Latent learning

Abstract: To determine general or species-specific properties in neural systems, it is necessary to use comparative data in evaluating experimental findings. Presented here are data on associative learning and memory formation in honeybees, emphasizing a comparative approach. We focus on four aspects: (1) the role of an identified neuron, VUMmx1, as a neural substrate of appetitive reinforcement; (2) the sequences of molecular events as they correlate with five forms of memory stages; (3) the localization of the memory traces following appetitive olfactory learning; and (4) the brief description of several forms of complex learning in bees (configuration in olfactory conditioning, categorization in visual feature learning, delayed matching-to-sample learning, and latent learning in navigation). VUMmx1 activity following the conditioned stimulus odor is sufficient to replace the unconditioned stimulus, and VUMmx1 changes its response properties during learning similarly to what is known from dopamine neurons in the basal ganglia of the mammalian brain. The transition from short- to mid- and long-term forms of memory can be related to specific activation of second messenger cascades (involving NOS, PKA, PKC, and PKM) resembling general features of neural plasticity at the cellular level. The particular time course of the various memory traces may be adapted to the behavioral context in which they are used; here, the foraging cycle of the bee. Memory traces for even such a simple form of learning as olfactory conditioning are multiple and distributed, involving first- and second-order sensory neuropils (antennal lobe and mushroom bodies), but with distinctly different properties. The wealth of complex forms of learning in the context of foraging indicates basic cognitive capacities based on rule extraction and context-dependent learning. It is believed that bees might be a useful model for studying cognitive faculties at a middle level of complexity. Learning and Memory in a Mini-Brain Neuroscience needs a multitude of model systems. Practical reasons favor the study of very few, probably <100 species, of the two million and more animal species for in-depth studies of brain mechanisms and the relationship between brain and behavior. Although this strategy of focusing on a rather small selection of potentially interesting and practically useful species has certainly been one of the reasons for neuroscience’s success, it carries two dangers: of interpreting species-specific solutions as general mechanisms, and of overlooking the richness of mechanistic implementations for solving similar environmental demands developed during evolution. We learn from similarities and differences when we compare species, and we only recognize general mechanisms when we discover them all over again. As long as we deal with basic molecular and cellular properties of neural functions we are rather safe in assuming widespread use across species, but sensory, motor, and cognitive functions of even low complexity may be strongly adapted to the species’ ecological niche, and thus reflect different solutions. Conversely, neural systems that effectively solve common demands in animal life may be conserved in phy

Abstract: This paper presents a brief, informal outline followed by a formal statement of an elemental associative learning model first described by McLaren, Kaye, and Mackintosh (1989). The model assumes representation of stimuli by sets of elements (i.e., microfeatures) and a set of associative algorithms that incorporate the following: real-time simulation of learning; an error-correcting learning rule; weight decay that distinguishes between transient and permanent associations; and modulation of associative learning that gives high salience to and, hence, promotes rapid learning with novel, unpredicted stimuli and reduces the salience for a stimulus as its error term declines. The model is applied in outline fashion to some of the basic phenomena of simple conditioning and, in greater detail, to the phenomena of latent inhibition and perceptual learning. A detailed account of generalization and discrimination will be provided in a later paper.