational problems such as those solved by real brains.

Shannon's theory lays the foundation for understanding the flow of information from world into brain: There must be a set of possible messages. Brain structure determines what they are. Many messages convey quantitative facts. It is impossible to consider how neural tissue processes these numbers without first considering how it encodes them.

Various neurophysiological experiments have revealed remarkable correlations between cortical neuronal activity and subjective experiences. However, the mere presence of neuronal electrical activity does not appear to be sufficient to produce these experiences. It has been suggested that the explanation for the neural basis of consciousness might lie in understanding the reason that some types of neuronal activity possess subjective correlates and others do not. Here I propose and develop the idea that this difference may be caused by the existence (...) of an elementary nonarbitrary linkage between temporal or spatiotemporal patterns of neuronal activity and their subjective attributes. I also show how cortical neural circuits capable of generating experience-coding patterns could emerge during evolution and brain development, due to the presence of spontaneous stochastic neuronal activity and activity-dependent synaptic plasticity. This hypothesis leads to several testable predictions, principal among which is the idea that the neural correlates of consciousness are essentially innate and universal. (shrink)

Page is to be congratulated for challenging some misconceptions about neural representation. However, his target article, and the commentaries to it, highlight that the terms “local” and “distributed” are open to misinterpretation. These terms provide a poor description of neural coding strategies and a better taxonomy might resolve some of the issues.

The two most popular, yet opposing, models of taste processing, the labeled-line (LL) and across-neuron pattern (ANP) theories, are variants of spatial neural coding. Analyses whose outcomes have been argued to support either theory have sometimes glossed over important caveats and considerations that may drastically impact interpretation. Some of these issues are discussed here.

Degeneracy is ubiquitous across biological systems where structurally different elements can yield a similar outcome. Degeneracy is of particular interest in neuroscience too. On the one hand, degeneracy confers robustness to the nervous system and facilitates evolvability: Different elements provide a backup plan for the system in response to any perturbation or disturbance. On the other, a difficulty in the treatment of some neurological disorders such as chronic pain is explained in light of different elements all of which contribute to (...) the pathological behavior of the system. Under these circumstances, targeting a specific element is ineffective because other elements can compensate for this modulation. Understanding degeneracy in the physiological context explains its beneficial role in the robustness of neural circuits. Likewise, understanding degeneracy in the pathological context opens new avenues of discovery to find more effective therapies. (shrink)

If sensory organs encode environment, this code must be decoded to perception. The currently dominant theory of perception – predictive coding – assumes a “Bayesian decoder,” a probability function, which will present an optimal guess, given previous encodings of the environment – old codes testing new codes. Such a process would delay perception noticeably. This is inconsistent with the perception of music, which for several reasons must be direct.

Sustained neural activity during the delay phase of spatial working memory tasks is compelling evidence for the neural correlate of active storage and maintenance of spatial information, however, it does not provide insight into specific mechanisms of spatial coding. This activity may reflect a range of processes, such as maintenance of a stimulus position or a prepared motor response plan. The aim of our study was to examine neural evidence for the use of different coding (...) strategies, depending on the characteristics and demands of a spatial working memory task. Thirty-one and 44 participants performed a spatial working memory task while we measured their brain activity using fMRI in two separate experiments. Participants were asked to remember the position of a briefly presented target stimulus and, after a delay period, to use a joystick to indicate either the position of the remembered target or an indicated non-matching location. The task was designed so that the predictability of the response could be manipulated independently of task difficulty and memory retrieval process. We were particularly interested in contrasting conditions in which participants could use prospective coding of the motor response or had to rely on retrospective sensory information. Prospective motor coding was associated with activity in somatomotor, premotor, and motor cortices and increased integration of brain activity with and within the somatomotor network. In contrast, retrospective sensory coding was associated with increased activity in parietal regions and increased functional connectivity with and within secondary visual and dorsal attentional networks. The observed differences in activation levels, dynamics of differences over trial duration, and integration of information within and between brain networks provide compelling evidence for the use of complementary spatial working memory strategies optimized to meet task demands. (shrink)

Both distributed coding, with its implication of neural reuse, and more specialized function have been recognized since the beginning of brain science. A controversy over imageless thought threw introspection into disrepute as a scientific method, making more objective methods dominate. It is known in information science that one element, such as a bit in a computer, can participate in coding many independent states; in this commentary, an example is given.

Two universal functional principles of Grossberg’s Adaptive Resonance Theory decipher the brain code of all biological learning and adaptive intelligence. Low-level representations of multisensory stimuli in their immediate environmental context are formed on the basis of bottom-up activation and under the control of top-down matching rules that integrate high-level, long-term traces of contextual configuration. These universal coding principles lead to the establishment of lasting brain signatures of perceptual experience in all living species, from aplysiae to primates. They are re-visited (...) in this concept paper on the basis of examples drawn from the original code and from some of the most recent related empirical findings on contextual modulation in the brain, highlighting the potential of Grossberg’s pioneering insights and groundbreaking theoretical work for intelligent solutions in the domain of developmental and cognitive robotics. (shrink)

Two universal functional principles of Grossberg’s Adaptive Resonance Theory [19] decipher the brain code of all biological learning and adaptive intelligence. Low-level representations of multisensory stimuli in their immediate environmental context are formed on the basis of bottom-up activation and under the control of top-down matching rules that integrate high-level long-term traces of contextual configuration. These universal coding principles lead to the establishment of lasting brain signatures of perceptual experience in all living species, from aplysiae to primates. They are (...) re-visited in this concept paper here on the basis of examples drawn from the original code and from some of the most recent related empirical findings on contextual modulation in the brain, highlighting the potential of Grossberg’s pioneering insights and groundbreaking theoretical work for intelligent solutions in the domain of developmental and cognitive robotics. (shrink)

We review the hypothesis that the brain uses a generative model to explain the causes of sensory inputs, using prediction schemes that operate based upon assimilation of time-series sensory data. We put this hypothesis in the context of psychopathology, in particular, schizophrenia's positive symptoms. Building upon work of Helmholtz and upon theories in computational cognitive processing, we hypothesize that delusions in schizophrenia can be explained in terms of false inference. An impairment in inferring appropriate information from the sensory input reflects (...) upon the ability to assess the environment and predict outcomes. Although the inference mechanism likely involves both conscious and unconscious processes, we hypothesize that the trigger of delusions may lie within the unconscious neural pathways. A collection of computational predictive codes have been proposed for modeling perception. We discuss two examples, which may be eligible as substrates for intuitive coding. We argue that failure of the psychotic patient to choose the correct computational scheme, or the optimal range of parameters, may readily lead to an altered reconstruction of the object and false inference, feeding into the delusion mechanism. We finally propose using these models in conjunction with cognitive and imaging data, in order to obtain more testable predictions. (shrink)

“Neural coding” is a popular metaphor in neuroscience, where objective properties of the world are communicated to the brain in the form of spikes. Here I argue that this metaphor is often inappropriate and misleading. First, when neurons are said to encode experimental parameters, the neural code depends on experimental details that are not carried by the coding variable. Thus, the representational power of neural codes is much more limited than generally implied. Second, neural (...) codes carry information only by reference to things with known meaning. In contrast, perceptual systems must build information from relations between sensory signals and actions, forming an internal model. Neural codes are inadequate for this purpose because they are unstructured and therefore unable to represent relations. Third, coding variables are observables tied to the temporality of experiments, whereas spikes are timed actions that mediate coupling in a distributed dynamical system. The coding metaphor tries to fit the dynamic, circular, and distributed causal structure of the brain into a linear chain of transformations between observables, but the two causal structures are incongruent. I conclude that the neural coding metaphor cannot provide a valid basis for theories of brain function, because it is incompatible with both the causal structure of the brain and the representational requirements of cognition. (shrink)

This book examines the concept of “ Neurosemantics”, a term currently used in two different senses: the informational meaning of the physical processes in the neural circuits, and semantics in its classical sense, as the meaning of language, explained in terms of neural processes. The book explores this second sense of neurosemantics, yet in doing so, it addresses much of the first meaning as well. Divided into two parts, the book starts with a description and analysis of the (...) mathematics of the brain, including computational units, representational mechanisms and algorithmic principles. This first part pays special attention to the neural architecture which has been used in developing models of neurosemantics. The second part of the book presents a collection of models, and describes each model reproducing specific aspects of the semantics of language. Some of these models target one of the core problems of semantics, the reference of nouns, and in particular of nouns with a strong perceptual characterization. Others address the semantics of predicates, with a detailed analysis of colour attributes. While this book represents a radical shift from traditional semantics, it still pursues a line of continuity that is based on the idea that meaning can be captured, and explained, by a sort of computation. (shrink)

In this paper we defend structural representations, more specifically neural structural representation. We are not alone in this, many are currently engaged in this endeavor. The direction we take, however, diverges from the main road, a road paved by the mathematical theory of measure that, in the 1970s, established homomorphism as the way to map empirical domains of things in the world to the codomain of numbers. By adopting the mind as codomain, this mapping became a boon for all (...) those convinced that a representation system should bear similarities with what was being represented, but struggled to find a precise account of what such similarities mean. The euforia was brief, however, and soon homomorphism revealed itself to be affected by serious weaknesses, the primary one being that it included systems embarrassingly alien to representations. We find that the defense attempts that have followed, adopt strategies that share a common format: valid structural representations come as “homomorphism plus X”, with various “X”, provided in descriptive format only. Our alternative direction stems from the observation of the overlooked departure from homomorphism as used in the theory of measure and its later use in mental representations. In the former case, the codomain or the realm of numbers, is the most suited for developing theorems detailing the existence and uniqueness of homomorphism for a wide range of empirical domains. In the latter case, the codomain is the realm of the mind, possibly more vague and more ill-defined than the empirical domain itself. The time is ripe for articulating the mapping between represented domains and the mind in formal terms, by exploiting what is currently known about coding mechanisms in the brain. We provide a sketch of a possible development in this direction, one that adopts the theory of neural population coding as codomain. We will show that our framework is not only not in disagreement with the “plus X” proposals, but can lead to natural derivation of several of the “X”. (shrink)

Brette contends that the neural coding metaphor is an invalid basis for theories of what the brain does. Here, we argue that it is an insufficient guide for building an artificial intelligence that learns to accomplish short- and long-term goals in a complex, changing environment.

This article presents an update on known and unknown biological codes. While the genetic code has been recognized as a code for decades, most other codes were hidden in the shadow of this hallmark discovery. It was the dawn of the new millennium when the histone language and code were proclaimed in the years 2000 and 2001, respectively, marking the start of an explosion in the number of published codes across all biological disciplines since then. Actually, there are hundreds to (...) thousands of biological codes, but only a fraction—although still a substantial number—have been explicitly described as such. A first assessment of those codes allows for a simple categorization into structural and regulatory codes, at the same time maintaining Marcello Barbieri´s initial differentiation of organic versus neural codes. The article reviews prominent codes and showcases restriction-modification and toxin-antitoxin systems as code systems that have gone unrecognized until today. This article should read as a motivation for all those who want to discover ever more new codes—from literature or in the field. (shrink)