Construction of direction selectivity in V1: from simple to complex cells

Despite detailed knowledge about the anatomy and physiology of the primary visual cortex (V1), the immense number of feed-forward and recurrent connections onto a given V1 neuron make it difficult to understand how the physiological details relate to a given neuron’s functional properties. Here, we focus on a well-known functional property of many V1 complex cells: phase-invariant direction selectivity (DS). While the energy model explains its construction at the conceptual level, it remains unclear how the mathematical operations described in this model are implemented by cortical circuits. To understand how DS of complex cells is constructed in cortex, we apply a nonlinear modeling framework to extracellular data from macaque V1. We use a modification of spike-triggered covariance (STC) analysis to identify multiple biologically plausible "spatiotemporal features" that either excite or suppress a cell. We demonstrate that these features represent the true inputs to the neuron more accurately, and the resulting nonlinear model compactly describes how these inputs are combined to result in the functional properties of the cell. In a population of 59 neurons, we find that both simple and complex V1 cells are selective to combinations of excitatory and suppressive motion features. Because the strength of DS and simple/complex classification is well predicted by our models, we can use simulations with inputs matching thalamic and simple cells to assess how individual model components contribute to these measures. Our results unify experimental observations regarding the construction of DS from thalamic feed-forward inputs to V1: based on the differences between excitatory and inhibitory inputs, they suggest a connectivity diagram for simple and complex cells that sheds light on the mechanism underlying the DS of cortical cells. More generally, they illustrate how stage-wise nonlinear combination of multiple features gives rise to the processing of more abstract visual information

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Construction of direction selectivity through local energy computations in primary visual cortex.

Despite detailed knowledge about the anatomy and physiology of neurons in primary visual cortex (V1), the large numbers of inputs onto a given V1 neuron make it difficult to relate them to the neuron's functional properties. For example, models of direction selectivity (DS), such as the Energy Model, can successfully describe the computation of phase-invariant DS at a conceptual level, while leaving it unclear how such computations are implemented by cortical circuits. Here, we use statistical modeling to derive a description of DS computation for both simple and complex cells, based on physiologically plausible operations on their inputs. We present a new method that infers the selectivity of a neuron's inputs using extracellular recordings in macaque in the context of random bar stimuli and natural movies in cat. Our results suggest that DS is initially constructed in V1 simple cells through summation and thresholding of non-DS inputs with appropriate spatiotemporal relationships. However, this de novo construction of DS is rare, and a majority of DS simple cells, and all complex cells, appear to receive both excitatory and suppressive inputs that are already DS. For complex cells, these numerous DS inputs typically span a fraction of their overall receptive fields and have similar spatiotemporal tuning but different phase and spatial positions, suggesting an elaboration to the Energy Model that incorporates spatially localized computation. Furthermore, we demonstrate how these computations might be constructed from biologically realizable components, and describe a statistical model consistent with the feed-forward framework suggested by Hubel and Wiesel