Hebbian activity-dependent plasticity in white matter

Synaptic plasticity is required for learning and follows Hebb's Rule, the computational principle underpinning associative learning. In recent years, a complementary type of brain plasticity has been identified in myelinated axons, which make up the majority of brain's white matter. Like synaptic plasticity, myelin plasticity is required for learning, but it is unclear whether it is Hebbian or whether it follows different rules. Here, we provide evidence that white matter plasticity operates following Hebb's Rule in humans. Across two experiments, we find that co-stimulating cortical areas to induce Hebbian plasticity leads to relative increases in cortical excitability and associated increases in a myelin marker within the stimulated fiber bundle. We conclude that Hebbian plasticity extends beyond synaptic changes, and can be observed in human white matter fibers.

Modelling how cleaner fish approach an ephemeral reward task demonstrates a role for ecologically tuned chunking in the evolution of advanced cognition

What makes cognition “advanced” is an open and not precisely defined question. One perspective involves increasing the complexity of associative learning, from conditioning to learning sequences of events (“chaining”) to representing various cue combinations as “chunks.” Here we develop a weighted graph model to study the mechanism enabling chunking ability and the conditions for its evolution and success, based on the ecology of the cleaner fish Labroides dimidiatus. In some environments, cleaners must learn to serve visitor clients before resident clients, because a visitor leaves if not attended while a resident waits for service. This challenge has been captured in various versions of the ephemeral reward task, which has been proven difficult for a range of cognitively capable species. We show that chaining is the minimal requirement for solving this task in its common simplified laboratory format that involves repeated simultaneous exposure to an ephemeral and permanent food source. Adding ephemeral–ephemeral and permanent–permanent combinations, as cleaners face in the wild, requires individuals to have chunking abilities to solve the task. Importantly, chunking parameters need to be calibrated to ecological conditions in order to produce adaptive decisions. Thus, it is the fine-tuning of this ability, which may be the major target of selection during the evolution of advanced associative learning.

Cerebellar associative learning underlies skilled reach adaptation

Cerebellar output has been shown to enhance movement precision by scaling the decelerative phase of reaching movements in mice. We hypothesized that during reach, initial kinematics cue late-phase adjustments through cerebellar associative learning. We identify a population-level response in mouse PCs that scales inversely with reach velocity, suggesting a candidate mechanism for anticipatory control to target limb endpoint. We next interrogate how such a response is generated by combining high-density neural recordings with closed-loop optogenetic stimulation of cerebellar mossy fiber afferents originating in the pontine nuclei during reach, using perturbation schedules reminiscent of classic adaptation paradigms. We found that reach kinematics and PC electrophysiology adapt to position-locked mossy fiber perturbations and exhibit aftereffects when stimulation is removed. Surprisingly, we observed partial adaptation to position-randomized stimulation schedules but no opposing aftereffect. A model that recapitulated these findings provided novel insight into how the cerebellum deciphers cause-and-effect relationships to adapt.

A Smarter Pavlovian Dog with Optically Modulated Associative Learning in an Organic Ferroelectric Neuromem

Associative learning is a critical learning principle uniting discrete ideas and percepts to improve individuals’ adaptability. However, enabling high tunability of the association processes as in biological counterparts and thus integration of multiple signals from the environment, ideally in a single device, is challenging. Here, we fabricate an organic ferroelectric neuromem capable of monadically implementing optically modulated associative learning. This approach couples the photogating effect at the interface with ferroelectric polarization switching, enabling highly tunable optical modulation of charge carriers. Our device acts as a smarter Pavlovian dog exhibiting adjustable associative learning with the training cycles tuned from thirteen to two. In particular, we obtain a large output difference (>103), which is very similar to the all-or-nothing biological sensory/motor neuron spiking with decrementless conduction. As proof-of-concept demonstrations, photoferroelectric coupling-based applications in cryptography and logic gates are achieved in a single device, indicating compatibility with biological and digital data processing.

Can associative learning be the general process for intelligent behavior in non-human animals?

The general process- and adaptive specialization hypotheses represent two contrasting explanations for understanding intelligence in non-human animals. The general process hypothesis proposes that associative learning underlies all learning, whereas the adaptive specialization hypothesis suggests additional distinct learning processes required for intelligent behavior. Here, we use a selection of experimental paradigms commonly used in comparative cognition to explore these hypotheses. We tested if a novel computational model of associative learning --- A-learning --- could solve the problems presented in these tests. Results show that this formulation of associative learning suffices as a mechanism for general animal intelligence, without the need for adaptive specialization, as long as adequate motor- and perceptual systems are there to support learning. In one of the tests, however, the addition of a short-term trace memory was required for A-learning to solve that particular task. We further provide a case study showcasing the flexibility, and lack thereof, of associative learning, when looking into potential learning of self-control and the development of behavior sequences. From these simulations we conclude that the challenges do not so much involve the complexity of a learning mechanism, but instead lie in the development of motor- and perceptual systems, and internal factors that motivate agents to explore environments with some precision, characteristics of animals that have been fine-tuned by evolution for million of years.

Conditioning and Associative Learning

Associative learning is the process whereby humans and other animals learn the predictive relationship between cues in their environment. This process underlies simple forms of learning from rewards, such as classical and operant conditioning. In this chapter, we introduce the basics of associative learning and discuss the role that memory processes play in the establishment and maintenance of this learning. We then discuss the role that associative learning plays in human memory, including through paired associate learning, the enhancement of memory by reward, and the formation of episodic memories. Finally, we illustrate how the memory process influences choice in decision-making, where associative learning allows people to learn the values of different options. We conclude with some suggestions about how models of associative learning, memory, and choice can be integrated into a single theoretical framework.

Conditional Freezing, Flight and Darting?

Fear conditioning is one of the most frequently used laboratory procedures for modeling learning and memory generally, and anxiety disorders in particular. The conditional response (CR) used in the majority of fear conditioning studies in rodents is freezing. Recently, it has been reported that under certain conditions, running, jumping or darting replaces freezing as the dominant CR. These findings raise both a critical methodological problem and an important theoretical issue. If only freezing is measured but rodents express their learning with a different response, then significant instances of learning, memory, or fear may be missed. In terms of theory, whatever conditions lead to these different behaviors may be a key to how animals transition between different defensive responses and different emotional states. We replicated these past results but along with several novel control conditions. Contrary to the prior conclusions, running and darting were entirely a result of nonassociative processes and were actually suppressed by associative learning. Darting and flight were taken to be analogous to nonassociative startle or alpha responses that are potentiated by fear. On the other hand, freezing was the purest reflection of associative learning. We also uncovered a rule that describes when these movements replace freezing: When afraid, freeze until there is a sudden novel change in stimulation, then burst into vigorous flight attempts. This rule may also govern the change from fear to panic.

Cannabis Use, Schizotypy and Kamin Blocking Performance

Cannabis use has been associated with increased risk for a first episode of psychosis and inappropriate assignment of salience to extraneous stimuli has been proposed as a mechanism underlying this association. Psychosis-prone (especially schizotypal) personality traits are associated with deficits in associative learning tasks that measure salience allocation. The aim of this study was to examine the relationship between history of cannabis use and Kamin blocking (KB), a form of selective associative learning, in a non-clinical sample. Additionally, KB was examined in relation to self-reported schizotypy and aberrant salience scale profiles. A cross-sectional study was conducted in 307 healthy participants with no previous psychiatric or neurological history. Participants were recruited and tested using the Testable Minds behavioural testing platform. KB was calculated using Oades' “mouse in the house task”, performance of which is disrupted in schizophrenia patients. Schizotypy was measured using the Schizotypal Personality Questionnaire (SPQ), and the Aberrant Salience Inventory (ASI) was used to assess self-reported unusual or inappropriate salience. The modified Cannabis Experience Questionnaire (CEQm) was used to collect detailed history of use of cannabis and other recreational drugs. Regression models and Bayesian t-tests or ANOVA (or non-parametric equivalents) examined differences in KB based on lifetime or current cannabis use (frequent use during previous year), as well as frequency of use among those who had previously used cannabis. Neither lifetime nor current cannabis use was associated with any significant change in total or trial-specific KB scores. Current cannabis use was associated with higher Disorganised SPQ dimension scores and higher total and sub-scale values for the ASI. A modest positive association was observed between total KB score and Disorganised SPQ dimension scores, but no relationships were found between KB and other SPQ measures. Higher scores on “Senses Sharpening” ASI sub-scale predicted decreased KB score only in participants who have not engaged in recent cannabis use. These results are discussed in the context of our understanding of the effects of long-term cannabis exposure on salience attribution, as well as inconsistencies in the literature with respect to both the relationship between KB and schizotypy and the measurement of KB associative learning phenomena.