Computational Neuroscience: Finding patterns in cortical responses

Simulations predict a paradoxical effect that should be revealed by patterned stimulation of the cortex

The 8th annual computational and systems neuroscience (Cosyne) meeting

1 Department of Neurobiology, Harvard Medical School, Boston, USA -- 2 Departments of Psychology and Neurobiology, Center for Perceptual Systems, The University of Texas at Austin, Austin USAThe 8th annual Computational and Systems Neuroscience meeting (Cosyne) was held February 24-27, 2011 in Salt Lake City, Utah (abstracts are freely available online: http://www.cosyne.org/c/index.php?title=Cosyne2011_Program webcite). Cosyne brings together experimental and theoretical approaches to systems neuroscience, with the goal of understanding neurons, neural assemblies, and the perceptual, cognitive and behavioral functions they mediate. The range of questions available to systems and computational neuroscience has grown substantially in recent years, with both theoretical and experimental approaches driven by the increasing availability of data about neural circuits and systems. The Cosyne meeting has reflected this growth, nearly doubling in size since the first meeting in 2004, to a new record of nearly 600 attendees this year. It remains single-track, which allows discussions of presentations to drive scientific interaction between attendees with diverse backgrounds. Poster sessions take place each evening, which provide a forum for intense scientific conversations that frequently spill out into more informal settings late at night. The meeting is followed by two days of workshops, held at the Snowbird ski resort, which feature more specialized talks and interactive discussions on a wide collection of topics, this year ranging from consciousness and compressed sensing to dynamics, learning, and [email protected]

Microstimulation of Frontal Cortex Can Reorder a Remembered Spatial Sequence

Complex goal-directed behaviors extend over time and thus depend on the ability to serially order memories and assemble compound, temporally coordinated movements. Theories of sequential processing range from simple associative chaining to hierarchical models in which order is encoded explicitly and separately from sequence components. To examine how short-term memory and planning for sequences might be coded, we used microstimulation to perturb neural activity in the supplementary eye field (SEF) while animals held a sequence of two cued locations in memory over a short delay. We found that stimulation affected the order in which animals saccaded to the locations, but not the memory for which locations were cued. These results imply that memory for sequential order can be dissociated from that of its components. Furthermore, stimulation of the SEF appeared to bias sequence endpoints to converge toward a location in contralateral space, suggesting that this area encodes sequences in terms of their endpoints rather than their individual components

The influence of background diabetic retinopathy in the second eye on rates of progression of diabetic retinopathy between 2005 and 2010

Abstract PURPOSE: The Gloucestershire Diabetic Eye Screening Programme offers annual digital photographic screening for diabetic retinopathy to a countywide population of people with diabetes. This study was designed to investigate progression of diabetic retinopathy in this programme of the English NHS Diabetic Eye Screening Programme. METHODS: Mydriatic digital retinal photographs of people with diabetes screened on at least 2 occasions between 2005 and 2010 were graded and included in this study if the classification at first screening was no DR (R0), background DR in one (R1a) or both eyes (R1b). Times to detection of referable diabetic retinopathy (RDR) comprising maculopathy (M1), preproliferative (R2) or proliferative retinopathy (R3) were analysed using survival models. RESULTS: Data were available on 19 044 patients, 56% men, age at screening 66 (57-74) years (median, 25th, 75th centile). A total of 8.3% of those with R1a and 28.2% of those with R1b progressed to any RDR, hazard ratios 2.9 [2.5-3.3] and 11.3 [10.0-12.8]. Similarly 7.1% and 0.11% of those with R1a progressed to M1 and R3, hazard ratios 2.7 [2.3-3.2] and 1.6 [0.5-5.0], compared to 21.8% and 1.07% of those with R1b, hazard ratio 9.1 [7.8-10.4] and 15.0 [7.1-31.5]. CONCLUSIONS: The risk of progression is significantly higher for those with background DR in both eyes than those with background retinopathy in only one or in neither eye

Multiple spatial memories in the brain : decoding and modification using microstimulation

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (leaves 87-97).Sequential processing --- using multiple sensory stimuli to plan and control a set of ordered movements --- is a central aspect of human behavior. Because previous and future movements must be stored during the execution of any movement in a sequence, memory is an indispensable aspect of sequential behavior. To study how memory is used to link sensory inputs to sequential motor outputs, we have used the oculomotor system as a model. We trained monkeys to remember the location of two spatial cues over a brief delay, and then make two eye movements to the remembered locations in the order that they appeared. We explored the role of two different frontal eye movement areas, the frontal and supplementary eye fields (FEF and SEF) during this memory delay. While both the FEF and SEF have shown to be important for sequential behavior, their individual roles are unknown. Here, using physiology, we show that the FEF is important for storing the location of multiple cues and their order in memory. In the SEF, we show that memory period stimulation can affect the order of a sequence, changing the goal of the entire sequence but not the individual movement components.(cont.) Thus, both areas appear to play complementary roles in sequential planning: the FEF stores target locations, while the SEF appears to control the order of a response sequence, coding entire sequences without affecting the locations of the intermediate targets. This work bears on several outstanding questions in the field. It clarifies the individual roles of the FEF and SEF during sequencing: the FEF may serve as a buffer for multiple memories while the SEF plays a role in organizing movement sequences. It relates several prior SEF results, suggesting that a primary role of SEF may be to specify movements by their goal. Finally, we suggest that this goal-centered scheme may be a fundamental way that many different types of movements are encoded.by Hark H. Histed.Sc.D

Emergence of irregular activity in networks of strongly coupled conductance-based neurons

Cortical neurons are characterized by irregular firing and a broad distribution of rates. The balanced state model explains these observations with a cancellation of mean excitatory and inhibitory currents, which makes fluctuations drive firing. In networks of neurons with current-based synapses, the balanced state emerges dynamically if coupling is strong, i.e., if the mean number of synapses per neuron K is large and synaptic efficacy is of the order of 1/K. When synapses are conductance-based, current fluctuations are suppressed when coupling is strong, questioning the applicability of the balanced state idea to biological neural networks. We analyze networks of strongly coupled conductance-based neurons and show that asynchronous irregular activity and broad distributions of rates emerge if synaptic efficacy is of the order of 1/log(K). In such networks, unlike in the standard balanced state model, current fluctuations are small and firing is maintained by a drift-diffusion balance. This balance emerges dynamically, without fine-tuning, if inputs are smaller than a critical value, which depends on synaptic time constants and coupling strength, and is significantly more robust to connection heterogeneities than the classical balanced state model. Our analysis makes experimentally testable predictions of how the network response properties should evolve as input increases

Response nonlinearities in networks of spiking neurons.

Combining information from multiple sources is a fundamental operation performed by networks of neurons in the brain, whose general principles are still largely unknown. Experimental evidence suggests that combination of inputs in cortex relies on nonlinear summation. Such nonlinearities are thought to be fundamental to perform complex computations. However, these non-linearities are inconsistent with the balanced-state model, one of the most popular models of cortical dynamics, which predicts networks have a linear response. This linearity is obtained in the limit of very large recurrent coupling strength. We investigate the stationary response of networks of spiking neurons as a function of coupling strength. We show that, while a linear transfer function emerges at strong coupling, nonlinearities are prominent at finite coupling, both at response onset and close to saturation. We derive a general framework to classify nonlinear responses in these networks and discuss which of them can be captured by rate models. This framework could help to understand the diversity of non-linearities observed in cortical networks

Response nonlinearities in networks of spiking neurons

Combining information from multiple sources is a fundamental operation performed by networks of neurons in the brain, whose general principles are still largely unknown. Experimental evidence suggests that combination of inputs in cortex relies on nonlinear summation. Such nonlinearities are thought to be fundamental to perform complex computations. However, these non-linearities are inconsistent with the balanced-state model, one of the most popular models of cortical dynamics, which predicts networks have a linear response. This linearity is obtained in the limit of very large recurrent coupling strength. We investigate the stationary response of networks of spiking neurons as a function of coupling strength. We show that, while a linear transfer function emerges at strong coupling, nonlinearities are prominent at finite coupling, both at response onset and close to saturation. We derive a general framework to classify nonlinear responses in these networks and discuss which of them can be captured by rate models. This framework could help to understand the diversity of non-linearities observed in cortical networks