Let’s Talk About Neural Time Travel.

Unfortunately Foster has not developed a functional time machine. Instead, he is currently working on elucidating the underlying mechanisms of neural time travel in rats and its role in goal directed navigation. Neural time travel, or chronesthesia, was first proposed by Endel Tulving in the 1980s. It refers to the ability to perceive the difference between perceived, remembered, known, and imagined time. At first glance, this appears to be a thought experiment that will leave you with a headache and not much to show for it but fear not. The basic principle of neural time travel, is the ability to distinguish now from then, whether it is a time in the past or the future. To make a grievous oversimplification, neural time travel is akin to locating memories, thoughts and imaginings in temporal space.

Now, what does this have to do with hippocampal place cells which by definition are interested in physical space?

Pyramidal place cell activity in the hippocampus encodes the spatial relationships between landmarks in the environment. The cells have spatial receptive fields which fire when the animal is in a specific location in the local environment. Each environment is independently represented by place cell activity and the relationship between the spatial fields is unique to each local environment. The firing sequences of the place cells encode the navigation of the animal as it moves through individual receptive fields, in a predictable manner. In addition to place cell activity in navigating animals, these networks of cells additionally exhibit oscillatory activity, hippocampal short wave ripple (SWR)-associated place cell sequences, in sleeping and stationary animals. This feature is the focus of David Foster’s most recent work. The SWR-associated place cell events are also referred to as “replay,” during which time the relative sequence of place cell firing generated by a navigating animal is repeated in a rapid burst.

The hippocampus of rats was implanted with forty tetrodes, to allow monitoring of network activity while the rat navigated through an open field in search of a reward in either a known location or a random location. From this, it was possible to accurately determine the location of the place cell receptive fields making it possible to determine the position and trajectory of the rat from the firing sequence. Foster and colleagues identified many brief increases in population activity in stationary animals. The firing sequence (trajectory event) of many of these events was not random, but encoded a trajectory through two dimensional space that was temporally compressed. Surprisingly, these trajectory events were not simple replay of the animal’s most recent path.


The end point of these trajectory events was more likely to be the known location of a reward than anywhere else, suggesting that the events are goal directed. Foster also demonstrated that the trajectory represented in these SWR-associated events predicted the actual navigational trajectory of the rat, the prediction was improved if the end point was the location of a known reward, suggesting the events are a reflection of future behavior. By analyzing the trajectory events when the animal was forced to learn a new location for the reward, Foster discovered that initially the trajectory events emphasize novel combinations of start and end points. This is additional evidence that this is not replay of previous firing sequences.

So now you can pretend to understand why the rats are running around in such a seemingly random manner, but how is this neural time travel? The presence of predictive neural firing suggests that the rats are able to utilize episodic memories to facilitate a goal directed future action. Establishing a trajectory from a novel start to a known reward location suggests that the rat is able to extract information from multiple other pathways and string them together in order to get somewhere in the future. There are many implications for how this may allow us to study episodic memory in the future, but I’m pretty sure that the most important finding is that your rats likely know that you were late to feed them yesterday and are probably coming up with a plan to do something about it when you get in to lab tomorrow.

Alex Smirnov is a first year student in the neuroscience graduate program currently rotating with Gentry Patrick. She is a big fan of electrophysiology and unproductive thought experiments.


  • Nyberg, L., Kim, A. S. N., Habib, R., Levine, B., & Tulving, E. (2010). Consciousness of subjective time in the brain. Proceedings of the National Academy of Sciences, 107(51), 22356–22359. doi:10.1073/pnas.1016823108
  • Pfeiffer, B. E., & Foster, D. J. (2015). Autoassociative dynamics in the generation of sequences of hippocampal place cells. Science, 349(6244), 180–183. doi:10.1126/science.aaa9633·
  • Pfeiffer, B. E., & Foster, D. J. (2013). Hippocampal place-cell sequences depict future paths to remembered goals. Nature, 497(7447), 74–79. doi:10.1038/nature12112

A New Path for Alzheimer’s Disease Research

Alzheimer’s Disease (AD) is the most common type of dementia (60% – 70% of dementia cases) and causes aberrant memory, thinking and behavior that progresses over time and ultimately results in death, usually within eight years of noticeable symptom onset. Symptoms generally first appear in people in their 60’s, and in 2015, an estimated 5.3 million Americans suffer from the disease of whom 5.1 million are age 65 and older. AD is currently the sixth leading cause of death in the United States.

There are two broad classes of AD, familial AD (FAD) and sporadic AD (SAD). FAD is relatively rare, accounting for between 1% and 5% of cases, and is characterized by either autosomal dominant inheritance or inheritance of genetic mutations in one or more of three genes, those encoding for: amyloid precursor protein (APP), presenilin 1 (PS1) and presenilin 2 (PS2). Inheritance of these genes causes increases in the production of AB42, which is the primary molecule in senile plaques. The etiology of SAD is much less clear, but is believed to derive from interactions between uncertain genetic and environmental risk factors. The best known genetic risk factor for SAD is the inheritance of the epsilon4 allele of the apolipoprotein E (APOE), which increases the likelihood of developing AD by 3 times in heterozygotes and 15 times in homozygotes. Additionally, genome-wide association studies (GWAS) have found 19 additional genetic markers that appear to affect risk. There are currently no known cures for AD, and treatment options are available but largely ineffective.

The current major hypothesis in the field of AD research characterizes AD pathophysiology using the amyloid cascade model. In this framework, amyloid beta (AB) fragments are thought to oligomerize and accumulate extracellularly as plaques, accompanied by tau protein hyperphosphorylation and neurofibrillary tangles, which causes neurotoxic events that result in neurodegeneration. This process starts primarily in the medial temporal lobes and frontal cortices, and eventually spreads throughout the brain.

Given the impact AD has on the general population, a major research effort is underway to better understand the disease and discover more effective treatment options. There are currently greater than 100,000 articles in Pubmed on the etiology, mechanisms, and typology on Alzheimer’s disease. However, there remains much to be discovered, and therapies generated based on this literature have by and large proven ineffective.

How could this be?

Lawrence Goldstein et. al. proposes that our efforts have fallen short partly because research on AD thus far has relied primarily on animal models and non-neuronal human cells to investigate the molecular mechanisms associated with the disease. Animal models are problematic because not one has generated a phenotype that fully characterizes the disease in its many forms, and often requires expressing human genes in a non-human genetic context. Using non-neuronal human cells is also problematic, mainly because they lack many of the most important qualities that make neurons unique, such as: size, ability to generate action potentials, extensive interconnectivity, compartmentalization into axonal and somatodendritic regions, and several others.

Goldstein et. al. suggest that a new era in AD research is underway, and will use human induced pluripotent stem cells (hiPSC). This technique allows human neurons cultivated from people with AD to be reprogrammed to a pluripotent state, which can then be differentiated into a variety of cell types such as neurons, astrocytes, oligodendrocytes, and other brain cells. Given that these cells were derived from people with AD and therefore have higher ecological validity than animal models or non-neuronal human cells, they can be used to more efficaciously test mechanisms of the disease, identify therapeutic targets, and evaluate genetic risk factors.

The use of hiPSCs in AD research is fairly new, but the few articles published thus far are promising. Goldstein et. al. breaks down the state of AD research using hiPSCs into three broad categories: 1) FAD mutations in APP (see above); 2) FAD mutations in PS1 and PS2, and; 3) effects of AB toxicity on different types of neurons. The results of these studies are shared in the table below.

Alzheimers Disease Table

Thus, the use of hiPSCs is an exciting new avenue of research into the possible causes and treatments of AD. However, if this new line of research proves to be the linchpin for finding an effective treatment or cure, there is a broader implication for neuroscientists: how much should we be relying on animal models and non-neural human cultures? To what extent are they useful? Can we devise a sound conceptual framework for deciding between methodologies? These are tough questions that scientists will struggle to answer in the very near term. However, if one branch of science can handle rapid growth and the requirement of choosing between myriad techniques, modern neuroscience is a good bet.

About the author: Daniel Stern is the same as he was for last week’s post, just slightly more rotund post Thanksgiving stuffing.

The Elegant Discovery of a Fear Memory Engram


The purpose of this post is to make as accessible as possible the sequence of experiments that culminated in the first thorough evidence for the existence of an engram. An engram can be characterized as the rain’s physical representation of a memory in a constellation of neurons that were active while the memory was acquired. The following post is a synthesis of an already very well written review of Sheena A Josselyn’s life work on discovering engrams for fear learning in the lateral amygdalae of mice.


Specific ensembles of neurons in the lateral amygdala (LA) are necessary for specific fear memories.

  • CREB (cAMP response element-binding protein) is a cellular transcription factor tthought to be involved in the formation of long-term memories, and has has been shown to play an important role in the formation of fear memories
  • Viral vectors over-expressed CREB in a subset of LA mouse neurons and facilitated fear memory formation AND localized the memories to the neurons over-expressing CREB, the engram.
  • Selective ablation of neurons over-expressing CREB (and therefor ‘containing’ the fear memory) selectively erased that particular fear memory, did not impact other fear memories, and did not preclude future fear memory formation.


The following is an attempt to create an easily accessible synopsis of decades of work and the innumerable experiments it took to discover the existence of a fear engram.

Experiment 1 - Josselyn

Experiment 2 - Josselyn

Experiment 3 - Josselyn

Experiment 4 - Josselyn.png

About the Author: Daniel Stern is a first year graduate student in the Neuroscience PhD program at UCSD. His life can be characterized as a gradual shift from east to west coast, having recently escaped both Chicago and the prospect of becoming a lawyer in NYC. He is very happy and humbled to be doing something he loves instead.