Topic: associative learning

A fundamental and distinguishing feature of human intelligence is that we transfer and accumulate knowledge as a society and across generations. We suggest that for communication and accumulation, explicit (but not implicit) intelligence is likely encapsulated by a network of attractors representing abstractions such as objects or actions and direct connections between them, rather than one that utilizes hidden layers of representation. We propose a network model for explicit human intelligence that is composed of attractors and learned connections as elemental units. As attractors are added a

Contemporary Bayesian theories of the mind highlight the role of prediction errors as a driving force in learning processes and the updating of our models of the world and of ourselves. This process is postulated to manifest both at explicit levels (e.g., subjective responses) and at implicit levels (e.g., eye movements, reaction time) of behavior. We developed a novel associative learning task in immersive virtual reality, in which a cue probabilistically predicted the subsequent location of a target to which participants had to reach. Specifically, the current study (N = 30) examined: (1) wh

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement

Many proposed neural circuits for learning are theoretically well-grounded but have limited biological evidence to support them. Emerging anatomical and functional data from neurons in the fruit fly brain allows us to identify interconnections in the mushroom body neuropil that form an 'incentive circuit', supporting rich dynamics of memory acquisition, consolidation and forgetting. In addition, new insights into dopamine function in the fruit fly brain motivate a novel 'dopaminergic plasticity rule' that alters the weights between input sensory patterns and output neurons of the mushroom body

Over the last fifteen years, Simona Ginsburg and I developed an evolutionary approach for studying basic consciousness, suggesting that the evolution of learning drove the evolutionary transition to from non-conscious to conscious animals. I present the rationale underlying this thesis, which has led to the identification of a capacity that we call the evolutionary transition marker, which, when we find evidence of it, we have evidence that the major evolutionary transition in which we are interested has gone to completion. I then put forward our proposal that the evolutionary marker of basic

Team work makes sensory streams work: our senses work together, learn from each other, and stand in for one another, the result of which is perception and understanding. Learned associations between stimuli in different sensory modalities can shape the way we perceive these stimuli (Mcgurk and Macdonald, 1976). During audio-visual associative learning, auditory cortex is thought to underlie multi-modal plasticity in visual cortex (McIntosh et al., 1998; Mishra et al., 2007; Zangenehpour and Zatorre, 2010). However, it is not well understood how processing in visual cortex is altered by an audi

A vast variety of sensory cues are represented as distinct patterns of neural responses in the brain. To retrieve memories associated with sensory cues, one would assume that the representations of sensory signals should be stable over time. However, recent development of population-level recording techniques has revealed that these representations can drift over time $^{1}$. To investigate the mechanisms and consequences of representational drift, we formulate a random feedforward network model $^{2,3}$ that incorporates stochastic fluctuations in membrane potential and synaptic strength $^{4

Odor naming is considered as a particularly challenging task. In free odor naming scenarios people often fail to respond with any linguistic label to certain odors they smell, resulting in what is called an omission (i.e., a lack of response). The cognitive demands and the nature of interactions between olfaction and language related brain’s neural resources are not well understood [1]. Behavioral task: the data that we used to compare our model output were collected from a Swedish National Study on Aging and Care in Kungsholmen (SNAC-K), where ~2500 subjects underwent an odor naming test of