Response properties of sensory neurons are commonly described using receptive fields. This description may be formalized in a model that operates with a small set of linear filters whose outputs are nonlinearly combined to determine the instantaneous firing rate. Spike-triggered average and covariance analyses can be used to estimate the filters and nonlinear combination rule from extracellular experimental data. We describe this methodology, demonstrating it with simulated model neuron examples that emphasize practical issues that arise in experimental situations.

During realistic, continuous perception, humans automatically segment experiences into 6 discrete events. Using a novel model of neural event dynamics, we investigate how cortical structures 7 generate event representations during continuous narratives, and how these events are stored and 8 retrieved from long-term memory. Our data-driven approach enables identification of event boundaries 9 and event correspondences across datasets without human-generated stimulus annotations, and 10 reveals that different regions segment narratives at different timescales. We also provide the first direct 11 evidence that narrative event boundaries in high-order areas (overlapping the default mode network) 12 trigger encoding processes in the hippocampus, and that this encoding activity predicts pattern 13 reinstatement during recall. Finally, we demonstrate that these areas represent abstract, multimodal 14 situation models, and show anticipatory event reinstatement as subjects listen to a familiar narrative. 15Our results provide strong evidence that brain activity is naturally structured into semantically 16 meaningful events, which are stored in and retrieved from long-term memory. 17 18 Note that previous analyses of this dataset have shown that the evoked activity is similar across 129 subjects, justifying an across-subjects design . We found that essentially all 130 brain regions that responded consistently to the movie (across subjects) showed evidence for event-like 131 structure, and that the optimal number of events varied across the cortex (Fig. 2). Sensory regions like 132 visual cortex showed faster transitions between stable activity patterns, while higher-level regions like 133 the precuneus had activity patterns that often remained constant for over a minute before transitioning 134 to a new stable pattern (see Fig. 2 insets). This topography of event timescales is broadly consistent with 135 that found in previous work measuring sensitivity to temporal scrambling of a 136 movie stimulus (see Supp. Fig. 3). 137

During decision-making, neurons in multiple brain regions exhibit responses that are correlated with decisions1-6. However, it remains uncertain whether or not various forms of decision-related activity are causally related to decision-making7-9. Here we address this question by recording and reversibly inactivating the lateral intraparietal (LIP) and middle temporal (MT) areas of rhesus macaques performing a motion direction discrimination task. Neurons in area LIP exhibited firing rate patterns that directly resembled the evidence accumulation process posited to govern decision making2,10, with strong correlations between their response fluctuations and the animal's choices. Neurons in area MT, in contrast, exhibited weak correlations between their response fluctuations and animal choices, and had firing rate patterns consistent with their sensory role in motion encoding1. The behavioral impact of pharmacological inactivation of each area was inversely related to their degree of decision-related activity: while inactivation of neurons in MT profoundly impaired psychophysical performance, inactivation in LIP had no measurable impact on decision-making performance, despite having silenced the very clusters that exhibited strong decision-related activity. Although LIP inactivation did not impair psychophysical behavior, it did influence spatial selection and oculomotor metrics in a free-choice control task. The absence of an effect on perceptual decision-making was stable over trials and sessions, arguing against several forms of compensation, and was robust to changes in stimulus type and task geometry. Thus, decision-related signals in LIP do not appear to be necessary for computing perceptual decisions. Our findings highlight a dissociation between decision correlation and causation, showing that strong neuron-decision correlations may reflect secondary or epiphenomenal signals, and do not necessarily offer direct access to the neural computations underlying decisions.

The lateral intraparietal area (LIP) of macaques has been asserted to play a fundamental role in sensorimotor decision-making. Here we dissect the neural code in LIP at the level of individual trial spike trains using a statistical approach based on generalized linear models. We show that LIP responses reflect a combination of temporally-overlapping task and decision-related signals. Our model accounts for the detailed statistics of LIP spike trains, and accurately predicts spike trains from task events on single trials. Moreover, we derive an optimal decoder for heterogeneous, multiplexed LIP responses that could be implemented in biologically plausible circuits. In contrast to interpretations of LIP as providing an instantaneous code for decision variables, we show that optimal decoding requires integrating LIP spikes over two timescales. These analyses provide a detailed understanding of the neural code in LIP, and a framework for studying the coding of multiplexed signals in higher brain areas.