Quotes on synaptic encoding of memory

I am collecting quotes from neuroscientists which convey the field’s “consensus” model for how long-term learning and memory are encoded in the brain. I have gathered the quotes below from papers and books that I have read. If you know of a quote that you think should be included please email me at: kenneth.hayworth@gmail.com .

These quotes generally concentrate on how spiny synapses play a central role in long-term learning and memory, how LTP/LTD at spiny synapses relates to memory, and how a synapse’s functional strength is correlated with its EM-visible ultrastructural features.

Quotes from journal articles:

“Somewhere in the skull, between the locus of the fully pre-processed stimulus and before the beginning of a generation of a response, there must be stations storing, passively, many memories. Those are the things we know and remember. They are, most likely, stored in the synaptic structure of each station.” Amit 1996
“Perhaps the most striking finding in the cell biology of memory is that the consolidation and long-term storage of memory involves transcription in the nucleus and structural changes at the synapse (Bailey and Kandel 2009). These structural components of learning-related synaptic plasticity can be grouped into two general categories: (1) remodeling and enlargement of preexisting synapses, and (2) alterations in the number of synapses, including both the addition and elimination of synaptic connections.” Bailey, Kandel, and Harris 2015
“The classic view is that items are embedded in long term memory via specific synaptic modifications, and presentation of these items leads to activation of stable activity patterns in the network (‘attractors’).” Barak and Tsodyks 2014
“Enhancement of synaptic efficacy is investigated via long-term potentiation (LTP), which is widely thought to involve the same cellular mechanisms as those engaged during learning and memory.” –Bell et al. 2014
“Learning is primarily mediated by activity-dependent modifications of synaptic strength within neuronal circuits.” –Bittner et al. 2017
“Structural synaptic plasticity is thought to be an essential feature of long-term potentiation (LTP), a cellular mechanism of learning and memory.” –Bourne and Harris 2011
“Today, it is generally accepted that the neurobiological substrate of memories resides in activity driven modifications of synaptic strength and structural remodeling of neural networks activated during learning.” Bruel-Jungerman et al. 2007
“This suggests synaptic connectivity in cortex is optimized to store a large number of attractor states in a robust fashion.” – Brunel 2016
“Recent studies have provided long-sought evidence that behavioural learning involves specific synapse gain and elimination processes, which lead to memory traces that influence behaviour.” –Caroni et al. 2012
“More than 60 years ago, Hebb proposed that repeated coactivation of a group of neurons might create a memory trace through enhancement of synaptic connections. Because of technical limitations, this hypothesis has been difficult to test with single-cell resolution in awake animals. By combining novel imaging and photostimulation techniques and analytical tools, our work can be interpreted as a confirmation of the Hebbian postulate and as a demonstration that cortical microcircuits can perform pattern completion.” – Carrillo-Reid et al. 2016
“Among all the different types of synapses, glutamatergic synapses are the predominant excitatory synapse in mammalian brain. Much is known about their composition, development, maturation, physiology, elimination and dysregulation. Glutamatergic synapses are subjected to constant bidirectional changes in response to external stimuli or environmental cues. This is termed synaptic plasticity, which is considered the molecular basis underlying many brain functions, such as learning and memory.” –Chen and Geng 2017
“One remarkable feature of the brain is to encode and store new information continuously without disrupting previously acquired memories. It is believed that experience-dependent changes in synaptic strength are crucial for information storage in the brain.” – Cichon and Gan 2015
“These properties strongly suggest that the information coding in the cerebral cortical areas are established by the unsupervised learning paradigm in which the activity is determined by a relaxation dynamics and the synapses are update by a Hebbian rule.”-Doya 1999
“ ‘Synaptic consolidation’ (also cellular consolidation, local consolidation) refers to the post-encoding transformation of information into a long-term form at local synaptic and cellular nodes in the neural circuit that encodes the memory. The current central dogma of synaptic consolidation is that it involves stimulus (‘‘teacher’’)-induced activation of intracellular signaling cascades, resulting in posttranslational modifications, modulation of gene expression and synthesis of gene products that alter synaptic efficacy.” –Dudai et al. 2015
“Recent studies have demonstrated that the formation and elimination of synaptic structures happens rapidly in a subpopulation of cortical neurons during various sensorimotor learning experiences, and that stabilized synaptic structures are associated with long lasting memories for the task. Therefore, circuit plasticity, mediated by structural remodeling, provides an underlying mechanism for learning and memory.” – Fu and Zuo 2011
“The acquisition of memory basically consists in the modulation of synaptic contacts between nerve cells. …[T]he empirical evidence thus far indicates that, in humans and nonhuman primates, memory is stored in overlapping and widely distributed networks of interconnected cortical neurons. Because cortical connectivity can serve practically infinite potential associations, potential networks are practically infinite, and this fact confers uniqueness to the cognitive memory of a given individual.” –Fuster 1997
“Dendritic spines are the major loci of synaptic plasticity and are considered as possible structural correlates of memory… [O]ur results demonstrate that a newly acquired motor skill depends on the formation of a task-specific dense synaptic ensemble.” Hayashi-Takagi et al. 2015
“It is generally believed that changes in the synaptic connections between neurons play a major role in learning and memory formation.” – Hofer and Bonhoeffer 2010
“Long-term memory consolidation is thought to involve long-lasting changes in the efficacy of pre-existing synaptic connections, as well as formation of new synapses and elimination of pre-existing synapses.”–Holtmaat and Caroni 2016
“[T]here is increasing evidence that experience dependent plasticity of specific circuits in the somatosensory and visual cortex involves cell type-specific structural plasticity: some boutons and dendritic spines appear and disappear, accompanied by synapse formation and elimination, respectively” –Holtmaat and Svoboda 2009
“With the application of the latest research tools, direct evidence shows that structural plasticity of the dendritic spines is required for the recall of memory… There is a strong positive correlation among the spine head size, the postsynaptic density (PSD) area, the presynaptic active zone area, and the amplitude of AMPA receptor-mediated excitatory postsynaptic currents (EPSCs) recorded in the spine.” –Hoshiba et al. 2017
“Memories are thought to be encoded as enduring physical changes in the brain, or engrams. Most neuroscientists agree that the formation of an engram involves strengthening of synaptic connections between populations of neurons” – Josselyn, Köhler and Frankland 2015
“Spines with large heads are stable, express large numbers of AMPA-type glutamate receptors, and contribute to strong synaptic connections. By contrast, spines with small heads are motile and unstable and contribute to weak or silent synaptic connections. …Recent progress in biophysical techniques and molecular biology has provided insight into the structure–function relationships of dendritic spines in the cerebral cortex, as well as support for the century-old hypothesis that spine structure is the basis for memory in the brain.” Kasai et al. 2003
“The dorsal striatum… receives convergent excitatory afferents from cortex and thalamus and forms the origin of the direct and indirect pathways, which are distinct basal ganglia circuits involved in motor control. It is also a major site of activity-dependent synaptic plasticity. Striatal plasticity alters the transfer of information throughout basal ganglia circuits and may represent a key neural substrate for adaptive motor control and procedural memory.” – Kreitzer and Malenka 2008
“Synaptic plasticity is generally accepted as the principal implementation of information storage in neural systems.” Kukushkin and Carew 2017
“Much evidence indicates that, after learning, memories are created by alterations in glutamate-dependent excitatory synaptic transmission. These modifications are then actively stabilized, over hours or days, by structural changes at postsynaptic sites on dendritic spines.” –Lamprecht and LeDoux 2004
[The] predicate… of all modern neuroscience is that cognitively important functions can be explained as an emergent property of neurons and their network connections.” –Lisman 2015
“It is widely believed that encoding and storing memories in the brain requires changes in the number, structure, or function of synapses. … This axiomatic view that synaptic plasticity is critical for learning and memory is supported by data derived from many different memory systems, neural circuits, and molecular pathways mediating an array of different behaviors.” – Maren 2005
“Changing the strength of connections between neurons is widely assumed to be the mechanism by which memory traces are encoded and stored in the central nervous system… We conclude that a wealth of data supports the notion that synaptic plasticity is necessary for learning and memory…” Martin, Grimwood, and Morris 2000
“ With a remarkable amount of prescience, Ramon y Cajal also speculated that memories were stored as increases in the numbers of connections between neurons. This idea forms the basis of learning-related synaptic plasticity—the idea that memories are stored as changes in the number and strength of synapses between neurons—a framework that has endured as a model for understanding the biology of memory.” Martin in Poo et al. 2016
“Our data indicate that distribution of functional AMPA receptors is tightly correlated with spine geometry…” Matsuzaki et al. 2001
“Dendritic spines of pyramidal neurons in the cerebral cortex undergo activity-dependent structural remodelling that has been proposed to be a cellular basis of learning and memory… Our results thus indicate that spines individually follow Hebb’s postulate for learning. They further suggest that small spines are preferential sites for long-term potentiation induction, whereas large spines might represent physical traces of long-term memory.” Matsuzaki et al. 2004
“The central nervous system functions primarily to convert patterns of activity in sensory receptors into patterns of muscle activity that constitute appropriate behavior. At the anatomical level this requires two complementary processes: a set of genetically encoded rules for building the basic network of connections, and a mechanism for subsequently fine tuning these connections on the basis of experience. Identifying the locus and mechanism of these structural changes has long been among neurobiology’s major objectives. Evidence has accumulated implicating a particular class of contacts, excitatory synapses made onto dendritic spines, as the sites where connective plasticity occurs.” Matus 2000
“We now understand in considerable molecular detail the mechanisms underlying long-term synaptic plasticity and the importance that such plastic changes play in memory storage, across a broad range of species and forms of memory. One surprising finding is the remarkable degree of conservation of memory mechanisms in different brain regions within a species and across species widely separated by evolution.” Mayford, Siegelbaum, and Kandel 2012
“The remarkable competence of the nervous system to adapt, learn, and form memories is considered to be based on activity- dependent modifications of synaptic connections. It is by now well established that functional activity-dependent changes are paralleled by structural alterations… Subsynaptic structures such as bouton, active zone, postsynaptic density (PSD) and dendritic spine, are highly correlated in their dimensions and also correlate with synapse strength.” Meyer et al. 2014
“The connectivity patterns among neurons are a key determinant of brain computations.” – Mizusaki et al. 2016
“[A]ccumulating evidence indicates that induction of plasticity is… associated with an important structural reorganization of synaptic connectivity… This structural synaptic reorganization has strong potential implications for our understanding of brain development and learning and memory mechanisms and suggests notably that structural modifications of brain circuits could represent the substrate of acquired new skills and of long-lasting memory traces.” –Muller et al. 2014
“It has been proposed that memories are encoded by modification of synaptic strengths through cellular mechanisms such as long-term potentiation (LTP) and long-term depression (LTD)… [W]e have engineered inactivation and reactivation of a memory using LTD and LTP, supporting a causal link between these synaptic processes and memory.” Nabavi et al. 2014
“The structural plasticity of dendritic spines is considered to be essential for various forms of synaptic plasticity, learning, and memory.” Nishiyama and Yasuda 2015
“Principle 7 (Synaptic weights encode knowledge, and adapt to support learning): Synaptic inputs vary in strength as a function of sender and receiver neuron activity, and this variation in strength can encode knowledge, by shaping the pattern that each neuron detects. There is now copious empirical evidence supporting this principle and it can probably be considered uncontroversial in the neuroscience community at this point.” –O’Reilly and Hazy 2016
“There is now general consensus that persistent modification of the synaptic strength via LTP and LTD of pre-existing connections represents a primary mechanism for the formation of memory engrams… There is a clear consensus on where the memory engram is stored—specific assemblies of synapses activated or formed during memory acquisition…” – Poo et al. 2016
“Ever since their first detection by Ramon y Cajal, dendritic spines have been postulated to underlie the neuronal locus of plasticity, where short-term alterations in synaptic strength are assumed to be converted to long-lasting memories that are embedded in stable morphological changes.” Sala and Segal 2014
“[I]n the last 10 years findings from this field have provided key contributions towards establishing the idea that stable, long-lasting changes in synaptic function underlie learning and memory.” Silva 2003
“Most neuroscientists believe that memories are encoded by changing the strength of synaptic connections between neurons… The great success of deep learning systems based on units connected by modifiable synaptic weights has greatly increased the confidence that this type of computational structure is a powerful paradigm for learning.” –Sossin 2018
“Evidence derived using optical imaging, molecular-genetic and optogenetic techniques in conjunction with appropriate behavioural analyses continues to offer support for the idea that changing the strength of connections between neurons is one of the major mechanisms by which engrams are stored in the brain.” – Takeuchi, Duszkiewicz and Morris 2014
“Long-term potentiation (LTP) at glutamatergic synapses is considered to underlie learning and memory and is associated with the enlargement of dendritic spines.” Tanaka et al. 2008
“All of these experiments seem to point strongly towards the notion that spines or synapses (and not entire cells) may be the smallest unit of memory storage in the brain and it may, therefore, be most appropriate to say that the “engram” of a memory is laid down in the set of spines or synapses that are changed when specific information is stored.” Tobias Bonhoeffer in Poo et al. 2016
“A major problem in understanding memory is how it can be very long-lasting and stable from early childhood until death, despite massive interruptions in brain state as extreme as prolonged comas. Current prominent candidates for molecular substrates for long-term memory storage have focused on macromolecules such as calmodulin-dependent protein kinase II (CaMKII) coupled with the NMDA receptor (1) and protein phosphatase 2A (2), protein kinase M zeta (PKMζ) (3), and cytoplasmic polyadenylation element binding protein (CPEB) (4), all of which are inside postsynaptic spines.” Tsien 2013
“Our findings reveal that rapid, but long-lasting, synaptic reorganization is closely associated with motor learning. The data also suggest that stabilized neuronal connections are the foundation of durable motor memory.” –Xu et al. 2009
“Our data suggest that reinforcement plasticity occurs at the single spine level… in such a way that dopamine regulates the gain of NMDAR dependent Hebbian plasticity via CaMKII activity… Thus, we have clarified a molecular and cellular basis of reinforcement plasticity at the level of single dendritic spines.” – Yagishita et al. 2014
“Changes in synaptic connections are considered essential for learning and memory formation… These studies indicate that learning and daily sensory experience leave minute but permanent marks on cortical connections and suggest that lifelong memories are stored in largely stably connected synaptic networks.” –Yang et al. 2009

Book quotes:

“The bottom line of this book is ‘You are your synapses’… Given the importance of synaptic transmission in brain function, it should practically be a truism to say that the self is synaptic. What else could it be?” –Synaptic Self: How Our Brains Become Who We Are (LeDoux 2002)
“[E]verything you know is encoded in the patterns of your synaptic weights…” –Computational Cognitive Neuroscience Textbook
“One of the chief ideas we shall develop in this book is that the specificity of the synaptic connections established during development underlie perception, action, emotion, and learning.”– Principles of Neural Science Textbook
“This suggests that it might be possible to calculate the synaptic strength of a synapse from the morphology of a spine. Specifically using the correlations between spine head volume and PSD size and between spine neck length and spine potential, one should be able to calculate the amplitude of the synaptic potential, knowing the spine head volume and neck length (and ideally also the neck diameter). Thus, it could become possible to analyze the morphology of a dendritic tree and reveal its functional input map.” – Yuste 2010 Dendritic Spines (Chapter 10)
“I concur with those that hypothesize that LTP leads to an increase in spine size… Indeed, the increases in spine size during LTP nicely explain the relation between spine size and synaptic strength… I discuss the correlation between the size of a spine head and the strength of the synapse, by which larger spines appear to inject larger currents into the dendrite than smaller spines. An additional, inverse correlation appears between the length of the spine neck and synaptic strength. Both correlations, beautifully demonstrating the marriage of form and function, are so clear that spine morphology might be used to calculate synaptic strength.” – Yuste 2010 Dendritic Spines (Chapter 1)
“[N]eural networks, first proposed as purely theoretical entities in the 1940s, are distributed circuits where the computation becomes an emergent property of the connectivity matrix and the temporal dynamics it can sustain. By using spines, biological circuits could make this strategy possible… My hypothesis is that all previously discussed functions, including synaptic plasticity, are part of this larger common design plan and that spine-laden circuits are biological neural networks.” – Yuste 2010 Dendritic Spines (Chapter 1)
“In fact, I would go so far as proposing that that pyramidal cells (or spiny neurons), form the “skeleton” of the circuit, which carries out the basic computation, whereas interneurons pay a secondary, yet perhaps crucial, role in helping principal cells operate in their overall integrative regime. From this point of view, one should be able to understand the basic computational structure, or transfer function, of a circuit without discussing interneurons.” – Yuste 2010 Dendritic Spines (Chapter 10)
“[M]y proposal is that these functions are exactly the ones that make circuits with spines behave as neural networks. Spines could be viewed as the anatomical signatures of linearly integrating, distributed neural networks. Spines would endow neural circuits with the ability to perform Boolean logic, to implement associative memory, to have multistable dynamics, to become self-organized, and to become veritable learning machines.” – Yuste 2010 Dendritic Spines (Chapter 10)
“The hippocampus also expands because memories are stored by strengthening synapses, which require them to enlarge.”-Principles of Neural Design Chapter 14 (Sterling and Laughlin 2017)
“The obvious site to compactly store information is at the synapse. Storage occurs by changing its transfer “weight,” that is, its ability to excite or inhibit a postsynaptic neuron. Since the synapse is the key site for processing information, storing it there avoids additional wire for relay. Moreover, information stored directly at a synapse can be retrieved directly—also avoiding additional wire. In short, as we peruse a blueprint of brain design, we should not seek a special organ for “information storage”—it is stored, as it should be, in every circuit.” –Principles of Neural Design Chapter 14 (Sterling and Laughlin 2017)
“[I]nformation is stored and retrieved at synapses. Storage occurs by increasing synaptic weight, that is, its contribution to firing the postsynaptic neuron. Space for synapses is already constrained by competing needs for local wires and long tracts, and cortical synapses are already as small as they can be (chapter 13), so memory capacity cannot increase by shrinking them further. In fact, to increase its synaptic strength, a synapse must enlarge. The presynaptic terminal enlarges to accommodate more vesicles and more active zones for release. The postsynaptic structure, typically a dendritic spine, also enlarges to accommodate more transmitter receptors, more synaptic scaffold proteins, and more regulatory proteins.”-Principles of Neural Design Chapter 14 (Sterling and Laughlin 2017)
“Of all these adjustments, the more stable and costly are classified as ‘learning’. Neuroscience is still learning about learning; yet it is already evident that learning—stable changes in functional architecture of synapses in response to their signaling history—is ubiquitous in the brain, from early sensory stages to cortex and final motor outputs.” –Principles of Neural Design Chapter 15 (Sterling and Laughlin 2017)
“[N]eurons form spines to maximize connectivity and minimize volume… [M]emories are stored at synapses, which always fill their allotted space. Even if some fraction of synapses were held in reserve, the reserve pool belongs to the overall design. So our memory banks are effectively full, and new memories can be stored only by pruning old ones.”-Principles of Neural Design Chapter 15 (Sterling and Laughlin 2017)

Quotes from scientists in non-scientific publications:

“My belief is that we’re not going to get human-level abilities until we have systems that have the same number of parameters in them as the brain,” says Hinton. “So in the brain, you have connections between the neurons called synapses, and they can change. All your knowledge is stored in those synapses. You have about 1,000-trillion synapses—10 to the 15, it’s a very big number. So that’s quite unlike the neural networks we have right now.” –Geoffrey Hinton