CNS: a GPU-based framework for simulating cortically-organized networks

Computational models whose organization is inspired by the cortex are increasing in both number and popularity. Current instances of such models include convolutional networks, HMAX, Hierarchical Temporal Memory, and deep belief networks. These models present two practical challenges. First, they are computationally intensive. Second, while the operations performed by individual cells, or units, are typically simple, the code needed to keep track of network connectivity can quickly become complicated, leading to programs that are difficult to write and to modify. Massively parallel commodity computing hardware has recently become available in the form of general-purpose GPUs. This helps address the first problem but exacerbates the second. GPU programming adds an extra layer of difficulty, further discouraging exploration. To address these concerns, we have created a programming framework called CNS ('Cortical Network Simulator'). CNS models are automatically compiled and run on a GPU, typically 80-100x faster than on a single CPU, without the user having to learn any GPU programming. A novel scheme for the parametric specification of network connectivity allows the user to focus on writing just the code executed by a single cell. We hope that the ability to rapidly define and run cortically-inspired models will facilitate research in the cortical modeling community. CNS is available under the GNU General Public License

Contributions of distinct interneuron types to neocortical dynamics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, February 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references.Inhibitory interneurons are thought to play a crucial role in several features of neocortical processing, including dynamics on the timescale of milliseconds. Their anatomical and physiological characteristics are diverse, suggesting that different types regulate distinct aspects of neocortical dynamics. Interneurons expressing parvalbumin (PV) and somatostatin (SOM) form two non-overlapping populations. Here, I describe computational, correlational (neurophysiological) and causal (optogenetic) studies testing the role of PV and SOM neurons in dynamic regulation of sensory processing. First, by combining extra- and intracellular recordings with optogenetic and sensory stimulation and pharmacology, we have shown that PV cells play a key role in the generation of neocortical gamma oscillations, confirming the predictions of prior theoretical and correlative studies. Following this experimental study, we used a biophysically plausible model, simulating thousands of neurons, to explore mechanisms by which these gamma oscillations shape sensory responses, and how such transformations impact signal relay to downstream neocortical areas. We found that the local increase in spike synchrony of sensory-driven responses, which occurs without decreasing spike rate, can be explained by pre- and post-stimulus inhibition acting on pyramidal and PV cells. This transformation led to increased activity downstream, constituting an increase in gain between the two regions. This putative benefit of PV-mediated inhibition for signal transmission is only realized if the strength and timing of inhibition in the downstream area is matched to the upstream source. Second, we tested the hypothesis that SOM cells impact a distinct form of dynamics, sensory adaptation, using intracellular recordings, optogenetics and sensory stimulation. In resting neocortex, we found that SOM cell activation generated inhibition in pyramidal neurons that matched that seen in in-vitro studies. Optical SOM cell activation also transformed sensory-driven responses, decreasing evoked activity. In adapted responses, optical SOM cell inactivation relieved the impact of sustained sensory input, leading to increased membrane potential and spike rate. In contrast, SOM cell inactivation had minimal impact on sensory responses in a non-adapted neocortex, supporting the prediction that this class of interneurons is only recruited when the network is in an activated state. These findings present a previously unappreciated mechanism controlling sensory adaptation.by Ulf Knoblich.Ph.D

Categorization in IT and PFC: Model and Experiments

In a recent experiment, Freedman et al. recorded from inferotemporal (IT) and prefrontal cortices (PFC) of monkeys performing a "cat/dog" categorization task (Freedman 2001 and Freedman, Riesenhuber, Poggio, Miller 2001). In this paper we analyze the tuning properties of view-tuned units in our HMAX model of object recognition in cortex (Riesenhuber 1999) using the same paradigm and stimuli as in the experiment. We then compare the simulation results to the monkey inferotemporal neuron population data. We find that view-tuned model IT units that were trained without any explicit category information can show category-related tuning as observed in the experiment. This suggests that the tuning properties of experimental IT neurons might primarily be shaped by bottom-up stimulus-space statistics, with little influence of top-down task-specific information. The population of experimental PFC neurons, on the other hand, shows tuning properties that cannot be explained just by stimulus tuning. These analyses are compatible with a model of object recognition in cortex (Riesenhuber 2000) in which a population of shape-tuned neurons provides a general basis for neurons tuned to different recognition tasks

Neocortical Interneurons: From Diversity, Strength

Interneurons in the neocortex of the brain are small, locally projecting inhibitory GABAergic cells with a broad array of anatomical and physiological properties. The diversity of interneurons is believed to be crucial for regulating myriad operations in the neocortex. Here, we describe current theories about how interneuron diversity may support distinct neocortical processes that underlie perception

Arousal and Locomotion Make Distinct Contributions to Cortical Activity Patterns and Visual Encoding

SummarySpontaneous and sensory-evoked cortical activity is highly state-dependent, yet relatively little is known about transitions between distinct waking states. Patterns of activity in mouse V1 differ dramatically between quiescence and locomotion, but this difference could be explained by either motor feedback or a change in arousal levels. We recorded single cells and local field potentials from area V1 in mice head-fixed on a running wheel and monitored pupil diameter to assay arousal. Using naturally occurring and induced state transitions, we dissociated arousal and locomotion effects in V1. Arousal suppressed spontaneous firing and strongly altered the temporal patterning of population activity. Moreover, heightened arousal increased the signal-to-noise ratio of visual responses and reduced noise correlations. In contrast, increased firing in anticipation of and during movement was attributable to locomotion effects. Our findings suggest complementary roles of arousal and locomotion in promoting functional flexibility in cortical circuits.Video Abstrac

Targeted optogenetic stimulation and recording of neurons in vivo using cell-type-specific expression of Channelrhodopsin-2

A major long-term goal of systems neuroscience is to identify the different roles of neural subtypes in brain circuit function. The ability to causally manipulate selective cell types is critical to meeting this goal. This protocol describes techniques for optically stimulating specific populations of excitatory neurons and inhibitory interneurons in vivo in combination with electrophysiology. Cell type selectivity is obtained using Cre-dependent expression of the light-activated channel Channelrhodopsin-2. We also describe approaches for minimizing optical interference with simultaneous extracellular and intracellular recording. These optogenetic techniques provide a spatially and temporally precise means of studying neural activity in the intact brain and allow a detailed examination of the effect of evoked activity on the surrounding local neural network. Injection of viral vectors requires 30–45 min, and in vivo electrophysiology with optogenetic stimulation requires 1–4 h.National Institutes of Health (U.S.)National Science Foundation (U.S.)Simons FoundationNational Institutes of Health (U.S.). Pioneer AwardNational Eye Institue (K99 Award)Knut and Alice Wallenberg Foundation (Postdoctoral Fellowship)Brain & Behavior Research Foundation. Young Investigator AwardThomas F. Peterse

Driving fast-spiking cells induces gamma rhythm and controls sensory responses,”

Cortical gamma oscillations (20280 Hz) predict increases in focused attention, and failure in gamma regulation is a hallmark of neurological and psychiatric disease. Current theory predicts that gamma oscillations are generated by synchronous activity of fast-spiking inhibitory interneurons, with the resulting rhythmic inhibition producing neural ensemble synchrony by generating a narrow window for effective excitation. We causally tested these hypotheses in barrel cortex in vivo by targeting optogenetic manipulation selectively to fast-spiking interneurons. Here we show that light-driven activation of fast-spiking interneurons at varied frequencies (82200 Hz) selectively amplifies gamma oscillations. In contrast, pyramidal neuron activation amplifies only lower frequency oscillations, a cell-type-specific double dissociation. We found that the timing of a sensory input relative to a gamma cycle determined the amplitude and precision of evoked responses. Our data directly support the fast-spiking-gamma hypothesis and provide the first causal evidence that distinct network activity states can be induced in vivo by cell-type-specific activation. Brain states characterized by rhythmic electrophysiological activity have been studied intensively for more than 80 years Cell-type-specific expression of channelrhodopsin-2 To test directly the hypothesis that FS interneuron activity in an in vivo cortical circuit is sufficient to induce gamma oscillations, we used the light-sensitive bacteriorhodopsin Chlamydomonas reinhardtii channelrhodopsin-2 (ChR2), a cation channel activated by ,470 nm blue ligh

Stimulus Simplification and Object Representation: A Modeling Study

Tsunoda et al. [20] recently studied the nature of object representation in monkey inferotemporal cortex using a combination of optical imaging and extracellular recordings. In particular, they examined IT neuron responses to complex natural objects and "simplified" versions thereof. In that study, in 42% of the cases, optical imaging revealed a decrease in the number of activation patches in IT as stimuli were "simplified". However, in 58% of the cases, "simplification" of the stimuli actually led to the appearance of additional activation patches in IT. Based on these results, the authors propose a scheme in which an object is represented by combinations of active and inactive columns coding for individual features. We examine the patterns of activation caused by the same stimuli as used by Tsunoda et al. in our model of object recognition in cortex. We find that object-tuned units can show a pattern of appearance and disappearance of features identical to the experiment. Thus, the data of Tsunoda et al. appear to be in quantitative agreement with a simple object-based representation in which an object's identity is coded by its similarities to reference objects. Moreover, the agreement of simulations and experiment suggests that the simplification procedure used in Tsunoda et al. is not necessarily an accurate method to determine neuronal tuning