Modulation of auditory responses by modality-specific attention in rat primary auditory cortex

How does attention modulate sensory representations? In order to probe the underlying neural mechanisms, we established a simple rodent model of modality-specific attention. Here we describe results of experiments in freely moving rats in which we have used tetrodes to record neural responses in primary auditory cortex (area A1) while subjects performed this behavior.

Subjects were first trained to perform distinct auditory and olfactory two alternative forced-choice (2AFC) tasks. Training and testing were conducted in a custom three-poke computer-controlled behavioral apparatus. Subjects initiated trials with a center-poke, which triggered presentation of a tone (either 5 or 15 Hz), an odor (either R(-)-2-Octanol or S(+)-2-Octanol), or both. Subjects responded moving to the left or right poke. Correct responses were rewarded with water. Auditory and olfactory blocks (of 50 trials each) were interleaved in a single session. In auditory blocks, pure tones were either presented with or without a null odor (caproic acid, n=2 and 3 respectively), and subjects were cued to perform the task based on auditory stimuli. In olfactory blocks, both odors and pure tones were presented simultaneously, and subjects were cued to perform the task based on olfactory stimuli.

After subjects reached consistent performance on the interleaved blocks, tetrode drives were implanted in primary auditory cortex of the left hemisphere. Single unit responses to tones were heterogeneous, and included transient, sustained, and suppressed. Among 304 responsive units recorded, 19% (58 units) showed modality-specific attentional modulation of at least one of the tone-evoked responses; in most cases, the responses to a particular auditory stimulus was enhanced in the auditory block (or, equivalently, suppressed in the olfactory block). In addition, we also observed modality-specific attentional modulation of the spontaneous activity in similar proportion of units (61 units). 

Our results suggest that shifting attention from audition to olfaction and back can modulate the activity of single neurons in primary auditory cortex

Neural Mechanisms of Selective Auditory Attention in Rats (Dissertation)

How does attention modulate sensory representations? In order to probe the underlying neural mechanisms, we established a simple rodent model of modality-specific attention. Rats were trained to perform distinct auditory two-tone discrimination and olfactory odor discrimination in a two alternative choice (2AC) paradigm. 
To determine auditory cortex’s role in this frequency discrimination task, we used GABA-A receptor agonist muscimol to transiently and reversibly inactivate auditory cortexes bilaterally in rats performing simple interleaved auditory and olfactory discrimination. With olfactory discrimination performance serving as internal control for motivation and decision making capability, we found only auditory two-tone discrimination was selectively impaired in these rats. This shows the auditory cortex is involved in this two-tone discrimination task.
To investigate the neural correlate of modality-specific attention in the auditory cortex, we trained rats to perform interleaved auditory and olfactory blocks (of 50~70 trials each) in a single session. In auditory blocks, pure tones were either presented with or without a neutral odor (caproic acid, n=2 and 3 respectively), and subjects were rewarded for discriminating auditory stimuli. In olfactory blocks, both task odors and pure tones were presented simultaneously, and subjects were rewarded for discriminating olfactory stimuli. We recorded neural responses in primary auditory cortex (area A1) in freely moving rats while subjects performed this behavior. Single unit responses to tones were heterogeneous, and included transient, sustained, and suppressed. We found 205 of 802 units recorded responsive to the stimuli we used. Of these 205 units, 18.5% showed modality-specific attentional modulation of the anticipatory activity before tone onset. In addition, we also observed in smaller proportion of units (11.2%) modality-specific attentional modulation of the tone-evoked responses; in most cases, the responses to a particular auditory stimulus was enhanced in the auditory block (or, equivalently, suppressed in the olfactory block). Attention increased choice probability of the population in the auditory block. We have also observed significant behavior choice probability in small proportions of units. 
Our results suggest that shifting attention between audition to olfaction tasks can modulate the activity of single neurons in primary auditory cortex

Correlated connectivity and the distribution of firing rates in the neocortex

Two recent experimental observations pose a challenge to many cortical models. First, the activity in the auditory cortex is sparse, and firing rates can be described by a lognormal distribution. Second, the distribution of non-zero synaptic strengths between nearby cortical neurons can also be described by a lognormal distribution. Here we use a simple model of cortical activity to reconcile these observations. The model makes the experimentally testable prediction that synaptic efficacies onto a given cortical neuron are statistically correlated, i.e. it predicts that some neurons receive many more strong connections than other neurons. We propose a simple Hebb-like learning rule which gives rise to both lognormal firing rates and synaptic efficacies. Our results represent a first step toward reconciling sparse activity and sparse connectivity in cortical networks

VC Dimension of an Integrate-and-Fire Neuron Model

We find the VC dimension of a leaky integrate-and-fire neuron model. The VC dimension quantifies the ability of a function class to partition an input pattern space, and can be considered a measure of computational capacity. In this case, the function class is the class of integrate-and-fire models generated by varying the integration time constant τ and the threshold ϴ, the input space they partition is the space of continuous-time signals, and the binary partition is specified by whether or not the model reaches threshold and spikes at some specified time. We show that the VC dimension diverges only logarithmically with the input signal bandwidth N , where the signal bandwidth is determined by the noise inherent in the process of spike generation. For reasonable estimates of the signal bandwidth, the VC dimension turns out to be quite small (¡10). We also extend this approach to ar- bitrary passive dendritic trees. The main contributions of this work are (1) it offers a novel treatment of the computational capacity of this class of dynamic system; and (2) it provides a framework for analyzing the computational capabilities of the dynamical systems defined by networks of spiking neurons

Neural Circuit Architectural Priors for Embodied Control

Artificial neural networks for motor control usually adopt generic architectures like fully connected MLPs. While general, these tabula rasa architectures rely on large amounts of experience to learn, are not easily transferable to new bodies, and have internal dynamics that are difficult to interpret. In nature, animals are born with highly structured connectivity in their nervous systems shaped by evolution; this innate circuitry acts synergistically with learning mechanisms to provide inductive biases that enable most animals to function well soon after birth and learn efficiently. Convolutional networks inspired by visual circuitry have encoded useful biases for vision. However, it is unknown the extent to which ANN architectures inspired by neural circuitry can yield useful biases for other AI domains. In this work, we ask what advantages biologically inspired ANN architecture can provide in the domain of motor control. Specifically, we translate C. elegans locomotion circuits into an ANN model controlling a simulated Swimmer agent. On a locomotion task, our architecture achieves good initial performance and asymptotic performance comparable with MLPs, while dramatically improving data efficiency and requiring orders of magnitude fewer parameters. Our architecture is interpretable and transfers to new body designs. An ablation analysis shows that constrained excitation/inhibition is crucial for learning, while weight initialization contributes to good initial performance. Our work demonstrates several advantages of biologically inspired ANN architecture and encourages future work in more complex embodied control.Comment: NeurIPS 202

Corticostriatal Plasticity Established by Initial Learning Persists after Behavioral Reversal.

The neural mechanisms that allow animals to adapt their previously learned associations in response to changes in the environment remain poorly understood. To probe the synaptic mechanisms that mediate such adaptive behavior, we trained mice on an auditory-motor reversal task, and tracked changes in the strength of corticostriatal synapses associated with the formation of learned associations. Using a ChR2-based electrophysiological assay in acute striatal slices, we measured the strength of these synapses after animals learned to pair auditory stimuli with specific actions. Here, we report that the pattern of synaptic strength initially established by learning remains unchanged even when the task contingencies are reversed. Our findings reveal that synaptic changes associated with the initial acquisition of this task are not erased or overwritten, and that behavioral reversal of learned associations may recruit a separate neural circuit. These results suggest a more complex role of the striatum in regulating flexible behaviors where activity of striatal neurons may vary given the behavioral contexts of specific stimulus-action associations

Long-term Cre-mediated retrograde tagging of neurons using a novel recombinant pseudorabies virus

Brain regions contain diverse populations of neurons that project to different long-range targets. The study of these subpopulations in circuit function and behavior requires a toolkit to characterize and manipulate their activity in vivo. We have developed a novel set of reagents based on Pseudorabies Virus (PRV) for efficient and long-term genetic tagging of neurons based on their projection targets. By deleting IE180, the master transcriptional regulator in the PRV genome, we have produced a mutant virus capable of infection and transgene expression in neurons but unable to replicate in or spread from those neurons. IE180-null mutants showed no cytotoxicity, and infected neurons exhibited normal physiological function more than 45 days after infection, indicating the utility of these engineered viruses for chronic experiments. To enable rapid and convenient construction of novel IE180-null recombinants, we engineered a bacterial artificial chromosome (BAC) shuttle-vector system for moving new constructs into the PRV IE180-null genome. Using this system we generated an IE180-null recombinant virus expressing the site-specific recombinase Cre. This Cre-expressing virus (PRV-hSyn-Cre) efficiently and robustly infects neurons in vivo and activates transgene expression from Cre-dependent vectors in local and retrograde projecting populations of neurons in the mouse. We also generated an assortment of recombinant viruses expressing fluorescent proteins (mCherry, EGFP, ECFP). These viruses exhibit long-term labeling of neurons in vitro but transient labeling in vivo. Together these novel IE180-null PRV reagents expand the toolkit for targeted gene expression in the brain, facilitating functional dissection of neuronal circuits in vivo

Rosetta Brains: A Strategy for Molecularly-Annotated Connectomics

We propose a neural connectomics strategy called Fluorescent In-Situ Sequencing of Barcoded Individual Neuronal Connections (FISSEQ-BOINC), leveraging fluorescent in situ nucleic acid sequencing in fixed tissue (FISSEQ). FISSEQ-BOINC exhibits different properties from BOINC, which relies on bulk nucleic acid sequencing. FISSEQ-BOINC could become a scalable approach for mapping whole-mammalian-brain connectomes with rich molecular annotations

BARcode DEmixing through Non-negative Spatial Regression (BarDensr).

Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many voxels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities. We apply BarDensr to simulated and real data and find that it achieves state of the art signal recovery, particularly in densely-labeled regions or data with low spatial resolution. Finally, BarDensr is fast and parallelizable. We provide open-source code as well as an implementation for the 'NeuroCAAS' cloud platform

Competing Sound Sources Reveal Spatial Effects in Cortical Processing

Neurons in the avian auditory forebrain show strong sensitivity to the spatial configuration of two competing sources, even though there is only weak spatial dependence for any single source