Volodya Yakovlev

Climbing neuronal activity as an event-based cortical representation of time.

The brain has the ability to represent the passage of time between two behaviorally relevant events. Recordings from different areas in the cortex of monkeys suggest the existence of neurons representing time by increasing (climbing) activity, which is triggered by a first event and peaks at the expected time of a second event, e.g., a visual stimulus or a reward. When the typical interval between the two events is changed, the slope of the climbing activity adapts to the new timing. We present a model in which the climbing activity results from slow firing rate adaptation in inhibitory neurons. Hebbian synaptic modifications allow for learning the new time interval by changing the degree of firing rate adaptation. This event-based representation of time is consistent with Webers law in interval timing, according to which the error in estimating a time interval is proportional to the interval length.

Inter-trial neuronal activity in inferior temporal cortex: A putative vehicle to generate long-term visual associations.

When monkeys perform a delayed match-to-sample task, some neurons in the anterior inferotemporal cortex show sustained activity following the presentation of specific visual stimuli, typically only those that are shown repeatedly. When sample stimuli are shown in a fixed temporal order, the few images that evoke delay activity in a given neuron are often neighboring stimuli in the sequence, suggesting that this delay activity may be the neural correlate of associative long-term memory. Here we report that stimulus-selective sustained activity is also evident following the presentation of the test stimulus in the same task. We use a neural network model to demonstrate that persistent stimulus-selective activity across the intertrial interval can lead to similar mnemonic representations (distributions of delay activity across the neural population) for neighboring visual stimuli. Thus, inferotemporal cortex may contain neural machinery for generating long-term stimulus–stimulus associations.

Paradigmatic working memory (attractor) cell in IT cortex

We discuss paradigmatic properties of the activity of single cells comprising an attractora developed stable delay activity distribution. To demonstrate these properties and a methodology for measuring their values, we present a detailed account of the spike activity recorded from a single cell in the inferotemporal cortex of a monkey performing a delayed match-to-sample (DMS) task of visual images. In particular, we discuss and exemplify (1) the relation between spontaneous activity and activity immediately preceding the first stimulus in each trial during a series of DMS trials, (2) the effect on the visual response (i.e., activity during stimulation) of stimulus degradation (moving in the space of IT afferents), (3) the behavior of the delay activity (i.e., activity following visual stimulation) under stimulus degradation (attractor dynamics and the basin of attraction), and (4) the propagation of information between trialsthe vehicle for the formation of (contextual) correlations by learning a fixed stimulus sequence (Miyashita, 1988). In the process of the discussion and demonstration, we expose effective tools for the identification and characterization of attractor dynamics.1 A color version of this article is found on the Web at: http://www.fiz.huji.ac.il/staff/acc/faculty/damita

Universal Memory Mechanism for Familiarity Recognition and Identification

Macaque monkeys were tested on a delayed-match-to-multiple-sample task, with either a limited set of well trained images (in randomized sequence) or with never-before-seen images. They performed much better with novel images. False positives were mostly limited to catch-trial image repetitions from the preceding trial. This result implies extremely effective one-shot learning, resembling Standings finding that people detect familiarity for 10,000 once-seen pictures (with 80% accuracy) (Standing, 1973). Familiarity memory may differ essentially from identification, which embeds and generates contextual information. When encountering another person, we can say immediately whether his or her face is familiar. However, it may be difficult for us to identify the same person. To accompany the psychophysical findings, we present a generic neural network model reproducing these behaviors, based on the same conservative Hebbian synaptic plasticity that generates delay activity identification memory. Familiarity becomes the first step toward establishing identification. Adding an inter-trial reset mechanism limits false positives for previous-trial images. The model, unlike previous proposals, relates repetition–recognition with enhanced neural activity, as recently observed experimentally in 92% of differential cells in prefrontal cortex, an area directly involved in familiarity recognition. There may be an essential functional difference between enhanced responses to novel versus to familiar images: The maximal signal from temporal cortex is for novel stimuli, facilitating additional sensory processing of newly acquired stimuli. The maximal signal for familiar stimuli arising in prefrontal cortex facilitates the formation of selective delay activity, as well as additional consolidation of the memory of the image in an upstream cortical module.

A model of expectation effects in inferior temporal cortex

Abstract Preliminary experimental data suggests that primate inferior temporal cortex implements an automatic mechanism of expectation: inter-stimulus delay activity often increases or decreases monotonically. The slope of the activity is such that the maximum/minimum is always reached at the time of the onset of the second stimulus, adapting to the length of the interval. This mechanism could play an important role for a variety of neural computations that act on a time scale of a few seconds. We developed a model that reproduces such monotonically increasing activity by making use of short-term synaptic facilitation and network effects.

It's hard to forget: resetting memory in delay-match-to-multiple-image tasks.

The Delay-Match-to-Sample (DMS) task has been used in countless studies of memory, undergoing numerous modifications, making the task more and more challenging to participants. The physiological correlate of memory is modified neural activity during the cue-to-match delay period reflecting reverberating attractor activity in multiple interconnected cells. DMS tasks may use a fixed set of well-practiced stimulus images—allowing for creation of attractors—or unlimited novel images, for which no attractor exists. Using well-learned stimuli requires that participants determine if a remembered image was seen in the same or a preceding trial, only responding to the former. Thus, trial-to-trial transitions must include a “reset” mechanism to mark old images as such. We test two groups of monkeys on a delay-match-to-multiple-images task, one with well-trained and one with novel images. Only the first developed a reset mechanism. We then switched tasks between the groups. We find that introducing fixed images initiates development of reset, and once established, switching to novel images does not disable its use. Without reset, memory decays slowly, leaving ~40% recognizable after a minute. Here, presence of reward further enhances memory of previously-seen images.

Modeling behavior in different delay match to sample tasks in one simple network

Delay match to sample (DMS) experiments provide an important link between the theory of recurrent network models and behavior and neural recordings. We define a simple recurrent network of binary neurons with stochastic neural dynamics and Hebbian synaptic learning. Most DMS experiments involve heavily learned images, and in this setting we propose a readout mechanism for match occurrence based on a smaller increment in overall network activity when the matched pattern is already in working memory, and a reset mechanism to clear memory from stimuli of previous trials using random network activity. Simulations show that this model accounts for a wide range of variations on the original DMS tasks, including ABBA tasks with distractors, and more general repetition detection tasks with both learned and novel images. The differences in network settings required for different tasks derive from easily defined changes in the levels of noise and inhibition. The same models can also explain experiments involving repetition detection with novel images, although in this case the readout mechanism for match is based on higher overall network activity. The models give rise to interesting predictions that may be tested in neural recordings.

Comparing Monkey and Human Multi-Item Memory

Are memory capabilities of humans and monkeys similar or does one species have superior abilities? In particular, does language afford better memory facilities? We compared monkey and human memory capabilities in a delay-match-to-multiple-item memory task. In each trial, a series of samples was presented and participants detected and responded to a repetition of any previous item seen in the same trial. Two difficulties are included that are not present in standard delay-match-to-sample tasks: The repetition can be for any image in the trial, not only for the first, so participants must remember all seen images. Secondly, repetition of images that appeared in a previous trial are not considered valid, and should be ignored. Thus, participants need remember all the images of the trial and if the current repeated image was seen in the current trial. In general, we used novel images that had not been seen before, introducing a few catch images from previous trials, which should be ignored. Performance Hit rate is similar for monkeys and humans, about 90% except for the longest trials. The False Positive (FP) rate is very different, however, about 80% for monkeys, 30% for humans, for images from the preceding trial. When monkeys are intensively trained with a limited set of images, they necessarily develop a reset mechanism allowing them to reject the frequent presentation of images seen in earlier trials. Interestingly, they are able to transfer this reset capability to task performance with novel (and catch) images, reducing the FP rate to below 20%. Thus, there is a surprising similarity between human and monkey performance following intensive training with a limited set of images, forcing acquisition of a reset mechanism. This similarity is found even for human performance without such prior training. We conclude that human participants have an inherent reset mechanism before visiting our laboratory. Meeting abstract presented at VSS 2015.