RESUMO
The notion of attractor networks is the leading hypothesis for how associative memories are stored and recalled. A defining anatomical feature of such networks is excitatory recurrent connections. These "attract" the firing pattern of the network to a stored pattern, even when the external input is incomplete (pattern completion). The CA3 region of the hippocampus has been postulated to be such an attractor network; however, the experimental evidence has been ambiguous, leading to the suggestion that CA3 is not an attractor network. In order to resolve this controversy and to better understand how CA3 functions, we simulated CA3 and its input structures. In our simulation, we could reproduce critical experimental results and establish the criteria for identifying attractor properties. Notably, under conditions in which there is continuous input, the output should be "attracted" to a stored pattern. However, contrary to previous expectations, as a pattern is gradually "morphed" from one stored pattern to another, a sharp transition between output patterns is not expected. The observed firing patterns of CA3 meet these criteria and can be quantitatively accounted for by our model. Notably, as morphing proceeds, the activity pattern in the dentate gyrus changes; in contrast, the activity pattern in the downstream CA3 network is attracted to a stored pattern and thus undergoes little change. We furthermore show that other aspects of the observed firing patterns can be explained by learning that occurs during behavioral testing. The CA3 thus displays both the learning and recall signatures of an attractor network. These observations, taken together with existing anatomical and behavioral evidence, make the strong case that CA3 constructs associative memories based on attractor dynamics.
Assuntos
Potenciais de Ação/fisiologia , Relógios Biológicos/fisiologia , Hipocampo/fisiologia , Memória/fisiologia , Modelos Neurológicos , Rede Nervosa/fisiologia , Neurônios/fisiologia , Animais , Simulação por Computador , HumanosRESUMO
The role of gamma oscillations in producing synchronized firing of groups of principal cells is well known. Here, we argue that gamma oscillations have a second function: they select which principal cells fire. This selection process occurs through the interaction of excitation with gamma frequency feedback inhibition. We sought to understand the rules that govern this process. One possibility is that a constant fraction of cells fire. Our analysis shows, however, that the fraction is not robust because it depends on the distribution of excitation to different cells. A robust description is termed E%-max: cells fire if they have suprathreshold excitation (E) within E% of the cell that has maximum excitation. The value of E%-max is approximated by the ratio of the delay of feedback inhibition to the membrane time constant. From measured values, we estimate that E%-max is 5-15%. Thus, an E%-max winner-take-all process can discriminate between groups of cells that have only small differences in excitation. To test the utility of this framework, we analyzed the role of oscillations in V1, one of the few systems in which both spiking and intracellular excitation have been directly measured. We show that an E%-max winner-take-all process provides a simple explanation for why the orientation tuning of firing is narrower than that of the excitatory input and why this difference is not affected by increasing excitation. Because gamma oscillations occur in many brain regions, the framework we have developed for understanding the second function of gamma is likely to have wide applicability.
Assuntos
Encéfalo/fisiologia , Sincronização Cortical , Modelos Neurológicos , Periodicidade , Potenciais de Ação , Algoritmos , Membrana Celular/fisiologia , Interneurônios/fisiologia , Inibição Neural , Neurônios/fisiologia , Células Piramidais/fisiologia , Fatores de TempoRESUMO
Grid cells in the rat medial entorhinal cortex fire (periodically) over the entire environment. These cells provide input to hippocampal granule cells whose output is characterized by one or more small place fields. We sought to understand how this input-output transformation occurs. Available information allows simulation of this process with no freely adjustable parameters. We first examined the spatial distribution of excitation in granule cells produced by the convergence of excitatory inputs from randomly chosen grid cells. Because the resulting summation depends on the number of inputs, it is necessary to use a realistic number (approximately 1200) and to take into consideration their 20-fold variation in strength. The resulting excitation maps have only modest peaks and valleys. To analyze how this excitation interacts with inhibition, we used an E%-max (percentage of maximal suprathreshold excitation) winner-take-all rule that describes how gamma-frequency inhibition affects firing. We found that simulated granule cells have firing maps that have one or more place fields whose size and number approximates those observed experimentally. A substantial fraction of granule cells have no place fields, as observed experimentally. Because the input firing rates and synaptic properties are known, the excitatory charge into granule cells could be calculated (2-3 pC) and was found to be only somewhat larger than required to fire granule cells (1 pC). We conclude that the input-output transformation of dentate granule does not depend strongly on synaptic modification; place field formation can be understood in terms of simple summation of randomly chosen excitatory inputs, in conjunction with a winner-take-all network mechanism.
Assuntos
Hipocampo/citologia , Hipocampo/fisiologia , Modelos Neurológicos , Neurônios/fisiologia , Transmissão Sináptica , Potenciais de Ação , Algoritmos , Animais , Simulação por Computador , Potenciais Pós-Sinápticos Excitadores , Inibição Neural , Neurônios/citologia , RatosRESUMO
The existence of recurrent synaptic connections in CA3 led to the hypothesis that CA3 is an autoassociative network similar to the Hopfield networks studied by theorists. CA3 undergoes gamma frequency periodic inhibition that prevents a persistent attractor state. This argues against the analogy to Hopfield nets, in which an attractor state can be used for working memory. However, we show that such periodic inhibition allows one cycle of recurrent excitatory activity and that this is sufficient for memory retrieval (within milliseconds). Thus, gamma oscillations are compatible with a long-term autoassociative memory function for CA3. A second goal of our work was to evaluate previous methods for estimating the memory capacity (P) of CA3. We confirm the equation, P = c/a(2), where c is the probability that any two cells are recurrently connected and a is the fraction of cells representing a memory item. In applying this to CA3, we focus on CA3a, the subregion where recurrent connections are most numerous (c = 0.2) and approximate randomness. We estimate that a memory item is represented by approximately 225 of the 70,000 neurons in CA3a (a = 0.003) and that approximately 20,000 memory items can be stored. Our general conclusion is that the physiological and anatomical findings of CA3a are consistent with an autoassociative function. The nature of the information that is associated in CA3a is discussed. We also discuss how the autoassociative properties of CA3 and the heteroassociative properties of dentate synapses (linking sequential memories) form an integrated system for the storage and recall of item sequences. The recall process generates the phase precession in dentate, CA3, and entorhinal cortex.
Assuntos
Eletroencefalografia , Hipocampo/fisiologia , Rememoração Mental/fisiologia , Modelos Neurológicos , Tempo de Reação/fisiologia , Potenciais de Ação/fisiologia , Animais , Hipocampo/citologia , Humanos , Aprendizagem/fisiologia , Inibição Neural/fisiologia , Vias Neurais/fisiologia , PeriodicidadeRESUMO
Long-term potentiation (LTP) is an activity-dependent strengthening of synapses that is thought to underlie memory storage. Ca2+/calmodulin-dependent protein kinase II (CaMKII) has been a leading candidate as a memory molecule because it is persistently activated after LTP induction and can enhance transmission. Furthermore, a mutation that blocks persistent activation blocks LTP and forms of learning. However, direct evidence for a role of the kinase in maintaining synaptic strength has been lacking. Here, we show that a newly developed noncompetitive inhibitor of CaMKII strongly reduces synaptic transmission in the CA1 region of the hippocampal slice. This occurs through both presynaptic and postsynaptic action. To study the role of CaMKII in the maintenance of LTP, inhibitor was applied after LTP induction and then removed. Inhibition occurred in both LTP and control pathways but only partially recovered. The nonrecovering component was attributable primarily to a postsynaptic change. To test whether nonrecovery was attributable to a persistent reversal of LTP, we first saturated LTP and then transiently applied inhibitor. This procedure allowed additional LTP to be induced, indicating a reversal of an LTP maintenance mechanism. This is the first procedure that can reverse LTP by chemical means and suggests that a component of synaptic memory is attributable to CaMKII. The procedure also enhanced the LTP that could be induced in the control pathway, consistent with the idea that CaMKII is involved in controlling basal synaptic strength, perhaps as a result of LTP that occurred in vivo.