Biology subjects

Mel, B. W.

Publications and source records attributed to Mel, B. W..

3 recordsLinked to original sources

bioRxiv2018

Detecting object boundaries in natural images requires ‘incitatory’ cell-cell interactions

Detecting object boundaries is crucial for recognition, but how the process unfolds in visual cortex remains unknown. To study the problem faced by a hypothetical boundary cell, and to predict how cortical circuitry could produce a boundary cell from a population of conventional "simple cells", we labeled 30,000 natural image patches and used Bayes rule to help determine how a simple cell should influence a nearby boundary cell depending on its relative offset in receptive field position and orientation. We identified three basic types of cell-cell interactions: rising and falling interactions with a range of slopes and saturation rates, as well as non-monotonic (bump-shaped) interactions with varying modes and amplitudes. Using simple models we show that a ubiquitous cortical circuit motif consisting of direct excitation and indirect inhibition - a compound effect we call "incitation" - can produce the entire spectrum of simple cell-boundary cell interactions found in our dataset. Moreover, we show that the synaptic weights that parameterize an incitation circuit can be learned by a single-layer "delta" rule. We conclude that incitatory interconnections are a generally useful computing mechanism that the cortex may exploit to help solve difficult natural classification problems. Significance statementSimple cells in primary visual cortex (V1) respond to oriented edges, and have long been supposed to detect object boundaries, yet the prevailing model of a simple cell - a divisively normalized linear filter - is a surprisingly poor natural boundary detector. To understand why, we analyzed image statistics on and off object boundaries, allowing us to characterize the neural-style computations needed to perform well at this difficult natural classification task. We show that a simple circuit motif known to exist in V1 is capable of extracting high-quality boundary probability signals from local populations of simple cells. Our findings suggest a new, more general way of conceptualizing cell-cell interconnections in the cortex.

neuroscience↗

bioRxiv2018

Classical-contextual interactions in V1 may rely on dendritic computations

A signature feature of the neocortex is the dense network of horizontal connections (HCs) through which pyramidal neurons (PNs) exchange \"contextual\" information. In primary visual cortex (V1), HCs are thought to facilitate boundary detection, a crucial operation for object recognition, but how HCs modulate PN responses to boundary cues within their classical receptive fields (CRF) remains unknown. We began by \"asking\" natural images, through a structured data collection and ground truth labeling process, what function a V1 cell should use to compute boundary probability from aligned edge cues within and outside its CRF. The \"answer\" was an asymmetric 2-D sigmoidal function, whose nonlinear form provides the first normative account for the \"multiplicative\" center-flanker interactions previously reported in V1 neurons (Kapadia et al. 1995, 2000; Polat et al. 1998). Using a detailed compartmental model, we then show that this boundary-detecting classical-contextual interaction function can be computed with near perfect accuracy by NMDAR-dependent spatial synaptic interactions within PN dendrites - the site where classical and contextual inputs first converge in the cortex. In additional simulations, we show that local interneuron circuitry activated by HCs can powerfully leverage the nonlinear spatial computing capabilities of PN dendrites, providing the cortex with a highly flexible substrate for integration of classical and contextual information.\n\nSignificance StatementIn addition to the driver inputs that establish their classical receptive fields, cortical pyramidal neurons (PN) receive a much larger number of \"contextual\" inputs from other PNs through a dense plexus of horizontal connections (HCs). However by what mechanisms, and for what behavioral purposes, HCs modulate PN responses remains unclear. We pursued these questions in the context of object boundary detection in visual cortex, by combining an analysis of natural boundary statistics with detailed modeling PNs and local circuits. We found that nonlinear synaptic interactions in PN dendrites are ideally suited to solve the boundary detection problem. We propose that PN dendrites provide the core computing substrate through which cortical neurons modulate each others responses depending on context.

neuroscience↗

bioRxiv2018

NMDA spikes mediate amplification of odor pathway information in the piriform cortex

The piriform cortex (PCx) receives direct input from the olfactory bulb (OB) and is the brains main station for odor recognition and memory. The transformation of the odor code from OB to PCx is profound: mitral and tufted cells in olfactory glomeruli respond to individual odorant molecules, whereas pyramidal neurons (PNs) in the PCx responds to multiple, apparently random combinations of activated glomeruli. How these \"discontinuous\" receptive fields are formed from OB inputs remains unknown. Counter to the prevailing view that olfactory PNs sum their inputs passively, we show for the first time that NMDA spikes within individual dendrites can both amplify OB inputs and impose combination selectivity upon them, while their ability to compartmentalize voltage signals allows different dendrites to represent different odorant combinations. Thus, the 2-layer integrative behavior of olfactory PN dendrites provides a parsimonious account for the nonlinear remapping of the odor code from bulb to cortex.

neuroscience↗