50 Years Since the Marr, Ito, and Albus Models of the Cerebellum

Fifty years have passed since David Marr, Masao Ito, and James Albus proposed seminal models of cerebellar functions. These models share the essential concept that parallel-fiber-Purkinje-cell synapses undergo plastic changes, guided by climbing-fiber activities during sensorimotor learning. However, they differ in several important respects, including holistic versus complementary roles of the cerebellum, pattern recognition versus control as computational objectives, potentiation versus depression of synaptic plasticity, teaching signals versus error signals transmitted by climbing-fibers, sparse expansion coding by granule cells, and cerebellar internal models. In this review, we evaluate different features of the three models based on recent computational and experimental studies. While acknowledging that the three models have greatly advanced our understanding of cerebellar control mechanisms in eye movements and classical conditioning, we propose a new direction for computational frameworks of the cerebellum, that is, hierarchical reinforcement learning with multiple internal models.

Grounding cognitive-level processes in behavior: the view from dynamic systems theory

Marr's seminal work laid out a program of research by specifying key questions for cognitive science at different levels of analysis. Because dynamic systems theory (DST) focuses on time and interdependence of components, DST research programs come to very different conclusions regarding the nature of cognitive change. We review a specific DST approach to cognitive-level processes: dynamic field theory (DFT). We review research applying DFT to several cognitive-level processes: object permanence, naming hierarchical categories, and inferring intent, that demonstrate the difference in understanding of behavior and cognition that results from a DST perspective. These point to a central challenge for cognitive science research as defined by Marr-emergence. We argue that appreciating emergence raises questions about the utility of computational-level analyses and opens the door to insights concerning the origin of novel forms of behavior and thought (e.g., a new chess strategy). We contend this is one of the most fundamental questions about cognition and behavior.

Thirty Years After Marr's Vision: Levels of Analysis in Cognitive Science

Thirty years after the publication of Marr's seminal book Vision (Marr, 1982) the papers in this topic consider the contemporary status of his influential conception of three distinct levels of analysis for information-processing systems, and in particular the role of the algorithmic and representational level with its cognitive-level concepts. This level has (either implicitly or explicitly) been downplayed or eliminated both by reductionist neuroscience approaches from below that seek to account for behavior from the implementation level and by Bayesian approaches from above that seek to account for behavior in purely computational-level terms.

Long-term depression as a model of cerebellar plasticity

Long-term depression (LTD) here concerned is persistent attenuation of transmission efficiency from a bundle of parallel fibers to a Purkinje cell. Uniquely, LTD is induced by conjunctive activation of the parallel fibers and the climbing fiber that innervates that Purkinje cell. Cellular and molecular processes underlying LTD occur postsynaptically. In the 1960s, LTD was conceived as a theoretical possibility and in the 1980s, substantiated experimentally. Through further investigations using various pharmacological or genetic manipulations of LTD, a concept was formed that LTD plays a major role in learning capability of the cerebellum (referred to as "Marr-Albus-Ito hypothesis"). In this chapter, following a historical overview, recent intensive investigations of LTD are reviewed. Complex signal transduction and receptor recycling processes underlying LTD are analyzed, and roles of LTD in reflexes and voluntary movements are defined. The significance of LTD is considered from viewpoints of neural network modeling. Finally, the controversy arising from the recent finding in a few studies that whereas LTD is blocked pharmacologically or genetically, motor learning in awake behaving animals remains seemingly unchanged is examined. We conjecture how this mismatch arises, either from a methodological problem or from a network nature, and how it might be resolved.

Re-appreciating the why of cognition: 35 years after marr and poggio

Marr and Poggio’s levels of description are one of the most well-known theoretical constructs of twentieth century cognitive science. It entails that behavior can and should be considered at three different levels: computation, algorithm, and implementation. In this contribution focus is on the computational level of description, the level that describes the “why” of cognition. I argue that the computational level should be taken as a starting point in devising experiments in cognitive (neuro)science. Instead, the starting point in empirical practice often is a focus on the stimulus or on some capacity of the cognitive system. The “why” of cognition tends to be ignored when designing research, and is not considered in subsequent inference from experimental results. The overall aim of this manuscript is to show how re-appreciation of the computational level of description as a starting point for experiments can lead to more informative experimentation.