Frances S. Chance

Complex cells as cortically amplified simple cells

The majority of synapses in primary visual cortex mediate excitation between nearby neurons, yet the role of local recurrent connections in visual processing remains unclear. We propose that these connections are responsible for the spatial-phase invariance of complex-cell responses. In a network model with selective cortical amplification, neurons exhibit simple-cell responses when recurrent connections are weak and complex-cell responses when they are strong, suggesting that simple and complex cells are the low- and high-gain limits of the same basic cortical circuit. Given the ubiquity of invariant responses in cognitive processing, the recurrent mechanism we propose for complex cells may be widely applicable.

Drivers and modulators from push-pull and balanced synaptic input.

In 1998, Sherman and Guillery proposed that there are two types of inputs to cortical neurons; drivers and modulators. These two forms of input are required to explain how, for example, sensory driven responses are controlled and modified by attention and other internally generated gating signals. One might imagine that driver signals are carried by fast ionotropic receptors, whereas modulators correspond to slower metabotropic receptors. Instead, we have proposed a novel mechanism by which both driver and modulator inputs could be carried by transmission through the same types of ionotropic receptors. In this scheme, the distinction between driver and modulator inputs is functional and changeable rather than anatomical and fixed. Driver inputs are carried by excitation and inhibition acting in a push-pull manner. This means that increases in excitation are accompanied by decreases in inhibition and vice versa. Modulators correspond to excitation and inhibition that covary so that they increase or decrease together. Theoretical and experimental work has shown that such an arrangement modulates the gain of a neuron, rather than driving it to respond. Constructing drivers and modulators in this manner allows individual excitatory synaptic inputs to play either role, and indeed to switch between roles, depending on how they are linked with inhibition.

Gain Modulation of Neuronal Responses by Subtractive and Divisive Mechanisms of Inhibition

Gain modulation of neuronal responses is widely observed in the cerebral cortex of both anesthetized and behaving animals. Does this multiplicative effect on neuronal tuning curves require underlying multiplicative mechanisms of integration? We compare the effects of a divisive mechanism of inhibition (noisy excitatory and inhibitory synaptic inputs) with the effects of two subtractive mechanisms (shunting conductance and hyperpolarizing current) on the tuning curves of a model cortical neuron. We find that, although the effects of subtractive inhibition can appear nonlinear, they are accompanied by a change in response threshold and are best described as a vertical shift along the response axis. Increasing noisy synaptic activity divisively scales the model responses, reproducing a response-gain control effect. When mutual inhibition between subpopulations of local neurons is included, the model exhibits a gain modulation effect that is better described as input-gain control. We apply these findings to experimental data by examining how noisy synaptic input may underlie divisive surround suppression and attention-driven gain modulation of neuronal responses in the visual system.

Divisive inhibition in recurrent networks

Models of visual cortex suggest that response selectivity can arise from recurrent networks operating at high gain. However, such networks have a number of problematic features: (i) they operate perilously close to a point of instability, (ii) small changes in synaptic strength can dramatically modify the degree of amplification, and (iii) they respond slowly to rapidly changing stimuli. Divisive inhibition, acting through interneurons that are themselves divisively inhibited, can solve these problems without degrading the selectivity of a recurrent network.

Input-specific adaptation in complex cells through synaptic depression

Abstract A number of experiments have reported that neurons in primary visual cortex can adapt in a stimulus-dependent manner. Synaptic depression is a plausible mechanism for this type of adaptation because its synapse specificity allows modification of particular inputs while allowing others to remain unaffected. Furthermore, a form of synaptic depression measured in slice experiments sets in and recovers over appropriate time scales to account for such an effect. We show that synaptic depression on feedforward, but not on recurrent, inputs can produce a fast form of spatial-phase-specific adaptation in a complex cell model.

Temporal characteristics of V1 cells arising from synaptic depression

Neurons in the primary visual cortex (VI) exhibit response characteristics qualitatively different from their LGN afferents. Some of these characteristics, such as the oriented structure of simple cell receptive fields, can be explained as arising from linear combinations of LGN receptive fields.1 However, many aspects of VI responses, especially in the temporal domain, reflect underlying nonlinear mechanisms. Here we study the idea that synaptic depression may give rise to many of these nonlinearities. Synaptic depression is observed in intracortical3–5 as well as thalamocortical6,7 synapses and has been proposed as a possible mechanism for contrast adaptation and direction selectivity.2,9 We construct a model VI simple cell and explore the effects of synaptic depression on the temporal dynamics of its responses. Our results indicate that synaptic depression may play an important role in the enhancement of responses to transient stimuli, direction selectivity, and contrast adaptation.

Testing the role of associative interference and compound cues in sequence memory

A major question facing computational models of human memory concerns the storage and retrieval of sequentially processed information. Many current models assume a chaining of associations. According to this view, each item, or memory pattern, is associated with the preceding item in the sequence. In reproducing the sequence, each recalled item serves as a retrieval cue for the next item. A serious problem facing these chaining models is their susceptibility to associative interference. This paper presents a novel experimental method designed to assess the effects of associative interference in the retrieval of ordered lists of items. Experimental findings presented here suggest that subjects use multiple prior items, as well as context, to overcome the effects of associative interference in list recall.

A recurrent network model for the phase invariance of complex cell responses

Abstract Cortical amplification is a mechanism for modifying the selectivity of neurons through recurrent interactions. Although conventionally used to enhance selectivity, cortical amplification can also broaden it, de-tuning neurons. Here we show that the spatial-phase invariance of complex cell responses in primary visual cortex can arise using recurrent amplification of feedforward input. Neurons in the model network respond like simple cells when recurrent connections are weak and complex cells when they are strong. Simple or complex cells can coexist in such a network, and they can have a range of selectivities for image characteristics such as spatial frequency.

Transient Responses to Rapid Changes in Mean and Variance in Spiking Models

The mean input and variance of the total synaptic input to a neuron can vary independently, suggesting two distinct information channels. Here we examine the impact of rapidly varying signals, delivered via these two information conduits, on the temporal dynamics of neuronal firing rate responses. We examine the responses of model neurons to step functions in either the mean or the variance of the input current. Our results show that the temporal dynamics governing response onset depends on the choice of model. Specifically, the existence of a hard threshold introduces an instantaneous component into the response onset of a leaky-integrate-and-fire model that is not present in other models studied here. Other response features, for example a decaying oscillatory approach to a new steady-state firing rate, appear to be more universal among neuronal models. The decay time constant of this approach is a power-law function of noise magnitude over a wide range of input parameters. Understanding how specific model properties underlie these response features is important for understanding how neurons will respond to rapidly varying signals, as the temporal dynamics of the response onset and response decay to new steady-state determine what range of signal frequencies a population of neurons can respond to and faithfully encode.

Corrigendum: Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition

Corrigendum: Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition