Extinction of cue-evoked food seeking recruits a GABAergic interneuron ensemble in the dorsal medial prefrontal cortex of mice

Animals must quickly adapt food-seeking strategies to locate nutrient sources in dynamically changing environments. Learned associations between food and environmental cues that predict its availability promote food-seeking behaviors. However, when such cues cease to predict food availability, animals undergo 'extinction' learning, resulting in the inhibition of food-seeking responses. Repeatedly activated sets of neurons, or 'neuronal ensembles', in the dorsal medial prefrontal cortex (dmPFC) are recruited following appetitive conditioning and undergo physiological adaptations thought to encode cue-reward associations. However, little is known about how the recruitment and intrinsic excitability of such dmPFC ensembles are modulated by extinction learning. Here, we used in vivo 2-Photon imaging in male Fos-GFP mice that express green fluorescent protein (GFP) in recently behaviorally-activated neurons to determine the recruitment of activated pyramidal and GABAergic interneuron mPFC ensembles during extinction. During extinction, we revealed a persistent activation of a subset of interneurons which emerged from a wider population of interneurons activated during the initial extinction session. This activation pattern was not observed in pyramidal cells, and extinction learning did not modulate the excitability properties of activated neurons. Moreover, extinction learning reduced the likelihood of reactivation of pyramidal cells activated during the initial extinction session. Our findings illuminate novel neuronal activation patterns in the dmPFC underlying extinction of food-seeking, and in particular, highlight an important role for interneuron ensembles in this inhibitory form of learning

Neurophysiological and Morphological Plasticity in Rat Hippocampus and Medial Prefrontal Cortex Following Trace Fear Conditioning

Pavlovian fear conditioning provides a useful model system for investigating the mechanisms underlying associative learning. In recent years, there has been an increasing interest in trace fear conditioning, which requires conscious awareness of the contingency of CS and US therefore considered as a rodent model of explicit fear. Acquisition of trace fear conditioning requires an intact hippocampus and medial prefrontal cortex (mPFC), but the underlying mechanisms are still unclear. The current set of studies investigated how trace fear conditioning affects neuronal plasticity in both hippocampus and mPFC in adult rats. Trace fear conditioning significantly enhanced both intrinsic excitability and synaptic plasticity (LTP) in hippocampal CA1 neurons. Interestingly, intrinsic excitability and synaptic plasticity were significantly correlated with behavioral performance, suggesting that these changes were learning-specific. The next set of experiments investigated learning-related changes in mPFC. In order to study circuit-specific changes, only neurons that project to the basolateral nucleus of amygdala (BLA) were studied by injecting a retrograde tracer into BLA. Trace fear conditioning significantly enhanced the excitability the layer 5 (L5) projection neurons in the infralimbic (IL) subregion of mPFC whereas it decreased the excitability of L5 projection neurons in the prelimbic (PL) subregion. In both IL and PL, the conditioning effect was time-dependent because it was not observed following a retention (tested 10 days after conditioning). Furthermore, extinction reversed the conditioning effect in both IL and PL, suggesting that these changes are transient and plastic. For comparison, the effects of delay fear conditioning on mPFC neuronal excitability was also studied. These data demonstrated that in adult rats delay fear conditioning significantly enhanced the intrinsic excitability of IL but not PL neurons. However, this conditioning effect was only significant in response to stronger (e.g., larger magnitude) current injections, suggesting that this learning effect was weak. Finally, how trace fear conditioning and extinction modulate dendritic spine density of mPFC-BLA projection neurons was also studied. These data suggest that the spine density is significantly higher in L2/3 neurons than that of L5 neurons, and that extinction facilitates the elimination of spines within L2/3 neurons in both IL and PL. Together these data implicate that both neurophysiological and morphological changes within hippocampus and mPFC are critical for the acquisition and extinction of trace fear conditioning in rats

SK2 channels in cerebellar Purkinje cells contribute to excitability modulation in motor-learning-specific memory traces

Neurons store information by changing synaptic input weights. In addition, they can adjust their membrane excitability to alter spike output. Here, we demonstrate a role of such "intrinsic plasticity" in behavioral learning in a mouse model that allows us to detect specific consequences of absent excitability modulation. Mice with a Purkinje-cell-specific knockout (KO) of the calcium-activated K+ channel SK2 (L7-SK2) show intact vestibulo-ocular reflex (VOR) gain adaptation but impaired eyeblink conditioning (EBC), which relies on the ability to establish associations between stimuli, with the eyelid closure itself depending on a transient suppression of spike firing. In these mice, the intrinsic plasticity of Purkinje cells is prevented without affecting long-term depression or potentiation at their parallel fiber (PF) input. In contrast to the typical spike pattern of EBC-supporting zebrin-negative Purkinje cells, L7-SK2 neurons show reduced background spiking but enhanced excitability. Thus, SK2 plasticity and excitability modulation are essential for specific forms of motor learning

Spiking neural models & machine learning for systems neuroscience: Learning, Cognition and Behavior.

Learning, cognition and the ability to navigate, interact and manipulate the world around us by performing appropriate behavior are hallmarks of artificial as well as biological intelligence. In order to understand how intelligent behavior can emerge from computations of neural systems, this thesis suggests to consider and study learning, cognition and behavior simultaneously to obtain an integrative understanding. This involves building detailed functional computational models of nervous systems that can cope with sensory processing, learning, memory and motor control to drive appropriate behavior. The work further considers how the biological computational substrate of neurons, dendrites and action potentials can be successfully used as an alternative to current artificial systems to solve machine learning problems. It challenges the simplification of currently used rate-based artificial neurons, where computational power is sacrificed by mathematical convenience and statistical learning. To this end, the thesis explores single spiking neuron computations for cognition and machine learning problems as well as detailed functional networks thereof that can solve the biologically relevant foraging behavior in flying insects. The obtained results and insights are new and relevant for machine learning, neuroscience and computational systems neuroscience. The thesis concludes by providing an outlook how application of current machine learning methods can be used to obtain a statistical understanding of larger scale brain systems. In particular, by investigating the functional role of the cerebellar-thalamo-cortical system for motor control in primates

Practopoiesis: Or how life fosters a mind

The mind is a biological phenomenon. Thus, biological principles of organization should also be the principles underlying mental operations. Practopoiesis states that the key for achieving intelligence through adaptation is an arrangement in which mechanisms laying a lower level of organization, by their operations and interaction with the environment, enable creation of mechanisms lying at a higher level of organization. When such an organizational advance of a system occurs, it is called a traverse. A case of traverse is when plasticity mechanisms (at a lower level of organization), by their operations, create a neural network anatomy (at a higher level of organization). Another case is the actual production of behavior by that network, whereby the mechanisms of neuronal activity operate to create motor actions. Practopoietic theory explains why the adaptability of a system increases with each increase in the number of traverses. With a larger number of traverses, a system can be relatively small and yet, produce a higher degree of adaptive/intelligent behavior than a system with a lower number of traverses. The present analyses indicate that the two well-known traverses-neural plasticity and neural activity-are not sufficient to explain human mental capabilities. At least one additional traverse is needed, which is named anapoiesis for its contribution in reconstructing knowledge e.g., from long-term memory into working memory. The conclusions bear implications for brain theory, the mind-body explanatory gap, and developments of artificial intelligence technologies.Comment: Revised version in response to reviewer comment