Short-Term Memory Trace in Rapidly Adapting Synapses of Inferior Temporal Cortex

Visual short-term memory tasks depend upon both the inferior temporal cortex (ITC) and the prefrontal cortex (PFC). Activity in some neurons persists after the first (sample) stimulus is shown. This delay-period activity has been proposed as an important mechanism for working memory. In ITC neurons, intervening (nonmatching) stimuli wipe out the delay-period activity; hence, the role of ITC in memory must depend upon a different mechanism. Here, we look for a possible mechanism by contrasting memory effects in two architectonically different parts of ITC: area TE and the perirhinal cortex. We found that a large proportion (80%) of stimulus-selective neurons in area TE of macaque ITCs exhibit a memory effect during the stimulus interval. During a sequential delayed matching-to-sample task (DMS), the noise in the neuronal response to the test image was correlated with the noise in the neuronal response to the sample image. Neurons in perirhinal cortex did not show this correlation. These results led us to hypothesize that area TE contributes to short-term memory by acting as a matched filter. When the sample image appears, each TE neuron captures a static copy of its inputs by rapidly adjusting its synaptic weights to match the strength of their individual inputs. Input signals from subsequent images are multiplied by those synaptic weights, thereby computing a measure of the correlation between the past and present inputs. The total activity in area TE is sufficient to quantify the similarity between the two images. This matched filter theory provides an explanation of what is remembered, where the trace is stored, and how comparison is done across time, all without requiring delay period activity. Simulations of a matched filter model match the experimental results, suggesting that area TE neurons store a synaptic memory trace during short-term visual memory

Short-Term Memory Through Persistent Activity: Evolution of Self-Stopping and Self-Sustaining Activity in Spiking Neural Networks

Memories in the brain are separated in two categories: short-term and long-term memories. Long-term memories remain for a lifetime, while short-term ones exist from a few milliseconds to a few minutes. Within short-term memory studies, there is debate about what neural structure could implement it. Indeed, mechanisms responsible for long-term memories appear inadequate for the task. Instead, it has been proposed that short-term memories could be sustained by the persistent activity of a group of neurons. In this work, we explore what topology could sustain short-term memories, not by designing a model from specific hypotheses, but through Darwinian evolution in order to obtain new insights into its implementation. We evolved 10 networks capable of retaining information for a fixed duration between 2 and 11s. Our main finding has been that the evolution naturally created two functional modules in the network: one which sustains the information containing primarily excitatory neurons, while the other, which is responsible for forgetting, was composed mainly of inhibitory neurons. This demonstrates how the balance between inhibition and excitation plays an important role in cognition.Comment: 28 page

Spike-Timing Theory of Working Memory

Working memory (WM) is the part of the brain's memory system that provides temporary storage and manipulation of information necessary for cognition. Although WM has limited capacity at any given time, it has vast memory content in the sense that it acts on the brain's nearly infinite repertoire of lifetime long-term memories. Using simulations, we show that large memory content and WM functionality emerge spontaneously if we take the spike-timing nature of neuronal processing into account. Here, memories are represented by extensively overlapping groups of neurons that exhibit stereotypical time-locked spatiotemporal spike-timing patterns, called polychronous patterns; and synapses forming such polychronous neuronal groups (PNGs) are subject to associative synaptic plasticity in the form of both long-term and short-term spike-timing dependent plasticity. While long-term potentiation is essential in PNG formation, we show how short-term plasticity can temporarily strengthen the synapses of selected PNGs and lead to an increase in the spontaneous reactivation rate of these PNGs. This increased reactivation rate, consistent with in vivo recordings during WM tasks, results in high interspike interval variability and irregular, yet systematically changing, elevated firing rate profiles within the neurons of the selected PNGs. Additionally, our theory explains the relationship between such slowly changing firing rates and precisely timed spikes, and it reveals a novel relationship between WM and the perception of time on the order of seconds

The Neural Mechanisms Underlying Invariant Object Search In V4 And Inferotemporal Cortex

Finding a specific visual target, such as your car keys, requires the brain to combine visual information about objects in the currently viewed scene with working memory information about your target to determine whether your target is in view. This combination of context-specific signals with visual information is thought to happen via feedback of target information from higher brain areas to the ventral visual pathway. However, exactly where and how these signals are combined remains unknown. To investigate, we recorded neural responses in V4 and inferotemporal cortex (IT) while monkeys performed an invariant object search task, where targets could appear across variation in their size, position and background context. We applied two complementary approaches to this data to investigate the neural mechanisms underlying target search. The first approach (Chapter 2) is from a computational perspective: where and how are visual and target signals combined when searching for a target? Specifically, we found that while task-relevant modulations in V4 were large, they were larger in IT, suggesting that top-down context-specific modulations are integrated into the ventral visual pathway at multiple stages. In Chapter 3, we focused on the neural responses recorded from IT from the perspective of neural coding: we sought to understand how signal and noise combine to determine task performance. We found that while signals that report the solution for object search were much smaller than signals that act as noise for the task (nuisance modulations) in IT cortex, nuisance modulations had a small effect on task performance. This counterintuitive finding was due to large trial variability constrained by short, behaviorally relevant spike counting windows. Together, this body of work provides insight into where and how the brain combines context-specific signals with visual information during invariant object search

Neural network mechanisms of working memory interference

[eng] Our ability to memorize is at the core of our cognitive abilities. How could we effectively make decisions without considering memories of previous experiences? Broadly, our memories can be divided in two categories: long-term and short-term memories. Sometimes, short-term memory is also called working memory and throughout this thesis I will use both terms interchangeably. As the names suggest, long-term memory is the memory you use when you remember concepts for a long time, such as your name or age, while short-term memory is the system you engage while choosing between different wines at the liquor store. As your attention jumps from one bottle to another, you need to hold in memory characteristics of previous ones to pick your favourite. By the time you pick your favourite bottle, you might remember the prices or grape types of the other bottles, but you are likely to forget all of those details an hour later at home, opening the wine in front of your guests. The overall goal of this thesis is to study the neural mechanisms that underlie working memory interference, as reflected in quantitative, systematic behavioral biases. Ultimately, the goal of each chapter, even when focused exclusively on behavioral experiments, is to nail down plausible neural mechanisms that can produce specific behavioral and neurophysiological findings. To this end, we use the bump-attractor model as our working hypothesis, with which we often contrast the synaptic working memory model. The work performed during this thesis is described here in 3 main chapters, encapsulation 5 broad goals: In Chapter 4.1, we aim at testing behavioral predictions of a bump-attractor (1) network when used to store multiple items. Moreover, we connected two of such networks aiming to model feature-binding through selectivity synchronization (2). In Chapter 4.2, we aim to clarify the mechanisms of working memory interference from previous memories (3), the so-called serial biases. These biases provide an excellent opportunity to contrast activity-based and activity-silent mechanisms because both mechanisms have been proposed to be the underlying cause of those biases. In Chapter 4.3, armed with the same techniques used to seek evidence for activity-silent mechanisms, we test a prediction of the bump-attractor model with short-term plasticity (4). Finally, in light of the results from aim 4 and simple computer simulations, we reinterpret previous studies claiming evidence for activity-silent mechanisms (5)

The Neural Mechanisms Underlying Visual Target Search

The task of finding specific objects and switching between targets is ubiquitous in everyday life. Searching for a particular object requires our brains to activate and maintain a representation of the target (working memory), identify each encountered object (object recognition), and determine whether the currently viewed object matches the sought target (decision making). The comparison of working memory and visual information is thought to happen via feedback of target information from higher-order brain areas to the ventral visual pathway. However, what is exactly represented by these areas and how do they implement this comparison still remains unknown. To investigate these questions, we employed a combined approach involving electrophysiology experiments and computational modeling. In particular, we recorded neural responses in inferotemporal (IT) and perirhinal (PRH) cortex as monkeys performed a visual target search task, and we adopted population-based read-outs to measure the amount and format of information contained in these neural populations. In Chapter 2 we report that the total amount of target match information was matched in IT and PRH, but this information was contained in a more explicit (i.e. linearly separable) format in PRH. These results suggest that PRH implements an untangling computation to reformat its inputs from IT. Consistent with this hypothesis, a simple linear-nonlinear model was sufficient to capture the transformation between the two areas. In Chapter 3, we report that the untangling computation in PRH takes time to evolve. While this type of dynamic reformatting is normally attributed to complex recurrent circuits, here we demonstrated that this phenomenon could be accounted by the same instantaneous linear-nonlinear model presented in Chapter 2. This counterintuitive finding was due to the existence of non-stationarities in the IT neural representation. Finally, in Chapter 4 we completely describe a novel set of methods that we developed and applied in Chapters 2 and 3 to quantify the task-specific signals contained in the heterogeneous neural responses in IT and PRH, and to relate these signals to measures of task performance. Together, this body of work revealed a previously unknown untangling computation in PRH during visual search, and demonstrated that a feed-forward linear-nonlinear model is sufficient to describe this computation

Pinging the brain to reveal hidden working memory states

Maintaining information for short periods of time in working memory, without its existence in the outer world, is crucial for everyday life, allowing us to move beyond simple, reflexive actions, and towards complex, goal-directed behaviours. It has been the consensus that the continuous activity of specific neurons are responsible to keep these information “online” until they are no longer required. However, this classic theory has been questioned more recently. Working memories that are not actively rehearsed seem to be maintained in an “activity-silent” network, eliciting no measurable neural activity, suggesting that it is the short-term changes in the neural wiring patterns that is responsible for their maintenance. These memories are thus hidden from conventional measuring techniques making it difficult to research them.This thesis proposes an approach to reveal hidden working memories that is analogues to active sonar: Hidden structures can be inferred from the echo of a “ping”. Similarly, by pushing a wave of activity through the silent neural network via external stimulation (for example a white flash), the resulting recording patterns expose the previously hidden memories held in said network. This approach is demonstrated in a series of experiments where both visual and auditory working memories are revealed. It is also used to reconstruct specific working memories with high-fidelity after different maintenance periods, showing that the maintenance of even a single piece of information is by no means perfect, as it tends to randomly and gradually transform within 1 to 2 seconds (for example purple becomes blue)

Genetic influences on emotion/cognition interactions : from synaptic regulation to individual differences in working memory for emotional faces

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