The goal of this dissertation is to advance our understanding of perceptual decisions. A perceptual decision is a decision that is based on sensory evidence. For example, a monkey must choose whether to eat a food item based on sensory information such as its color, texture or odor. Previous research has identified regions of the brain involved in the encoding of sensory information as well as areas involved in transforming encoded representations of stimuli into signals useful for forming decisions about those stimuli. Researchers carried out much of this work by painstakingly observing the firing of single neurons or small groups of neurons while a subject performs a task, and used this information to propose and evaluate models of the decision process. However, previous studies have also shown that sensory stimuli are encoded in a distributed fashion across populations of neurons rather than in individual or small groups of neurons. Thus it is likely that populations of neurons, rather than individual neurons, are responsible for the formation of a decision. Here I directly address the question of how decisions are formed through the collective activity of populations of cortical neurons. I used voltage-sensitive dye imaging, a technique that allowed me to simultaneously monitor millions of neurons in sensory cortex, while primates performed a simple yet challenging binary decision task. I also used psychophysical techniques and computational modeling to address fundamental questions about the nature of perceptual decisions. Here I provide new evidence that choice-related neural activity is distributed across a broad population of neurons, and that most of the decision-related neural activity occurs as early as primary sensory cortex. I propose a physiological and computational mechanism for the subject’s decision process in our task, and demonstrate that this process is likely sub-optimal due to intrinsic uncertainty about sensory stimuli. Overall, I conclude that in our task, perceptual decisions are likely to be limited primarily by the quality of evidence that resides in populations of neurons in sensory cortex, secondarily by sub-optimal decoding of these sensory signals, and to a much lesser extent by additional downstream neural variability. / text

The mammalian brain is a complex computing system that contains billions of neurons and trillions of connections. Is there a general principle that governs the processing in such large neural populations? This dissertation attempts to address this question using computational modeling and quantitative analysis of direct physiological measurements of large neural populations in the monkey primary visual cortex (V1). First, the complete spatiotemporal dynamics of V1 responses over the entire region that is activated by small stationary stimuli are characterized quantitatively. The dynamics of the responses are found to be systematic but complex. Importantly, they are inconsistent with many popular computational models of neural processing. Second, a simple population gain control (PGC) model that can account for these complex response properties is proposed for the small stationary stimuli. The PGC model is then used to predict the responses to stimuli composed of two elements and stimuli that move at a constant speed. The predictions of the model are consistent with the measured responses in V1 for both stimuli. PGC is the first model that can account for the complete spatiotemporal dynamics of V1 population responses for different types of stimuli, suggesting that gain control is a general mechanism of neural processing. / text

No / To investigate the underlying nature of the effects of transcranial magnetic stimulation (TMS) on speed perception, we applied repetitive TMS (rTMS) to human V5/MT+ following adaptation to either fast- (20 deg/s) or slow (4 deg/s)-moving grating stimuli. The adapting stimuli induced changes in the perceived speed of a standard reference stimulus moving at 10 deg/s. In the absence of rTMS, adaptation to the slower stimulus led to an increase in perceived speed of the reference, whilst adaptation to the faster stimulus produced a reduction in perceived speed. These induced changes in speed perception can be modelled by a ratio-taking operation of the outputs of two temporally tuned mechanisms that decay exponentially over time. When rTMS was applied to V5/MT+ following adaptation, the perceived speed of the reference stimulus was reduced, irrespective of whether adaptation had been to the faster- or slower-moving stimulus. The fact that rTMS after adaptation always reduces perceived speed, independent of which temporal mechanism has undergone adaptation, suggests that rTMS does not selectively facilitate activity of adapted neurons but instead leads to suppression of neural function. The results highlight the fact that potentially different effects are generated by TMS on adapted neuronal populations depending upon whether or not they are responding to visual stimuli. / BBSRC