Fishnition: Developing Models From Cognition Toward Consciousness

A challenge to developing a model for testing animal consciousness is the pull of opposite intuitions. On one extreme, the anthropocentric view holds that consciousness is a highly sophisticated capacity involving self-reflection and conceptual categorization that is almost certainly exclusive to humans. At the opposite extreme, an anthropomorphic view attributes consciousness broadly to any behavior that involves sensory responsiveness. Yet human experience and observation of diverse species suggest that the most plausible case is that consciousness functions between these poles. In exploring the middle ground, we discuss the pros and cons of “high level” approaches such as the dual systems approach. According to this model, System 1 can be thought of as unconscious; processing is fast, automatic, associative, heuristic, parallel, contextual, and likely to be conserved across species. Consciousness is associated with System 2 processing that is slow, effortful, rule-based, serial, abstract, and exclusively human. An advantage of this model is the clear contrast between heuristic and decision-based responses, but it fails to include contextual decision-making in novel conditions which falls in between these two categories. We also review a “low level” model involving trace conditioning, which is a trained response to the first of two paired stimuli separated by an interval. This model highlights the role of consciousness in maintaining a stimulus representation over a temporal span, though it overlooks the importance of attention in subserving and also disrupting trace conditioning in humans. Through a critical analysis of these two extremes, we will develop the case for flexible behavioral response to the stimulus environment as the best model for demonstrating animal consciousness. We discuss a methodology for gauging flexibility across a wide variety of species and offer a case study in spatial navigation to illustrate our proposal. Flexibility serves the evolutionary function of enabling the complex evaluation of changing conditions, where motivation is the basis for goal valuation, and attention selects task-relevant stimuli to aid decision-making processes. We situate this evolutionary function within the Temporal Representation Theory of consciousness, which proposes that consciousness represents the present moment in order to facilitate flexible action.

Role of the motor cortex in the generation of classically conditioned eyelid and vibrissae responses

The eyelid motor system has been used for years as an experimental model for studying the neuronal mechanisms underlying motor and cognitive learning, mainly with classical conditioning procedures. Nonetheless, it is not known yet which brain structures, or neuronal mechanisms, are responsible for the acquisition, storage, and expression of these motor responses. Here, we studied the temporal correlation between unitary activities of identified eyelid and vibrissae motor cortex neurons and the electromyographic activity of the orbicularis oculi and vibrissae muscles and magnetically recorded eyelid positions during classical conditioning of eyelid and vibrissae responses, using both delay and trace conditioning paradigms in behaving mice. We also studied the involvement of motor cortex neurons in reflexively evoked eyelid responses and the kinematics and oscillatory properties of eyelid movements evoked by motor cortex microstimulation. Results show the involvement of the motor cortex in the performance of conditioned responses elicited during the classical conditioning task. However, a timing correlation analysis showed that both electromyographic activities preceded the firing of motor cortex neurons, which must therefore be related more with the reinforcement and/or proper performance of the conditioned responses than with their acquisition and storage.

Adaptively Timed Learning

This chapter explains how humans and other animals learn to adaptively time their behaviors to match external environmental constraints. It hereby explains how nerve cells learn to bridge big time intervals of hundreds of milliseconds or even several seconds, and thereby associate events that are separated in time. This is accomplished by a spectrum of cells that each respond in overlapping time intervals and whose population response can bridge intervals much larger than any individual cell can. Such spectral timing occurs in circuits that include the lateral entorhinal cortex and hippocampal cortex. Trace conditioning, in which CS and US are separated in time, requires the hippocampus, whereas delay conditioning, in which they overlap, does not. The Weber law observed in trace conditioning naturally emerges from spectral timing dynamics, as later confirmed by data about hippocampal time cells. Hippocampal adaptive timing enables a cognitive-emotional resonance to be sustained long enough to become conscious of its feeling and its causal event, and to support BDNF-modulated memory consolidation. Spectral timing supports balanced exploratory and consummatory behaviors whereby restless exploration for immediate gratification is replaced by adaptively timed consummation. During expected disconfirmations of reward, orienting responses are inhibited until an adaptively timed response is released. Hippocampally-mediated incentive motivation supports timed responding via the cerebellum. mGluR regulates adaptive timing in hippocampus, cerebellum, and basal ganglia. Breakdowns of mGluR and dopamine modulation cause symptoms of autism and Fragile X syndrome. Inter-personal circular reactions enable social cognitive capabilities, including joint attention and imitation learning, to develop.

Mesolimbic dopamine adapts the rate of learning from errors in performance

Recent success in training artificial agents and robots derives from a combination of direct learning of behavioral policies and indirect learning via value functions. Policy learning and value learning employ distinct algorithms that depend upon evaluation of errors in performance and reward prediction errors, respectively. In animals, behavioral learning and the role of mesolimbic dopamine signaling have been extensively evaluated with respect to reward prediction errors; however, to date there has been little consideration of how direct policy learning might inform our understanding. Here we used a comprehensive dataset of orofacial and body movements to reveal how behavioral policies evolve as naive, head-restrained mice learned a trace conditioning paradigm. Simultaneous multi-regional measurement of dopamine activity revealed that individual differences in initial reward responses robustly predicted behavioral policy hundreds of trials later, but not variation in reward prediction error encoding. These observations were remarkably well matched to the predictions of a neural network based model of behavioral policy learning. This work provides strong evidence that phasic dopamine activity regulates policy learning from performance errors in addition to its roles in value learning and further expands the explanatory power of reinforcement learning models for animal learning.

Distinct neuronal populations contribute to trace conditioning and extinction learning in the hippocampal CA1

Trace conditioning and extinction learning depend on the hippocampus, but it remains unclear how neural activity in the hippocampus is modulated during these two different behavioral processes. To explore this question, we performed calcium imaging from a large number of individual CA1 neurons during both trace eye-blink conditioning and subsequent extinction learning in mice. Our findings reveal that distinct populations of CA1 cells contribute to trace conditioned learning versus extinction learning, as learning emerges. Furthermore, we examined network connectivity by calculating co-activity between CA1 neuron pairs and found that CA1 network connectivity patterns also differ between conditioning and extinction, even though the overall connectivity density remains constant. Together, our results demonstrate that distinct populations of hippocampal CA1 neurons, forming different sub-networks with unique connectivity patterns, encode different aspects of learning.

Predictive olfactory learning in Drosophila

Olfactory learning and conditioning in the fruit fly is typically modelled by correlation-based associative synaptic plasticity. It was shown that the conditioning of an odor-evoked response by a shock depends on the connections from Kenyon cells (KC) to mushroom body output neurons (MBONs). Although on the behavioral level conditioning is recognized to be predictive, it remains unclear how MBONs form predictions of aversive or appetitive values (valences) of odors on the circuit level. We present behavioral experiments that are not well explained by associative plasticity between conditioned and unconditioned stimuli, and we suggest two alternative models for how predictions can be formed. In error-driven predictive plasticity, dopaminergic neurons (DANs) represent the error between the predictive odor value and the shock strength. In target-driven predictive plasticity, the DANs represent the target for the predictive MBON activity. Predictive plasticity in KC-to-MBON synapses can also explain trace-conditioning, the valence-dependent sign switch in plasticity, and the observed novelty-familiarity representation. The model offers a framework to dissect MBON circuits and interpret DAN activity during olfactory learning.

Muscimol-induced inactivation of the anterior cingulate cortex does not impair trace fear conditioning in the rat

Previous studies suggest that trace conditioning depends on the anterior cingulate cortex (ACC). To examine the role of ACC in trace fear conditioning further, 48 rats were surgically prepared for infusion with saline or 62.5 or 125 µg/side muscimol to inactivate ACC reversibly prior to conditioning. A noise stimulus was followed by a 1 mA footshock, with or without a 10-second trace interval between these events in a conditioned suppression procedure. The trace-conditioned groups (10 seconds) showed less test suppression than the control-conditioned groups (0 seconds). Counter to prediction, there was no effect of muscimol infusion on suppression to the noise stimulus in the 10-second trace groups.

Hippocampal and lateral entorhinal cortex physiological activity during trace conditioning under urethane anesthesia

Some forms of learning, such as some types of conditioning, can occur in anesthetized states. However, the extent to which memories can be formed in these states is still an open question. Here, we investigated the trace conditioning under urethane anesthesia and found heart rate, hippocampus, and lateral entorhinal cortex physiological changes to stimuli presentation. This new preparation may allow for exploration of memory acquisition of time-discontinuous events in the nonawake brain.