Higher brain functions served by the lowly rodent primary visual cortex

It has been more than 50 years since the first description of ocular dominance plasticity-the profound modification of primary visual cortex (V1) following temporary monocular deprivation. This discovery immediately attracted the intense interest of neurobiologists focused on the general question of how experience and deprivation modify the brain as a potential substrate for learning and memory. The pace of discovery has quickened considerably in recent years as mice have become the preferred species to study visual cortical plasticity, and new studies have overturned the dogma that primary sensory cortex is immutable after a developmental critical period. Recent work has shown that, in addition to ocular dominance plasticity, adult visual cortex exhibits several forms of response modification previously considered the exclusive province of higher cortical areas. These "higher brain functions" include neural reports of stimulus familiarity, reward-timing prediction, and spatiotemporal sequence learning. Primary visual cortex can no longer be viewed as a simple visual feature detector with static properties determined during early development. Rodent V1 is a rich and dynamic cortical area in which functions normally associated only with "higher" brain regions can be studied at the mechanistic level.National Eye Institute (Grant RO1 EY023037)National Institute of Mental Health (U.S.) (Grant K99 MH09965

What does scalar timing tell us about neural dynamics?

The “Scalar Timing Law,” which is a temporal domain generalization of the well known Weber Law, states that the errors estimating temporal intervals scale linearly with the durations of the intervals. Linear scaling has been studied extensively in human and animal models and holds over several orders of magnitude, though to date there is no agreed upon explanation for its physiological basis. Starting from the assumption that behavioral variability stems from neural variability, this work shows how to derive firing rate functions that are consistent with scalar timing. We show that firing rate functions with a log-power form, and a set of parameters that depend on spike count statistics, can account for scalar timing. Our derivation depends on a linear approximation, but we use simulations to validate the theory and show that log-power firing rate functions result in scalar timing over a large range of times and parameters. Simulation results match the predictions of our model, though our initial formulation results in a slight bias toward overestimation that can be corrected using a simple iterative approach to learn a decision threshold.R01MH093665K99MH09965

Learning temporal representations in cortical networks through reward dependent expression of synaptic plasticity

textThe neural basis of the brain's ability to represent time, which is an essential component of cognition, is unknown. Despite extensive behavioral and electrophysiological studies, a theoretical framework capable of describing the elementary neural mechanisms used by biological neural networks to learn temporal representations does not exist. It is commonly believed that the underlying cellular mechanisms reside in high order cortical regions and there is an ongoing debate about the neural structures required for temporal processing. Recent experimental studies report sustained neural activity that can represent the timing of expected reward in low-level primary sensory cortices, suggesting that temporal representation may form locally in sensory areas of the cortex. This thesis proposes a theoretical framework that explains how temporal representations of the type seen experimentally can be encoded in local cortical networks and how specific temporal instantiations can be learned through reward modulated synaptic plasticity. The proposed framework asserts that the mechanism responsible for encoding the observed temporal intervals is long-term synaptic potentiation between neurons in a recurrent network. Analytical and numerical techniques are used to demonstrate that the model is sufficient to allow näive networks of both linear and non-linear neurons to encode and reliably represent durations specified by external cues during a training period. Analysis of a non-linear spiking neuron model is accomplished using a mean-field approach. The form of temporal learning described has specific implications that can be confirmed experimentally and these predictions are highlighted. Experimental support for a central component of the model is presented and all of the the results are discussed in relation to current experimental and computational work.Electrical and Computer Engineerin

Learned spatiotemporal sequence recognition and prediction in primary visual cortex

Learning to recognize and predict temporal sequences is fundamental to sensory perception and is impaired in several neuropsychiatric disorders, but little is known about where and how this occurs in the brain. We discovered that repeated presentations of a visual sequence over a course of days resulted in evoked response potentiation in mouse V1 that was highly specific for stimulus order and timing. Notably, after V1 was trained to recognize a sequence, cortical activity regenerated the full sequence even when individual stimulus elements were omitted. Our results advance the understanding of how the brain makes 'intelligent guesses' on the basis of limited information to form visual percepts and suggest that it is possible to study the mechanistic basis of this high-level cognitive ability by studying low-level sensory systems.Howard Hughes Medical InstitutePicower Institute for Learning and Memory (Innovation Fund)National Institute of Mental Health (U.S.) (Grant K99MH099654

Sex and estrous cycle affect experience-dependent plasticity in mouse primary visual cortex.

Sex hormones can affect cellular physiology and modulate synaptic plasticity, but it is not always clear whether or how sex-dependent differences identified in vitro express themselves as functional dimorphisms in the brain. Historically, most experimental neuroscience has been conducted using only male animals and the literature is largely mute about whether including female mice in will introduce variability due to inherent sex differences or endogenous estrous cycles. Though this is beginning to change following an NIH directive that sex should be included as a factor in vertebrate research, the lack of information raises practical issues around how to design experimental controls and apply existing knowledge to more heterogeneous populations. Various lines of research suggest that visual processing can be affected by sex and estrous cycle stage. For these reasons, we performed a series of in vivo electrophysiological experiments to characterize baseline visual function and experience-dependent plasticity in the primary visual cortex (V1) of male and female mice. We find that sex and estrous stage have no statistically significant effect on baseline acuity measurements, but that both sex and estrous stage have can modulate two mechanistically distinct forms of experience dependent cortical plasticity. We also demonstrate that resulting variability can be largely controlled with appropriate normalizations. These findings suggest that V1 plasticity can be used for mechanistic studies focusing on how sex hormones effect experience dependent plasticity in the mammalian cortex

Spontaneous and Evoked Release Are Independently Regulated at Individual Active Zones

Neurotransmitter release from synaptic vesicle fusion is the fundamental mechanism for neuronal communication at synapses. Evoked release following an action potential has been well characterized for its function in activating the postsynaptic cell, but the significance of spontaneous release is less clear. Using transgenic tools to image single synaptic vesicle fusion events at individual release sites (active zones) in Drosophila, we characterized the spatial and temporal dynamics of exocytotic events that occur spontaneously or in response to an action potential. We also analyzed the relationship between these two modes of fusion at single release sites. A majority of active zones participate in both modes of fusion, although release probability is not correlated between the two modes of release and is highly variable across the population. A subset of active zones is specifically dedicated to spontaneous release, indicating a population of postsynaptic receptors is uniquely activated by this mode of vesicle fusion. Imaging synaptic transmission at individual release sites also revealed general rules for spontaneous and evoked release, and indicate that active zones with similar release probability can cluster spatially within individual synaptic boutons. These findings suggest neuronal connections contain two information channels that can be spatially segregated and independently regulated to transmit evoked or spontaneous fusion signals.National Institutes of Health (U.S.) (Grant NS40296