Biology subjects

Howard, M. W.

Publications and source records attributed to Howard, M. W..

10 recordsLinked to original sources

bioRxiv2024

Ramping cells in rodent mPFC encode time to past and future events via real Laplace transform

In interval reproduction tasks, animals must remember the event starting the interval and anticipate the time of the planned response to terminate the interval. The interval reproduction task thus allows for studying both memory for the past and anticipation of the future. We analyzed previously published recordings from rodent mPFC (Henke et al., 2021) during an interval reproduction task and identified two cell groups by modeling their temporal receptive fields using hierarchical Bayesian models. The firing in the "past cells" group peaked at the start of the interval and relaxed exponentially back to baseline. The firing in the "future cells" group increased exponentially and peaked right before the planned action at the end of the interval. Contrary to the previous assumption that timing information in the brain has one or two time scales for a given interval, we found strong evidence for a continuous distribution of the exponential rate constants for both past and future cell populations. The real Laplace transformation of time predicts exponential firing with a continuous distribution of rate constants across the population. Therefore, the firing pattern of the past cells can be identified with the Laplace transform of time since the past event while the firing pattern of the future cells can be identified with the Laplace transform of time until the planned future event.

neuroscience↗

bioRxiv2022

The time to initiate retrieval of a memory depends on recency

Continuous recognition has long been used to study the recency effect in recognition memory. In continuous recognition, response time (RT) increases as a logarithmic function of the lag since a probe was last presented. Although this could simply be due to decaying trace strength, careful examination of response time (RT) distributions showed that the onset of RT distributions changed with the logarithm of the lag since a probe was originally presented. Each doubling of lag resulted in a shift of roughly 20 ms in the rise time of the RT distributions. To test the hypothesis that this increase was simply due to increased facility in processing the probe item, Experiment six repeated items six times. Repetition resulted in faster RTs, but did not change the effect of log lag on RT. In light of recent neurophysio-logical evidence, we consider the hypothesis that memory requires a recovery of temporal context, and the time to retrieve a prior temporal context goes up with the logarithm of the time since it was experienced.

animal behavior and cognition↗

bioRxiv2021

A compressed representation of spatial distance in the rodent hippocampus

Principal cells in the rodent hippocampus often fire in response to traversal through a specific spatial location (place cells), as well as elapsed time during an imposed temporal delay or after stimulus offset (time cells). Sequences of time cells unfold rapidly at first, with many time cells with narrow time fields. As the triggering event recedes into the past, time cells are fewer and have broader fields. This means that the representation of time in the hippocampus is compressed with greater resolution for time points near the present. Using tetrode recordings we measured individual CA1 units while rats traveled along a track that could be changed in length. Consistent with previous results, most place cells coded for distance from the starting point of the trajectory. Critically, place cells became less numerous and showed gradually widening fields with distance from the starting location. These results suggest that as the animal leaves a landmark, the hippocampal place code forms a compressed representation of distance from the starting location. The representation of time and space in the hippocampus have similar properties suggesting that they arise from similar computational mechanisms. Significance StatementThe hippocampus represents relationships between events in time and space. It has been hypothesized that temporal and spatial relationships are the result of a common computational mechanism. Previous work has shown that the representation of time in the hippocampus is compressed, with less neural resolution for more temporally remote events, consistent with the observation that temporal memory is worse for events further in the past. This paper shows an analogous result for spatial relationships. Place cells coded for distance from the start of a journey. As distance increased, place fields became broader and less numerous, showing a decrease in spatial resolution. This result suggests a unified coding scheme for the dimensions of time and space in the rodent hippocampus.

neuroscience↗

bioRxiv2019

Conjunctive representation of what and when in monkey hippocampus and lateral prefrontal cortex during an associative memory task

Adaptive memory requires the organism to form associations that bridge between events separated in time. Many studies show interactions between hippocampus (HPC) and prefrontal cortex (PFC) during formation of such associations. We analyze neural recording from monkey HPC and PFC during a memory task that requires the monkey to associate stimuli separated by about a second in time. After the first stimulus was presented, large numbers of units in both HPC and PFC fired in sequence. Many units fired only when a particular stimulus was presented at a particular time in the past. These results indicate that both HPC and PFC maintain a temporal record of events that could be used to form associations across time. This temporal record of the past is a key component of the temporal coding hypothesis, a hypothesis in psychology that memory not only encodes what happened, but when it happened.

neuroscience↗

bioRxiv2019

A temporal record of the past with a spectrum of time constants in the monkey entorhinal cortex

Episodic memory is believed to be intimately related to our experience of the passage of time. Indeed, neurons in the hippocampus and other brain regions critical to episodic memory code for the passage of time at a range of time scales. The origin of this temporal signal, however, remains unclear. Here, we examined temporal responses in the entorhinal cortex of macaque monkeys as they viewed complex images. Many neurons in the entorhinal cortex were responsive to image onset, showing large deviations from baseline firing shortly after image onset but relaxing back to baseline at different rates. This range of relaxation rates allowed for the time since image onset to be decoded on the scale of seconds. Further, these neurons carried information about image content, suggesting that neurons in the entorhinal cortex carry information not only about when an event took place but also the identity of that event. Taken together, these findings suggest that the primate entorhinal cortex uses a spectrum of time constants to construct a temporal record of the past in support of episodic memory.

neuroscience↗

bioRxiv2019

Generation of scale-invariant sequential activity in linear recurrent networks

Sequential neural activity has been observed in many parts of the brain and has been proposed as a neural mechanism for memory. The natural world expresses temporal relationships at a wide range of scales. Because we cannot know the relevant scales a priori it is desirable that memory, and thus the generated sequences, are scale-invariant. Although recurrent neural network models have been proposed as a mechanism for generating sequences, the requirements for scale-invariant sequences are not known. This paper reports the constraints that enable a linear recurrent neural network model to generate scale-invariant sequential activity. A straightforward eigendecomposition analysis results in two independent conditions that are required for scaleinvariance for connectivity matrices with real, distinct eigenvalues. First, the eigenvalues of the network must be geometrically spaced. Second, the eigenvectors must be related to one another via translation. These constraints are easily generalizable for matrices that have complex and distinct eigenvalues. Analogous albeit less compact constraints hold for matrices with degenerate eigenvalues. These constraints, along with considerations on initial conditions, provide a general recipe to build linear recurrent neural networks that support scale-invariant sequential activity.

neuroscience↗

bioRxiv2018

Predicting the future with multi-scale successor representations

The successor representation (SR) is a candidate principle for generalization in reinforcement learning, computational accounts of memory, and the structure of neural representations in the hippocampus. Given a sequence of states, the SR learns a predictive representation for every given state that encodes how often, on average, each upcoming state is expected to be visited, even if it is multiple steps ahead. A discount or scale parameter determines how many steps into the future SR's generalizations reach, enabling rapid value computation, subgoal discovery, and flexible decision-making in large trees. However, SR with a single scale could discard information for predicting both the sequential order of and the distance between states, which are common problems in navigation for animals and artificial agents. Here we propose a solution: an ensemble of SRs with multiple scales. We show that the derivative of multi-scale SR can reconstruct both the sequence of expected future states and estimate distance to goal. This derivative can be computed linearly: we show that a multi-scale SR ensemble is the Laplace transform of future states, and the inverse of this Laplace transform is a biologically plausible linear estimation of the derivative. Multi-scale SR and its derivative could lead to a common principle for how the medial temporal lobe supports both map-based and vector-based navigation.

neuroscience↗

bioRxiv2017

Scanning along a compressed timeline of the future

Several authors have suggested a deep symmetry between the psychological processes that underlie our ability to remember the past and make predictions about the future. The judgment of recency (JOR) task measures temporal order judgments for the past by presenting pairs of probe stimuli; participants choose the probe that was presented more recently. We performed a short-term relative JOR task and introduced a novel judgment of imminence (JOI) task to study temporal order judgments for the future. In the JOR task, participants were presented with a sequence of stimuli and asked to choose which of two probe stimuli was presented closer to the present. In the JOI task, participants were trained on a probabilistic sequence. After training, the sequence was interrupted with probe stimuli. Participants were asked to choose which of two probe stimuli was expected to be presented closer to the present. Replicating prior work on JOR, we found that RT results supported a backward self-terminating search model operating on a temporally-organized representation of the past. We also showed that RT distributions are consistent with this model and that the temporally-organized representation is compressed. Critically, results for the JOI task probing expectations of the future were mirror-symmetric to results from memory, suggesting a forward self-terminating search model operating on a temporally-organized representation of the future.

animal behavior and cognition↗

bioRxiv2017

Recency Order Judgments In Short Term Memory: Replication And Extension Of Hacker (1980)

The classic finding from short-term relative JOR tasks is that correct response time (RT) depends on the lag to the more recent item but not to the less recent item (Hacker, 1980). For decades, researchers have argued that this finding is consistent with a self-terminating backward scanning model (Muter, 1979; Hacker, 1980; Hockley, 1984; McElree & Dosher, 1993). This finding has taken on new importance in light of recent proposal that many forms of memory depend on a compressed representation of the past (Howard, Shankar, Aue, & Criss, 2015). This paper replicates and extends the results of the classic papers. A Bayesian t-test showed substantial evidence for the null effect of lag to the less recent item on correct RT. In addition, this paper reports that correct RT is a sub-linear function of lag to the more recent probe and replicates the classic finding that error RT depends on lag to the less recent probe. These findings place new constraints on models of short-term memory scanning.

animal behavior and cognition↗

bioRxiv2017

Compressed timeline of recent experience in monkey lPFC

Cognitive theories suggest that working memory maintains not only the identity of recently-presented stimuli but also a sense of the elapsed time since the stimuli presentation. Previous studies of the neural underpinnings of working memory have focused on sustained firing, which can account for maintenance of the stimulus identity, but not for representation of the elapsed time. We analyzed single-unit recordings from the macaque lateral prefrontal cortex (lPFC) during performance of a delayed-match-to-category task. The sample stimulus triggered a consistent sequence of neurons, each neuron in the sequence firing during a circumscribed period of time. The sequences initiated by different sample stimuli were distinct but overlapping, with the degree of overlap reflecting the visual similarity of the stimuli that caused the sequences. These sequences of neurons encoded both stimulus identity and the elapsed time. The temporal code became less precise as the sample stimulus receded into the past. These findings suggest that working memory is maintained as a compressed timeline, consistent with longstanding cognitive theories of human memory.

neuroscience↗